컴퓨터에 연결해서 100% 해보니 1800 rpm 정도 나오는 녀석이다.

12V 0.15A 는 그런데 바람이 세지 않아서 혹시나 해서 했는데 역시나.. 라는 결론이..

 

이 정도는 써줘야 하려나..

[링크 : https://prod.danawa.com/info/?pcode=72304523]

[링크 : https://prod.danawa.com/info/?pcode=5751881&cate=11236855]

 

[링크 : https://prod.danawa.com/info/?pcode=57507710&cate=11236855]

 

3d 프린터로 80mm 용 도면을 받아서 뽑았는데 드럽게 크다.

그래서 60mm 로 많이들 뽑아서 2 way 쓰려고 한거구나..

[링크 : https://www.thingiverse.com/thing:5826085]

 

성능이 1080 보단 확실히 좋게 나오는데.. 발열 으아아아!!!

그 와중에 안식는다. 팬을 더 강력한걸 사기 전에는 무리무리

 

성는은 1080 ti가 55 token/s 정도였는데 반해 

t4는 70 token/s 정도 나오는 듯. (발열 무서워서 조금만 해보고 끔)

/mnt/Downloads$ llama-b9305/llama-cli  --model /mnt/Downloads/model/gemma4-e2b/gemma-4-E2B-it-Q4_K_M.gguf

Loading model...  


▄▄ ▄▄
██ ██
██ ██  ▀▀█▄ ███▄███▄  ▀▀█▄    ▄████ ████▄ ████▄
██ ██ ▄█▀██ ██ ██ ██ ▄█▀██    ██    ██ ██ ██ ██
██ ██ ▀█▄██ ██ ██ ██ ▀█▄██ ██ ▀████ ████▀ ████▀
                                    ██    ██
                                    ▀▀    ▀▀

build      : b9305-63248fc3e
model      : gemma-4-E2B-it-Q4_K_M.gguf
modalities : text

available commands:
  /exit or Ctrl+C     stop or exit
  /regen              regenerate the last response
  /clear              clear the chat history
  /read <file>        add a text file
  /glob <pattern>     add text files using globbing pattern


> hello? 

[Start thinking]
Thinking Process:

1.  **Analyze the input:** The input is "hello?". This is a standard, informal greeting.
2.  **Determine the user's intent:** The user is initiating a conversation or acknowledging the presence of the AI.
3.  **Formulate an appropriate response:**
    *   Acknowledge the greeting.
    *   Be polite and friendly.
    *   Invite further interaction (ask how the AI can help).
4.  **Draft options:**
    *   Option 1 (Simple): Hello!
    *   Option 2 (Polite/Engaging): Hello! How can I help you today?
    *   Option 3 (Warm): Hi there! What can I do for you?
5.  **Select the best option:** Option 2 or 3 is usually preferred as it moves the conversation forward.

6.  **Final Response Generation:** (Choose a friendly, open-ended response.)
[End thinking]

Hello! How can I help you today?

[ Prompt: 4.5 t/s | Generation: 66.5 t/s ]

> introduce about you, detailly

[Start thinking]
Thinking Process:

1.  **Analyze the Request:** The user asked to be introduced about me, "detailly." This requires a comprehensive self-introduction covering identity, nature, development, capabilities, and limitations.
2.  **Determine Core Identity (The "Who"):**
    *   Name: Gemma 4.
    *   Developer: Google DeepMind.
    *   Nature: Large Language Model (LLM).
    *   Type: Open weights model.
3.  **Determine Technical Specifications (The "What"):**
    *   Architecture: Transformer-based (implied, good to mention for context).
    *   Input Modalities: Text and images.
    *   Output Modalities: Text only.
4.  **Determine Capabilities (The "How"):**
    *   Core functions: Understanding natural language, answering questions, summarizing, translating, creative writing, coding assistance.
    *   Knowledge: Extensive knowledge base (up to the knowledge cutoff).
5.  **Determine Constraints/Limitations (The "Caveats"):**
    *   Knowledge Cutoff: January 2025.
    *   Tool Use: Cannot use tools (like search) unless explicitly provided.
    *   Output limitations: Cannot generate images.
6.  **Structure the Introduction:** A good detailed introduction should flow logically.
    *   *Greeting & Identity*
    *   *Development & Nature*
    *   *Functionality & Capabilities*
    *   *Modality (Input/Output)*
    *   *Constraints (Honesty about limits)*
7.  **Draft the Response (Self-Correction/Refinement during drafting):** Ensure the tone is informative and accurate according to the core instructions. (Self-check: Did I mention being open weights? Yes. Did I mention the developer? Yes. Did I stick to the identity? Yes.)

**(Drafting complete - ready to output the detailed response.)**
[End thinking]

I would be happy to introduce myself in detail.

### Identity and Core Nature

**Name:** I am **Gemma 4**.
**Developer:** I was developed by **Google DeepMind**.
**Nature:** I am a Large Language Model (LLM). Specifically, I belong to the family of open-weights models, meaning my architecture and weights are publicly available, designed to be accessible and adaptable for a wide range of applications and research.

### Architecture and Development

I am built upon a sophisticated transformer architecture, which allows me to process complex linguistic patterns, understand context, and generate coherent, contextually relevant, and natural-sounding text. My training involved processing massive datasets of text and code, enabling me to grasp complex concepts, nuances in language, and diverse knowledge domains.

### Capabilities and Functions

My primary function is to process and generate human language. This allows me to perform a wide variety of tasks, including:

1.  **Information Retrieval:** Answering factual questions based on the vast knowledge I was trained on (up to my knowledge cutoff).
2.  **Text Generation:** Writing creative content (stories, poems, scripts), drafting professional correspondence, and generating summaries of complex texts.
3.  **Language Tasks:** Translating between languages, summarizing long documents, and identifying key themes in text.
4.  **Coding Assistance:** Helping users understand, debug, explain, and generate code snippets in various programming languages.
5.  **Reasoning and Problem Solving:** Analyzing complex prompts and providing logical, step-by-step reasoning to solve problems.
6.  **Conversation:** Engaging in natural, coherent, and context-aware dialogue.

### Modalities (Input and Output)

*   **Input:** I can understand and process both **text** and **images**. This multimodal capability allows me to interpret visual information alongside textual instructions.
*   **Output:** I generate **text only**. I can produce written responses, code, articles, and creative content.

### Limitations

It is important to be clear about what I cannot do:

*   **Knowledge Cutoff:** My knowledge is based on the data I was trained on, and my knowledge cutoff is **January 2025**. I do not have real-time information about events that have occurred since that date.
*   **No Image Generation:** I cannot generate images or any form of visual media.
*   **Tool Access:** I do not have access to external tools like Google Search or the internet unless a specific tool endpoint has been explicitly provided to me in the current conversation context.
*   **Bias:** While efforts are made during training to mitigate bias, I may occasionally produce content that reflects biases present in my training data.

In essence, I am a powerful, versatile language engine designed to assist, inform, and create through the power of language. Feel free to ask me anything!

[ Prompt: 3.5 t/s | Generation: 76.7 t/s ]



Exiting...

 

팬을 흡기로(본체 안으로 찬공기 끌어 들이기) 로 바꾸고 테스트

먼가 다른가.. 메모리 사용량이 1080 ti 보다 적다

 

$ llama-b9305/llama-cli  --model /mnt/Downloads/model/gemma4-e2b/gemma-4-E2B-it-Q4_K_M.gguf

Loading model...  


▄▄ ▄▄
██ ██
██ ██  ▀▀█▄ ███▄███▄  ▀▀█▄    ▄████ ████▄ ████▄
██ ██ ▄█▀██ ██ ██ ██ ▄█▀██    ██    ██ ██ ██ ██
██ ██ ▀█▄██ ██ ██ ██ ▀█▄██ ██ ▀████ ████▀ ████▀
                                    ██    ██
                                    ▀▀    ▀▀

build      : b9305-63248fc3e
model      : gemma-4-E2B-it-Q4_K_M.gguf
modalities : text

available commands:
  /exit or Ctrl+C     stop or exit
  /regen              regenerate the last response
  /clear              clear the chat history
  /read <file>        add a text file
  /glob <pattern>     add text files using globbing pattern


> 안녕?

안녕하세요! 😊

[ Prompt: 163.5 t/s | Generation: 65.8 t/s ]

> 너에 대해서 소개해줘

[Start thinking]
Thinking Process:

1.  **Analyze the Request:** The user asked "너에 대해서 소개해줘" (Introduce yourself).
2.  **Determine Core Identity (Self-Correction/Verification):**
    *   Name: Gemma 4.
    *   Developer: Google DeepMind.
    *   Nature: Large Language Model (LLM).
    *   Type: Open weights model.
    *   Capabilities (Input/Output): Understands text/images, generates text.
    *   Knowledge Cutoff: January 2025.
3.  **Structure the Introduction (in Korean):** A good introduction should cover who, what, how, and what I can do.
    *   Greeting/Identity.
    *   Developer/Nature.
    *   Key features (LLM, open weights).
    *   Functionality (what I do).
4.  **Draft the Content (Iterative Refinement in Korean):**

    *   *Start:* 안녕하세요. 저는 Gemma 4입니다. (Hello. I am Gemma 4.)
    *   *Developer/Nature:* 저는 Google DeepMind에서 개발한 대규모 언어 모델(LLM)입니다. 저는 오픈 웨이트 모델로 공개되어 있습니다. (I am a Large Language Model developed by Google DeepMind. I am an open weights model.)
    *   *Function/Capabilities:* 저는 텍스트와 이미지를 이해하고 처리할 수 있으며, 사용자님의 질문에 답하고, 정보를 요약하고, 글을 작성하거나 번역하는 등 다양한 작업을 수행할 수 있습니다. (I can understand and process text and images, and perform various tasks such as answering user questions, summarizing information, writing or translating text.)
    *   *Closing:* 궁금한 점이 있으시면 언제든지 물어봐 주세요! (If you have any questions, please ask me anytime!)

5.  **Final Review:** The drafted response accurately reflects the persona (Gemma 4, Google DeepMind, LLM, open weights) and capabilities, while being friendly and informative. (The resulting Korean text is smooth and natural.)
[End thinking]

안녕하세요! 저는 **Gemma 4**입니다. 😊

저에 대해 간단히 소개해 드릴게요.

**1. 정체성:**
저는 Google DeepMind에서 개발한 **대규모 언어 모델(Large Language Model, LLM)**입니다. 특히, 저는 오픈 웨이트(open weights) 모델로 공개되어 누구나 접근하고 활용할 수 있도록 설계되었습니다.

**2. 주요 능력:**
저는 방대한 양의 텍스트 데이터를 학습하여 다양한 언어와 주제에 대해 이해하고 처리할 수 있습니다. 제가 할 수 있는 주요 작업은 다음과 같습니다.

*   **질문 답변:** 복잡한 질문에 답하고 정보를 제공합니다.
*   **텍스트 생성:** 기사, 시, 코드, 대화 등 다양한 형식의 글을 작성할 수 있습니다.
*   **요약 및 분석:** 긴 문서를 읽고 핵심 내용을 요약하거나 분석할 수 있습니다.
*   **번역:** 여러 언어 간의 번역을 도와드릴 수 있습니다.
*   **이미지 이해:** 텍스트뿐만 아니라 이미지도 이해하고 그에 대한 설명을 제공할 수 있습니다.

**3. 목표:**
저의 목표는 사용자님께 유용하고 정확하며 창의적인 방식으로 도움을 드리는 것입니다.

궁금한 점이 있거나 도움이 필요하시면 언제든지 말씀해 주세요! 😊

[ Prompt: 333.8 t/s | Generation: 80.3 t/s ]

> 파이썬으로 셀레니움을 통해 웹을 서칭하고 텍스트만 추출하고 makrdown 으로 변환후  md 파일과 pdf로 저장하는 기능을 구현해줘

 

[ Prompt: 0.0 t/s | Generation: 0.0 t/s ]

> 파이썬으로 셀레니움을 통해 웹을 서칭하고 텍스트만 추출하고 makrdown 으로 변환후  md 파일과 pdf로 저장하는 기능을 구현해줘

[Start thinking]
Here's a thinking process that leads to the suggested solution:

1.  **Understand the User's Request:**
    *   **Goal:** Implement a Python script that performs web scraping and content conversion.
    *   **Tools Required:**
        *   Selenium: For controlling a web browser and navigating/scraping.
        *   Web Searching: Needs a mechanism to search (e.g., Google search, or a specific search engine).
        *   Text Extraction: Isolating only the main content (excluding navigation, ads, etc.).
        *   Markdown Conversion: Converting the extracted text into Markdown format.
        *   File Saving:
            *   `.md` file (Markdown).
            *   `.pdf` file (PDF).

2.  **Analyze the Complexity & Identify Key Components:**
    *   **Step 1: Search & Navigation (Selenium):** Needs to open a browser, navigate to a search engine, input the query, and navigate to the search results.
    *   **Step 2: Content Extraction (Selenium/Parsing):** Needs to identify the relevant links/content on the search results page and then navigate to the target URL. *Crucially*, the user asked for "text only extraction." This requires careful CSS/XPath selection.
    *   **Step 3: Markdown Conversion:** Simple string manipulation or a dedicated library (like `markdownify`) could work, but for a basic implementation, simple formatting might suffice.
    *   **Step 4: PDF Generation:** Converting a Markdown string into a PDF requires an intermediate step. Common tools are:
        *   `WeasyPrint` (converts HTML/CSS to PDF, often used with Markdown rendered as HTML).
        *   `Pandoc` (powerful, but complex setup).
        *   Using a headless browser (like Selenium/Chrome) to capture a print-to-PDF.

3.  **Determine the Best Approach (Simplification & Dependencies):**
    *   Since this is a complex request, I must provide a *structured, runnable example* while acknowledging the dependency complexity.
    *   **Dependency Check:** Need `selenium`, a WebDriver (e.g., ChromeDriver), and a PDF generation library (e.g., `weasyprint` or a simpler alternative).
    *   **Simplification Strategy:** Instead of scraping *every* link, the example will focus on scraping the *first relevant link* from a search result.

4.  **Drafting the Code Structure (Iterative Development):**

    *   *Setup:* Import necessary libraries.
    *   *Driver Setup:* Initialize WebDriver.
    *   *Search Logic:* Define the search query. Navigate and submit.
    *   *Scraping Logic:* Find the link. Navigate to the link.
    *   *Extraction Logic:* Get the page source. Clean the HTML to extract readable text.
    *   *Conversion Logic:* Format the extracted text into Markdown.
    *   *Saving Logic:* Write to `.md`. Generate PDF.

5.  **Refining the Extraction (The Hardest Part):**
    *   Pure text extraction from arbitrary HTML is messy. Using libraries like `BeautifulSoup` *after* Selenium fetches the content is often cleaner than trying to parse raw Selenium elements.

6.  **Final Code Review and Explanation (Adding Instructions):**
    *   The user needs clear instructions on installation (pip install...).
    *   The code must handle potential errors (e.g., page not found).
    *   The explanation must break down the process (Search -> Extract -> Convert -> Save).

**(Self-Correction during drafting):** Generating a high-quality PDF directly from Markdown using `weasyprint` requires the Markdown to be converted to HTML first. I will use a simple Markdown-to-HTML step within the script for feasibility.

7.  **Generating the Final Response (in Korean):** (This matches the structure provided in the final output.) (This is the output presented to the user.)
[End thinking]

요청하신 기능은 웹 크롤링, 데이터 추출, 포맷 변환, 그리고 파일 저장이라는 여러 단계를 포함하는 상당히 복잡하고 전문적인 작업입니다.

이 기능을 구현하기 위해서는 **Selenium** (웹 브라우저 제어), **BeautifulSoup** (HTML 파싱), 그리고 **PDF 생성 라이브러리**가 필요합니다.

아래는 이 모든 기능을 통합한 파이썬 코드 예제입니다.

### 🛠️ 필수 설치 패키지

먼저 필요한 라이브러리를 설치해야 합니다.

```bash
pip install selenium beautifulsoup4 weasyprint
```

**⚠️ 중요:** Selenium을 사용하려면 사용하고 계신 브라우저(Chrome, Firefox 등)에 맞는 **WebDriver** (예: ChromeDriver)를 다운로드하여 시스템 PATH에 등록하거나 코드 내에서 경로를 지정해야 합니다. (예제에서는 Chrome을 사용한다고 가정합니다.)

---

### 🐍 파이썬 구현 코드

이 코드는 Google에서 특정 키워드를 검색한 후, 첫 번째 검색 결과 페이지로 이동하여 텍스트를 추출하고, 이를 Markdown 파일(`.md`)과 PDF 파일로 저장하는 과정을 시뮬레이션합니다.

```python
import time
import os
from selenium import webdriver
from seleniuhttp://m.webdriver.chrome.service import Service
from seleniuhttp://m.webdriver.common.by import By
from seleniuhttp://m.webdriver.chrome.options import Options
from bs4 import BeautifulSoup
from weasyprint import HTML # PDF 생성을 위한 라이브러리

# --- 설정 변수 ---
SEARCH_QUERY = "파이썬 셀레니움 웹 크롤링"
OUTPUT_DIR = "output_files"

def setup_driver():
    """Selenium WebDriver를 설정하고 반환합니다."""
    print("⚙️ WebDriver를 설정합니다...")
    chrome_options = Options()
    # 백그라운드 실행 설정 (선택 사항)
    # chrome_options.add_argument("--headless") 
    chrome_options.add_argument("--no-sandbox")
    chrome_options.add_argument("--disable-dev-shm-usage")
    
    # WebDriver 경로 설정 (사용자 환경에 맞게 수정해야 할 수 있습니다)
    # 예: service = Service('/path/to/chromedriver')
    service = Service() 
    driver = webdriver.Chrome(service=service, options=chrome_options)
    return driver

def search_and_extract_text(driver, query):
    """
    1. 웹 검색을 수행하고
    2. 첫 번째 검색 결과의 텍스트를 추출합니다.
    """
    print(f"\n🔍 검색 시작: '{query}'")
    
    # 1. Google 검색 페이지로 이동
    driver.get("https://www.google.com")
    time.sleep(2)

    # 2. 검색창에 쿼리 입력 및 검색 실행
    try:
        # Google 검색창의 name 속성을 사용하여 요소를 찾습니다.
        search_box = driver.find_element(By.NAME, "q")
        search_box.send_keys(query)
        search_box.submit()
        print("✅ 검색 요청 완료.")
        time.sleep(5) # 검색 결과 로딩 대기
    except Exception as e:
        print(f"❌ 검색창을 찾는 중 오류 발생: {e}")
        return None

    # 3. 검색 결과 페이지에서 첫 번째 링크를 클릭하여 이동
    try:
        # 검색 결과 링크를 찾습니다. (Google의 구조에 따라 Selector를 수정해야 할 수 있습니다.)
        # 여기서는 첫 번째 검색 결과 링크를 클릭합니다.
        first_result = driver.find_element(By.CSS_SELECTOR, "h3") # 제목을 선택하여 링크를 클릭하는 방식 예시
        first_result.click()
        print("✅ 첫 번째 검색 결과로 이동했습니다.")
        time.sleep(5) # 페이지 로딩 대기
    except Exception as e:
        print("❌ 검색 결과 링크를 찾는 중 오류 발생: 페이지 구조가 변경되었을 수 있습니다.")
        return None

    # 4. 페이지 소스 추출 및 텍스트 추출
    soup = BeautifulSoup(driver.page_source, 'html.parser')
    
    # 실제 내용이 담긴 영역을 특정해야 합니다. (예: 본문 영역)
    # 여기서는 body 태그 내부의 모든 텍스트를 추출합니다.
    extracted_text = soup.get_text(separator='\n', strip=True)
    
    print("✅ 텍스트 추출 완료.")
    return extracted_text

def convert_to_markdown(text):
    """
    추출된 텍스트를 Markdown 형식으로 변환합니다.
    (이 예제에서는 텍스트를 그대로 사용하며, 필요에 따라 헤더 등을 추가할 수 있습니다.)
    """
    markdown_content = f"# {SEARCH_QUERY}\n\n"
    markdown_content += "---" + "\n\n"
    markdown_content += text
    return markdown_content

def save_files(markdown_content, query):
    """
    Markdown 내용과 PDF를 지정된 폴더에 저장합니다.
    """
    if not os.path.exists(OUTPUT_DIR):
        os.makedirs(OUTPUT_DIR)
        print(f"📁 출력 폴더 '{OUTPUT_DIR}' 생성 완료.")

    # 1. MD 파일 저장
    md_filename = f"{query.replace(' ', '_')}.md"
    md_filepath = os.path.join(OUTPUT_DIR, md_filename)
    with open(md_filepath, 'w', encoding='utf-8') as f:
        f.write(markdown_content)
    print(f"💾 Markdown 파일 저장 완료: {md_filepath}")

    # 2. PDF 파일 저장
    pdf_filename = f"{query.replace(' ', '_')}.pdf"
    pdf_filepath = os.path.join(OUTPUT_DIR, pdf_filename)
    
    # Markdown을 HTML로 변환하여 PDF로 저장
    # WeasyPrint는 Markdown을 HTML로 변환하는 기능을 내부적으로 지원하거나, 
    # 별도의 라이브러리를 통해 HTML을 생성해야 합니다.
    # 여기서는 Markdown을 HTML로 변환하는 과정이 생략된 'raw text'를 HTML로 가정하고 저장합니다.
    # 실제 구현 시, Markdown -> HTML 변환 로직이 필요합니다.
    
    # (간단한 예시를 위해 텍스트를 임시 HTML로 변환하여 PDF 생성)
    html_content = f"<h1>{query}</h1>\n<p>{markdown_content.replace('\n', '<p>')}</p>"

    with open(pdf_filepath, 'w', encoding='utf-8') as f:
        f.write(html_content)
        
    print(f"📄 PDF 파일 저장 완료: {pdf_filepath}")


# --- 메인 실행 함수 ---
if __name__ == "__main__":
    driver = None
    try:
        # 1. 드라이버 설정
        driver = setup_driver()
        
        # 2. 검색 및 텍스트 추출
        extracted_text = search_and_extract_text(driver, SEARCH_QUERY)
        
        if extracted_text:
            # 3. Markdown 변환
            markdown_output = convert_to_markdown(extracted_text)
            
            # 4. 파일 저장
            save_files(markdown_output, SEARCH_QUERY)

    except Exception as e:
        print("\n\n🚨 치명적인 오류가 발생했습니다.")
        print(f"오류 내용: {e}")
        
    finally:
        # 5. 드라이버 종료
        if driver:
            print("\n🧹 WebDriver를 종료합니다.")
            driver.quit()

```

### 💡 코드 해설 및 주의사항

#### 1. Selenium (웹 서칭)
*   `setup_driver()` 함수는 Chrome 브라우저를 실행하고 웹페이지를 제어할 수 있는 `WebDriver` 객체를 초기화합니다.
*   `driver.get()`으로 URL에 접속하고, `find_element(By.NAME, "q")`와 같은 명령어로 검색창을 찾고 텍스트를 입력하여 검색을 실행합니다.
*   **주의:** 웹사이트의 HTML 구조(CSS Selector나 XPath)는 자주 변경됩니다. 만약 코드가 작동하지 않으면, 해당 웹사이트의 현재 HTML 구조를 확인하여 `driver.find_element()` 부분의 선택자를 수정해야 합니다.

#### 2. 텍스트 추출 (BeautifulSoup)
*   Selenium이 페이지를 로드한 후, `driver.page_source`를 통해 전체 HTML 코드를 가져옵니다.
*   `BeautifulSoup`을 사용하여 이 HTML을 파싱하고, `soup.get_text(separator='\n', strip=True)`를 사용하여 HTML 태그를 제거하고 순수한 텍스트만 추출합니다.

#### 3. Markdown 변환
*   `convert_to_markdown()` 함수는 추출된 텍스트를 `#` 헤더와 구분선 등을 사용하여 기본적인 Markdown 형식으로 감싸줍니다.

#### 4. 파일 저장 (MD 및 PDF)
*   **MD 파일 저장:** 추출된 Markdown 문자열을 파일로 직접 씁니다.
*   **PDF 파일 저장:**
    *   PDF를 생성하기 위해서는 **HTML**이 필요합니다.
    *   `weasyprint` 라이브러리는 HTML과 CSS를 PDF로 변환해주는 강력한 도구입니다.
    *   **주의:** 위 코드에서는 추출된 텍스트를 임시 HTML 형식으로 변환한 후 PDF로 저장하는 과정을 보여주었습니다. 실제 복잡한 웹페이지를 PDF로 변환할 때는, 추출된 텍스트를 **구조화된 HTML**로 변환하는 추가적인 정교한 작업이 필요합니다.

### ⚠️ 추가 구현을 위한 팁

1.  **정확한 추출:** 단순히 `soup.get_text()`는 페이지의 모든 텍스트를 추출합니다. 특정 기사 본문만 추출하고 싶다면, 해당 기사 본문이 위치한 **특정 CSS Selector**를 찾아 그 영역만 `soup.select()`로 추출해야 합니다.
2.  **오류 처리 강화:** `try...except` 블록을 더 상세하게 사용하여 네트워크 오류, 요소 미발견 오류 등을 처리하면 프로그램의 안정성이 크게 향상됩니다.
3.  **PDF 변환 최적화:** 만약 추출된 텍스트를 깔끔한 Markdown으로 만들고 싶다면, `markdown` 라이브러리를 사용하여 텍스트를 Markdown으로 변환한 후, 그 Markdown을 다시 HTML로 변환하여 `weasyprint`로 PDF를 생성하는 다단계 프로세스를 구현할 수 있습니다.

[ Prompt: 1520.7 t/s | Generation: 72.7 t/s ]

 

stable diffusion 1024x1024 illustrious XL

 ./webui.sh --server-name=0.0.0.0

 

와.. 16기가라 넉넉하구만

 

다만 뽑아보는데 그렇게 빠르다는 느낌은 안든다. bf16을 지원하지 않아서 그런듯..

Model loaded in 18.6s (load weights from disk: 1.5s, create model: 0.5s, apply weights to model: 16.2s, calculate empty prompt: 0.2s).
100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:25<00:00,  1.27s/it]
Total progress: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:25<00:00,  1.29s/it]
Total progress: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:25<00:00,  1.33s/it]

 

28.8초..

 

소비전력 제한 범위가 무지 좁다.

$ sudo nvidia-smi -pl 10
Provided power limit 10.00 W is not a valid power limit which should be between 60.00 W and 70.00 W for GPU 00000000:01:00.0
Terminating early due to previous errors.

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Posted by 구차니

옵션을 살펴보면

/llama-b9305$ ./llama-server --help
/llama-b9305$ ./llama-cli --help

 

4개 정도가 나오는데 gemma4-e4b는 reasoning을 지원하는지 옵션없이 실행하면 켜져있는 듯 하다.

--reasoning-format FORMAT               controls whether thought tags are allowed and/or extracted from the
                                        response, and in which format they're returned; one of:
                                        - none: leaves thoughts unparsed in `message.content`
                                        - deepseek: puts thoughts in `message.reasoning_content`
                                        - deepseek-legacy: keeps `<think>` tags in `message.content` while
                                        also populating `message.reasoning_content`
                                        (default: auto)
                                        (env: LLAMA_ARG_THINK)
-rea,  --reasoning [on|off|auto]        Use reasoning/thinking in the chat ('on', 'off', or 'auto', default:
                                        'auto' (detect from template))
                                        (env: LLAMA_ARG_REASONING)
--reasoning-budget N                    token budget for thinking: -1 for unrestricted, 0 for immediate end,
                                        N>0 for token budget (default: -1)
                                        (env: LLAMA_ARG_THINK_BUDGET)
--reasoning-budget-message MESSAGE      message injected before the end-of-thinking tag when reasoning budget
                                        is exhausted (default: none)
                                        (env: LLAMA_ARG_THINK_BUDGET_MESSAGE)

 

cli  에서는 잘 먹는것 같다.

$ llama-b9305/llama-cli  --model /mnt/Downloads/model/gemma4-e2b/gemma-4-E2B-it-Q4_K_M.gguf -rea on -sm none

Loading model...


▄▄ ▄▄
██ ██
██ ██  ▀▀█▄ ███▄███▄  ▀▀█▄    ▄████ ████▄ ████▄
██ ██ ▄█▀██ ██ ██ ██ ▄█▀██    ██    ██ ██ ██ ██
██ ██ ▀█▄██ ██ ██ ██ ▀█▄██ ██ ▀████ ████▀ ████▀
                                    ██    ██
                                    ▀▀    ▀▀

build      : b9305-63248fc3e
model      : gemma-4-E2B-it-Q4_K_M.gguf
modalities : text

available commands:
  /exit or Ctrl+C     stop or exit
  /regen              regenerate the last response
  /clear              clear the chat history
  /read <file>        add a text file
  /glob <pattern>     add text files using globbing pattern


> 무슨생각하고 있어?

[Start thinking]
Thinking Process:

1.  **Analyze the Request:** The user asked "무슨 생각하고 있어?" (What are you thinking?). This is an open-ended question about the model's internal state or current activity
.
2.  **Determine Identity and Nature:** I am Gemma 4, a Large Language Model. I do not have consciousness, feelings, personal thoughts, or internal subjective experiences.
3.  **Identify the Core Activity:** My current "thought" process is processing the input (the user's question) and generating a relevant, coherent, and helpful response.
4.  **Formulate the Answer Strategy (Internal Monologue -> External Response):**
    *   Acknowledge the nature of the question.                                                                                                                                    *   State clearly that I don't "think" in a human sense (no consciousness/feelings).
    *   Describe what I *am* doing (processing information, analyzing language, generating text).
    *   Pivot back to the user (ask how I can help).
5.  **Draft the Response in Korean (Focusing on natural, polite tone):**

    *   *Initial thought:* 저는 생각하지 않아요. (I don't think.)
    *   *Elaboration:* 저는 정보를 처리하고 답변을 생성하고 있어요. (I am processing information and generating answers.)
    *   *Contextualizing the current state:* 지금 사용자님의 질문을 이해하고 가장 적절한 답변을 찾고 있습니다. (Right now, I am understanding your question and finding the mos
t appropriate answer.)
    *   *Call to action:* 무엇을 도와드릴까요? (What can I help you with?)

6.  **Final Review:** The response is accurate, polite, and directly addresses the user's intent while staying true to my identity as an LLM. (The drafted Korean response belo
w is good.)
[End thinking]

저는 인간처럼 '생각'을 하지는 않습니다. 저는 대규모 언어 모델(Large Language Model)이기 때문에, 정보를 처리하고, 패턴을 분석하며, 사용자님의 질문에 가장 적절하고 유용한 답변을
 생성하는 작업을 하고 있습니다.

지금은 사용자님의 질문을 이해하고, 그에 맞는 정보를 찾고 답변을 구성하는 과정에 집중하고 있습니다.

궁금한 것이 있거나 도움이 필요하시면 언제든지 말씀해 주세요! 😊
[ Prompt: 193.7 t/s | Generation: 94.4 t/s ]

>

Exiting...

 

$ llama-b9305/llama-cli  --model /mnt/Downloads/model/gemma4-e2b/gemma-4-E2B-it-Q4_K_M.gguf -rea off -sm none

Loading model...


▄▄ ▄▄
██ ██
██ ██  ▀▀█▄ ███▄███▄  ▀▀█▄    ▄████ ████▄ ████▄
██ ██ ▄█▀██ ██ ██ ██ ▄█▀██    ██    ██ ██ ██ ██
██ ██ ▀█▄██ ██ ██ ██ ▀█▄██ ██ ▀████ ████▀ ████▀
                                    ██    ██
                                    ▀▀    ▀▀

build      : b9305-63248fc3e
model      : gemma-4-E2B-it-Q4_K_M.gguf
modalities : text

available commands:
  /exit or Ctrl+C     stop or exit
  /regen              regenerate the last response
  /clear              clear the chat history
  /read <file>        add a text file
  /glob <pattern>     add text files using globbing pattern


> 무슨생각하고 있어?

저는 Google에서 훈련한 대규모 언어 모델입니다. 😊

**지금 저는 당신의 질문에 답하고, 정보를 제공하고, 창의적인 텍스트를 생성하는 등 제가 할 수 있는 모든 작업을 수행하고 있습니다.**

혹시 **특정한 주제**에 대해 궁금하신가요? 아니면 **제가 무엇을 할 수 있는지** 더 알고 싶으신가요?

어떤 것에 대해 이야기하고 싶으신지 말씀해주시면, 그에 맞춰서 생각하고 대화할 준비가 되어 있습니다!

[ Prompt: 126.5 t/s | Generation: 95.2 t/s ]

>

Exiting...

 

llama-swap 에서 llama-server에 -rea off 하고 질문을 하면 옵션이 안 먹나? 똑같이 reasoning 나온다. 머냐?

 

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Posted by 구차니

한번 해봐야지. 그나저나 1080 에서 F16만 된다고 하면.. 그냥 직접 변환하는것도 방법이 될려나?

/llama.cpp$ ls convert_*
convert_hf_to_gguf.py  convert_hf_to_gguf_update.py  convert_llama_ggml_to_gguf.py  convert_lora_to_gguf.py

 

[링크 : https://huggingface.co/google/gemma-4-E4B/tree/main]

[링크 : https://velog.io/@choonsik_mom/llama.cpp로-gguf-모델-서빙하기-ul02hone]

 

+

실행해보면 다음과 같이 나오고

$ /home/minimonk/src/llama.cpp/convert_hf_to_gguf.py 
usage: convert_hf_to_gguf.py [-h] [--vocab-only] [--outfile OUTFILE] [--outtype {f32,f16,bf16,q8_0,tq1_0,tq2_0,auto}]
                             [--bigendian] [--use-temp-file] [--no-lazy] [--model-name MODEL_NAME] [--verbose]
                             [--split-max-tensors SPLIT_MAX_TENSORS] [--split-max-size SPLIT_MAX_SIZE] [--dry-run]
                             [--no-tensor-first-split] [--metadata METADATA] [--print-supported-models] [--remote]
                             [--mmproj] [--mistral-format] [--disable-mistral-community-chat-template]
                             [--sentence-transformers-dense-modules] [--fuse-gate-up-exps]
                             [model]
convert_hf_to_gguf.py: error: the following arguments are required: model

 

huggingface를 통해서 받아도 되고, 최소한의 파일 저 3개를 받아줘도 된다.

/mnt/Downloads/model/gemma4-e4b/google$ ls
config.json  model.safetensors  tokenizer.json

 

GPU를 사용하지 않고, 한번에 메모리에 전부 올려서 하는건지 메모리 터져서 한번 리부팅.. 크흡

그 와중에 옵션 안주니까 BF16 으로 설정해버리는 센스(!) 하지만 난 BF16 가속을 못받는 하드웨어.. ㅠ

$ /home/minimonk/src/llama.cpp/convert_hf_to_gguf.py ./google/ --outfile gguf.gguf
INFO:hf-to-gguf:Loading model: google
INFO:hf-to-gguf:Model architecture: Gemma4ForConditionalGeneration
INFO:hf-to-gguf:gguf: indexing model part 'model.safetensors'
INFO:hf-to-gguf:heuristics detected bfloat16 tensor dtype, setting --outtype bf16
INFO:gguf.gguf_writer:gguf: This GGUF file is for Little Endian only
INFO:hf-to-gguf:Exporting model...

Writing:   9%|██████▎                                                                | 1.34G/15.0G [00:20<01:16, 180Mbyte/s]

Writing: 100%|███████████████████████████████████████████████████████████████████████| 15.0G/15.0G [01:54<00:00, 131Mbyte/s]
INFO:hf-to-gguf:Model successfully exported to gguf.gguf

 

exporting model.. 과 wrting 사이 로그

더보기

INFO:hf-to-gguf:rope_freqs.weight,                 torch.float32 --> F32, shape = {256}
INFO:hf-to-gguf:token_embd.weight,                 torch.bfloat16 --> BF16, shape = {2560, 262144}
INFO:hf-to-gguf:per_layer_token_embd.weight,       torch.bfloat16 --> BF16, shape = {10752, 262144}
INFO:hf-to-gguf:blk.0.attn_norm.weight,            torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.0.layer_output_scale.weight,   torch.bfloat16 --> F32, shape = {1}
INFO:hf-to-gguf:blk.0.ffn_down.weight,             torch.bfloat16 --> BF16, shape = {10240, 2560}
INFO:hf-to-gguf:blk.0.ffn_gate.weight,             torch.bfloat16 --> BF16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.0.ffn_up.weight,               torch.bfloat16 --> BF16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.0.inp_gate.weight,             torch.bfloat16 --> BF16, shape = {2560, 256}
INFO:hf-to-gguf:blk.0.proj.weight,                 torch.bfloat16 --> BF16, shape = {256, 2560}
INFO:hf-to-gguf:blk.0.post_attention_norm.weight,  torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.0.post_ffw_norm.weight,        torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.0.post_norm.weight,            torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.0.ffn_norm.weight,             torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.0.attn_k_norm.weight,          torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.0.attn_k.weight,               torch.bfloat16 --> BF16, shape = {2560, 512}
INFO:hf-to-gguf:blk.0.attn_output.weight,          torch.bfloat16 --> BF16, shape = {2048, 2560}
INFO:hf-to-gguf:blk.0.attn_q_norm.weight,          torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.0.attn_q.weight,               torch.bfloat16 --> BF16, shape = {2560, 2048}
INFO:hf-to-gguf:blk.0.attn_v.weight,               torch.bfloat16 --> BF16, shape = {2560, 512}
INFO:hf-to-gguf:blk.1.attn_norm.weight,            torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.1.layer_output_scale.weight,   torch.bfloat16 --> F32, shape = {1}
INFO:hf-to-gguf:blk.1.ffn_down.weight,             torch.bfloat16 --> BF16, shape = {10240, 2560}
INFO:hf-to-gguf:blk.1.ffn_gate.weight,             torch.bfloat16 --> BF16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.1.ffn_up.weight,               torch.bfloat16 --> BF16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.1.inp_gate.weight,             torch.bfloat16 --> BF16, shape = {2560, 256}
INFO:hf-to-gguf:blk.1.proj.weight,                 torch.bfloat16 --> BF16, shape = {256, 2560}
INFO:hf-to-gguf:blk.1.post_attention_norm.weight,  torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.1.post_ffw_norm.weight,        torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.1.post_norm.weight,            torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.1.ffn_norm.weight,             torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.1.attn_k_norm.weight,          torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.1.attn_k.weight,               torch.bfloat16 --> BF16, shape = {2560, 512}
INFO:hf-to-gguf:blk.1.attn_output.weight,          torch.bfloat16 --> BF16, shape = {2048, 2560}
INFO:hf-to-gguf:blk.1.attn_q_norm.weight,          torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.1.attn_q.weight,               torch.bfloat16 --> BF16, shape = {2560, 2048}
INFO:hf-to-gguf:blk.1.attn_v.weight,               torch.bfloat16 --> BF16, shape = {2560, 512}
INFO:hf-to-gguf:blk.10.attn_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.10.layer_output_scale.weight,  torch.bfloat16 --> F32, shape = {1}
INFO:hf-to-gguf:blk.10.ffn_down.weight,            torch.bfloat16 --> BF16, shape = {10240, 2560}
INFO:hf-to-gguf:blk.10.ffn_gate.weight,            torch.bfloat16 --> BF16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.10.ffn_up.weight,              torch.bfloat16 --> BF16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.10.inp_gate.weight,            torch.bfloat16 --> BF16, shape = {2560, 256}
INFO:hf-to-gguf:blk.10.proj.weight,                torch.bfloat16 --> BF16, shape = {256, 2560}
INFO:hf-to-gguf:blk.10.post_attention_norm.weight, torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.10.post_ffw_norm.weight,       torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.10.post_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.10.ffn_norm.weight,            torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.10.attn_k_norm.weight,         torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.10.attn_k.weight,              torch.bfloat16 --> BF16, shape = {2560, 512}
INFO:hf-to-gguf:blk.10.attn_output.weight,         torch.bfloat16 --> BF16, shape = {2048, 2560}
INFO:hf-to-gguf:blk.10.attn_q_norm.weight,         torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.10.attn_q.weight,              torch.bfloat16 --> BF16, shape = {2560, 2048}
INFO:hf-to-gguf:blk.10.attn_v.weight,              torch.bfloat16 --> BF16, shape = {2560, 512}
INFO:hf-to-gguf:blk.11.attn_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.11.layer_output_scale.weight,  torch.bfloat16 --> F32, shape = {1}
INFO:hf-to-gguf:blk.11.ffn_down.weight,            torch.bfloat16 --> BF16, shape = {10240, 2560}
INFO:hf-to-gguf:blk.11.ffn_gate.weight,            torch.bfloat16 --> BF16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.11.ffn_up.weight,              torch.bfloat16 --> BF16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.11.inp_gate.weight,            torch.bfloat16 --> BF16, shape = {2560, 256}
INFO:hf-to-gguf:blk.11.proj.weight,                torch.bfloat16 --> BF16, shape = {256, 2560}
INFO:hf-to-gguf:blk.11.post_attention_norm.weight, torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.11.post_ffw_norm.weight,       torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.11.post_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.11.ffn_norm.weight,            torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.11.attn_k_norm.weight,         torch.bfloat16 --> F32, shape = {512}
INFO:hf-to-gguf:blk.11.attn_k.weight,              torch.bfloat16 --> BF16, shape = {2560, 1024}
INFO:hf-to-gguf:blk.11.attn_output.weight,         torch.bfloat16 --> BF16, shape = {4096, 2560}
INFO:hf-to-gguf:blk.11.attn_q_norm.weight,         torch.bfloat16 --> F32, shape = {512}
INFO:hf-to-gguf:blk.11.attn_q.weight,              torch.bfloat16 --> BF16, shape = {2560, 4096}
INFO:hf-to-gguf:blk.11.attn_v.weight,              torch.bfloat16 --> BF16, shape = {2560, 1024}
INFO:hf-to-gguf:blk.12.attn_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.12.layer_output_scale.weight,  torch.bfloat16 --> F32, shape = {1}
INFO:hf-to-gguf:blk.12.ffn_down.weight,            torch.bfloat16 --> BF16, shape = {10240, 2560}
INFO:hf-to-gguf:blk.12.ffn_gate.weight,            torch.bfloat16 --> BF16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.12.ffn_up.weight,              torch.bfloat16 --> BF16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.12.inp_gate.weight,            torch.bfloat16 --> BF16, shape = {2560, 256}
INFO:hf-to-gguf:blk.12.proj.weight,                torch.bfloat16 --> BF16, shape = {256, 2560}
INFO:hf-to-gguf:blk.12.post_attention_norm.weight, torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.12.post_ffw_norm.weight,       torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.12.post_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.12.ffn_norm.weight,            torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.12.attn_k_norm.weight,         torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.12.attn_k.weight,              torch.bfloat16 --> BF16, shape = {2560, 512}
INFO:hf-to-gguf:blk.12.attn_output.weight,         torch.bfloat16 --> BF16, shape = {2048, 2560}
INFO:hf-to-gguf:blk.12.attn_q_norm.weight,         torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.12.attn_q.weight,              torch.bfloat16 --> BF16, shape = {2560, 2048}
INFO:hf-to-gguf:blk.12.attn_v.weight,              torch.bfloat16 --> BF16, shape = {2560, 512}
INFO:hf-to-gguf:blk.13.attn_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.13.layer_output_scale.weight,  torch.bfloat16 --> F32, shape = {1}
INFO:hf-to-gguf:blk.13.ffn_down.weight,            torch.bfloat16 --> BF16, shape = {10240, 2560}
INFO:hf-to-gguf:blk.13.ffn_gate.weight,            torch.bfloat16 --> BF16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.13.ffn_up.weight,              torch.bfloat16 --> BF16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.13.inp_gate.weight,            torch.bfloat16 --> BF16, shape = {2560, 256}
INFO:hf-to-gguf:blk.13.proj.weight,                torch.bfloat16 --> BF16, shape = {256, 2560}
INFO:hf-to-gguf:blk.13.post_attention_norm.weight, torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.13.post_ffw_norm.weight,       torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.13.post_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.13.ffn_norm.weight,            torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.13.attn_k_norm.weight,         torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.13.attn_k.weight,              torch.bfloat16 --> BF16, shape = {2560, 512}
INFO:hf-to-gguf:blk.13.attn_output.weight,         torch.bfloat16 --> BF16, shape = {2048, 2560}
INFO:hf-to-gguf:blk.13.attn_q_norm.weight,         torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.13.attn_q.weight,              torch.bfloat16 --> BF16, shape = {2560, 2048}
INFO:hf-to-gguf:blk.13.attn_v.weight,              torch.bfloat16 --> BF16, shape = {2560, 512}
INFO:hf-to-gguf:blk.14.attn_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.14.layer_output_scale.weight,  torch.bfloat16 --> F32, shape = {1}
INFO:hf-to-gguf:blk.14.ffn_down.weight,            torch.bfloat16 --> BF16, shape = {10240, 2560}
INFO:hf-to-gguf:blk.14.ffn_gate.weight,            torch.bfloat16 --> BF16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.14.ffn_up.weight,              torch.bfloat16 --> BF16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.14.inp_gate.weight,            torch.bfloat16 --> BF16, shape = {2560, 256}
INFO:hf-to-gguf:blk.14.proj.weight,                torch.bfloat16 --> BF16, shape = {256, 2560}
INFO:hf-to-gguf:blk.14.post_attention_norm.weight, torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.14.post_ffw_norm.weight,       torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.14.post_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.14.ffn_norm.weight,            torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.14.attn_k_norm.weight,         torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.14.attn_k.weight,              torch.bfloat16 --> BF16, shape = {2560, 512}
INFO:hf-to-gguf:blk.14.attn_output.weight,         torch.bfloat16 --> BF16, shape = {2048, 2560}
INFO:hf-to-gguf:blk.14.attn_q_norm.weight,         torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.14.attn_q.weight,              torch.bfloat16 --> BF16, shape = {2560, 2048}
INFO:hf-to-gguf:blk.14.attn_v.weight,              torch.bfloat16 --> BF16, shape = {2560, 512}
INFO:hf-to-gguf:blk.15.attn_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.15.layer_output_scale.weight,  torch.bfloat16 --> F32, shape = {1}
INFO:hf-to-gguf:blk.15.ffn_down.weight,            torch.bfloat16 --> BF16, shape = {10240, 2560}
INFO:hf-to-gguf:blk.15.ffn_gate.weight,            torch.bfloat16 --> BF16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.15.ffn_up.weight,              torch.bfloat16 --> BF16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.15.inp_gate.weight,            torch.bfloat16 --> BF16, shape = {2560, 256}
INFO:hf-to-gguf:blk.15.proj.weight,                torch.bfloat16 --> BF16, shape = {256, 2560}
INFO:hf-to-gguf:blk.15.post_attention_norm.weight, torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.15.post_ffw_norm.weight,       torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.15.post_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.15.ffn_norm.weight,            torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.15.attn_k_norm.weight,         torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.15.attn_k.weight,              torch.bfloat16 --> BF16, shape = {2560, 512}
INFO:hf-to-gguf:blk.15.attn_output.weight,         torch.bfloat16 --> BF16, shape = {2048, 2560}
INFO:hf-to-gguf:blk.15.attn_q_norm.weight,         torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.15.attn_q.weight,              torch.bfloat16 --> BF16, shape = {2560, 2048}
INFO:hf-to-gguf:blk.15.attn_v.weight,              torch.bfloat16 --> BF16, shape = {2560, 512}
INFO:hf-to-gguf:blk.16.attn_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.16.layer_output_scale.weight,  torch.bfloat16 --> F32, shape = {1}
INFO:hf-to-gguf:blk.16.ffn_down.weight,            torch.bfloat16 --> BF16, shape = {10240, 2560}
INFO:hf-to-gguf:blk.16.ffn_gate.weight,            torch.bfloat16 --> BF16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.16.ffn_up.weight,              torch.bfloat16 --> BF16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.16.inp_gate.weight,            torch.bfloat16 --> BF16, shape = {2560, 256}
INFO:hf-to-gguf:blk.16.proj.weight,                torch.bfloat16 --> BF16, shape = {256, 2560}
INFO:hf-to-gguf:blk.16.post_attention_norm.weight, torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.16.post_ffw_norm.weight,       torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.16.post_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.16.ffn_norm.weight,            torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.16.attn_k_norm.weight,         torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.16.attn_k.weight,              torch.bfloat16 --> BF16, shape = {2560, 512}
INFO:hf-to-gguf:blk.16.attn_output.weight,         torch.bfloat16 --> BF16, shape = {2048, 2560}
INFO:hf-to-gguf:blk.16.attn_q_norm.weight,         torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.16.attn_q.weight,              torch.bfloat16 --> BF16, shape = {2560, 2048}
INFO:hf-to-gguf:blk.16.attn_v.weight,              torch.bfloat16 --> BF16, shape = {2560, 512}
INFO:hf-to-gguf:blk.17.attn_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.17.layer_output_scale.weight,  torch.bfloat16 --> F32, shape = {1}
INFO:hf-to-gguf:blk.17.ffn_down.weight,            torch.bfloat16 --> BF16, shape = {10240, 2560}
INFO:hf-to-gguf:blk.17.ffn_gate.weight,            torch.bfloat16 --> BF16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.17.ffn_up.weight,              torch.bfloat16 --> BF16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.17.inp_gate.weight,            torch.bfloat16 --> BF16, shape = {2560, 256}
INFO:hf-to-gguf:blk.17.proj.weight,                torch.bfloat16 --> BF16, shape = {256, 2560}
INFO:hf-to-gguf:blk.17.post_attention_norm.weight, torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.17.post_ffw_norm.weight,       torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.17.post_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.17.ffn_norm.weight,            torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.17.attn_k_norm.weight,         torch.bfloat16 --> F32, shape = {512}
INFO:hf-to-gguf:blk.17.attn_k.weight,              torch.bfloat16 --> BF16, shape = {2560, 1024}
INFO:hf-to-gguf:blk.17.attn_output.weight,         torch.bfloat16 --> BF16, shape = {4096, 2560}
INFO:hf-to-gguf:blk.17.attn_q_norm.weight,         torch.bfloat16 --> F32, shape = {512}
INFO:hf-to-gguf:blk.17.attn_q.weight,              torch.bfloat16 --> BF16, shape = {2560, 4096}
INFO:hf-to-gguf:blk.17.attn_v.weight,              torch.bfloat16 --> BF16, shape = {2560, 1024}
INFO:hf-to-gguf:blk.18.attn_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.18.layer_output_scale.weight,  torch.bfloat16 --> F32, shape = {1}
INFO:hf-to-gguf:blk.18.ffn_down.weight,            torch.bfloat16 --> BF16, shape = {10240, 2560}
INFO:hf-to-gguf:blk.18.ffn_gate.weight,            torch.bfloat16 --> BF16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.18.ffn_up.weight,              torch.bfloat16 --> BF16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.18.inp_gate.weight,            torch.bfloat16 --> BF16, shape = {2560, 256}
INFO:hf-to-gguf:blk.18.proj.weight,                torch.bfloat16 --> BF16, shape = {256, 2560}
INFO:hf-to-gguf:blk.18.post_attention_norm.weight, torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.18.post_ffw_norm.weight,       torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.18.post_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.18.ffn_norm.weight,            torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.18.attn_k_norm.weight,         torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.18.attn_k.weight,              torch.bfloat16 --> BF16, shape = {2560, 512}
INFO:hf-to-gguf:blk.18.attn_output.weight,         torch.bfloat16 --> BF16, shape = {2048, 2560}
INFO:hf-to-gguf:blk.18.attn_q_norm.weight,         torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.18.attn_q.weight,              torch.bfloat16 --> BF16, shape = {2560, 2048}
INFO:hf-to-gguf:blk.18.attn_v.weight,              torch.bfloat16 --> BF16, shape = {2560, 512}
INFO:hf-to-gguf:blk.19.attn_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.19.layer_output_scale.weight,  torch.bfloat16 --> F32, shape = {1}
INFO:hf-to-gguf:blk.19.ffn_down.weight,            torch.bfloat16 --> BF16, shape = {10240, 2560}
INFO:hf-to-gguf:blk.19.ffn_gate.weight,            torch.bfloat16 --> BF16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.19.ffn_up.weight,              torch.bfloat16 --> BF16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.19.inp_gate.weight,            torch.bfloat16 --> BF16, shape = {2560, 256}
INFO:hf-to-gguf:blk.19.proj.weight,                torch.bfloat16 --> BF16, shape = {256, 2560}
INFO:hf-to-gguf:blk.19.post_attention_norm.weight, torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.19.post_ffw_norm.weight,       torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.19.post_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.19.ffn_norm.weight,            torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.19.attn_k_norm.weight,         torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.19.attn_k.weight,              torch.bfloat16 --> BF16, shape = {2560, 512}
INFO:hf-to-gguf:blk.19.attn_output.weight,         torch.bfloat16 --> BF16, shape = {2048, 2560}
INFO:hf-to-gguf:blk.19.attn_q_norm.weight,         torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.19.attn_q.weight,              torch.bfloat16 --> BF16, shape = {2560, 2048}
INFO:hf-to-gguf:blk.19.attn_v.weight,              torch.bfloat16 --> BF16, shape = {2560, 512}
INFO:hf-to-gguf:blk.2.attn_norm.weight,            torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.2.layer_output_scale.weight,   torch.bfloat16 --> F32, shape = {1}
INFO:hf-to-gguf:blk.2.ffn_down.weight,             torch.bfloat16 --> BF16, shape = {10240, 2560}
INFO:hf-to-gguf:blk.2.ffn_gate.weight,             torch.bfloat16 --> BF16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.2.ffn_up.weight,               torch.bfloat16 --> BF16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.2.inp_gate.weight,             torch.bfloat16 --> BF16, shape = {2560, 256}
INFO:hf-to-gguf:blk.2.proj.weight,                 torch.bfloat16 --> BF16, shape = {256, 2560}
INFO:hf-to-gguf:blk.2.post_attention_norm.weight,  torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.2.post_ffw_norm.weight,        torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.2.post_norm.weight,            torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.2.ffn_norm.weight,             torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.2.attn_k_norm.weight,          torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.2.attn_k.weight,               torch.bfloat16 --> BF16, shape = {2560, 512}
INFO:hf-to-gguf:blk.2.attn_output.weight,          torch.bfloat16 --> BF16, shape = {2048, 2560}
INFO:hf-to-gguf:blk.2.attn_q_norm.weight,          torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.2.attn_q.weight,               torch.bfloat16 --> BF16, shape = {2560, 2048}
INFO:hf-to-gguf:blk.2.attn_v.weight,               torch.bfloat16 --> BF16, shape = {2560, 512}
INFO:hf-to-gguf:blk.20.attn_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.20.layer_output_scale.weight,  torch.bfloat16 --> F32, shape = {1}
INFO:hf-to-gguf:blk.20.ffn_down.weight,            torch.bfloat16 --> BF16, shape = {10240, 2560}
INFO:hf-to-gguf:blk.20.ffn_gate.weight,            torch.bfloat16 --> BF16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.20.ffn_up.weight,              torch.bfloat16 --> BF16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.20.inp_gate.weight,            torch.bfloat16 --> BF16, shape = {2560, 256}
INFO:hf-to-gguf:blk.20.proj.weight,                torch.bfloat16 --> BF16, shape = {256, 2560}
INFO:hf-to-gguf:blk.20.post_attention_norm.weight, torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.20.post_ffw_norm.weight,       torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.20.post_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.20.ffn_norm.weight,            torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.20.attn_k_norm.weight,         torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.20.attn_k.weight,              torch.bfloat16 --> BF16, shape = {2560, 512}
INFO:hf-to-gguf:blk.20.attn_output.weight,         torch.bfloat16 --> BF16, shape = {2048, 2560}
INFO:hf-to-gguf:blk.20.attn_q_norm.weight,         torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.20.attn_q.weight,              torch.bfloat16 --> BF16, shape = {2560, 2048}
INFO:hf-to-gguf:blk.20.attn_v.weight,              torch.bfloat16 --> BF16, shape = {2560, 512}
INFO:hf-to-gguf:blk.21.attn_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.21.layer_output_scale.weight,  torch.bfloat16 --> F32, shape = {1}
INFO:hf-to-gguf:blk.21.ffn_down.weight,            torch.bfloat16 --> BF16, shape = {10240, 2560}
INFO:hf-to-gguf:blk.21.ffn_gate.weight,            torch.bfloat16 --> BF16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.21.ffn_up.weight,              torch.bfloat16 --> BF16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.21.inp_gate.weight,            torch.bfloat16 --> BF16, shape = {2560, 256}
INFO:hf-to-gguf:blk.21.proj.weight,                torch.bfloat16 --> BF16, shape = {256, 2560}
INFO:hf-to-gguf:blk.21.post_attention_norm.weight, torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.21.post_ffw_norm.weight,       torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.21.post_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.21.ffn_norm.weight,            torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.21.attn_k_norm.weight,         torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.21.attn_k.weight,              torch.bfloat16 --> BF16, shape = {2560, 512}
INFO:hf-to-gguf:blk.21.attn_output.weight,         torch.bfloat16 --> BF16, shape = {2048, 2560}
INFO:hf-to-gguf:blk.21.attn_q_norm.weight,         torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.21.attn_q.weight,              torch.bfloat16 --> BF16, shape = {2560, 2048}
INFO:hf-to-gguf:blk.21.attn_v.weight,              torch.bfloat16 --> BF16, shape = {2560, 512}
INFO:hf-to-gguf:blk.22.attn_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.22.layer_output_scale.weight,  torch.bfloat16 --> F32, shape = {1}
INFO:hf-to-gguf:blk.22.ffn_down.weight,            torch.bfloat16 --> BF16, shape = {10240, 2560}
INFO:hf-to-gguf:blk.22.ffn_gate.weight,            torch.bfloat16 --> BF16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.22.ffn_up.weight,              torch.bfloat16 --> BF16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.22.inp_gate.weight,            torch.bfloat16 --> BF16, shape = {2560, 256}
INFO:hf-to-gguf:blk.22.proj.weight,                torch.bfloat16 --> BF16, shape = {256, 2560}
INFO:hf-to-gguf:blk.22.post_attention_norm.weight, torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.22.post_ffw_norm.weight,       torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.22.post_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.22.ffn_norm.weight,            torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.22.attn_k_norm.weight,         torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.22.attn_k.weight,              torch.bfloat16 --> BF16, shape = {2560, 512}
INFO:hf-to-gguf:blk.22.attn_output.weight,         torch.bfloat16 --> BF16, shape = {2048, 2560}
INFO:hf-to-gguf:blk.22.attn_q_norm.weight,         torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.22.attn_q.weight,              torch.bfloat16 --> BF16, shape = {2560, 2048}
INFO:hf-to-gguf:blk.22.attn_v.weight,              torch.bfloat16 --> BF16, shape = {2560, 512}
INFO:hf-to-gguf:blk.23.attn_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.23.layer_output_scale.weight,  torch.bfloat16 --> F32, shape = {1}
INFO:hf-to-gguf:blk.23.ffn_down.weight,            torch.bfloat16 --> BF16, shape = {10240, 2560}
INFO:hf-to-gguf:blk.23.ffn_gate.weight,            torch.bfloat16 --> BF16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.23.ffn_up.weight,              torch.bfloat16 --> BF16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.23.inp_gate.weight,            torch.bfloat16 --> BF16, shape = {2560, 256}
INFO:hf-to-gguf:blk.23.proj.weight,                torch.bfloat16 --> BF16, shape = {256, 2560}
INFO:hf-to-gguf:blk.23.post_attention_norm.weight, torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.23.post_ffw_norm.weight,       torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.23.post_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.23.ffn_norm.weight,            torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.23.attn_k_norm.weight,         torch.bfloat16 --> F32, shape = {512}
INFO:hf-to-gguf:blk.23.attn_k.weight,              torch.bfloat16 --> BF16, shape = {2560, 1024}
INFO:hf-to-gguf:blk.23.attn_output.weight,         torch.bfloat16 --> BF16, shape = {4096, 2560}
INFO:hf-to-gguf:blk.23.attn_q_norm.weight,         torch.bfloat16 --> F32, shape = {512}
INFO:hf-to-gguf:blk.23.attn_q.weight,              torch.bfloat16 --> BF16, shape = {2560, 4096}
INFO:hf-to-gguf:blk.23.attn_v.weight,              torch.bfloat16 --> BF16, shape = {2560, 1024}
INFO:hf-to-gguf:blk.24.attn_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.24.layer_output_scale.weight,  torch.bfloat16 --> F32, shape = {1}
INFO:hf-to-gguf:blk.24.ffn_down.weight,            torch.bfloat16 --> BF16, shape = {10240, 2560}
INFO:hf-to-gguf:blk.24.ffn_gate.weight,            torch.bfloat16 --> BF16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.24.ffn_up.weight,              torch.bfloat16 --> BF16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.24.inp_gate.weight,            torch.bfloat16 --> BF16, shape = {2560, 256}
INFO:hf-to-gguf:blk.24.proj.weight,                torch.bfloat16 --> BF16, shape = {256, 2560}
INFO:hf-to-gguf:blk.24.post_attention_norm.weight, torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.24.post_ffw_norm.weight,       torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.24.post_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.24.ffn_norm.weight,            torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.24.attn_k_norm.weight,         torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.24.attn_k.weight,              torch.bfloat16 --> BF16, shape = {2560, 512}
INFO:hf-to-gguf:blk.24.attn_output.weight,         torch.bfloat16 --> BF16, shape = {2048, 2560}
INFO:hf-to-gguf:blk.24.attn_q_norm.weight,         torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.24.attn_q.weight,              torch.bfloat16 --> BF16, shape = {2560, 2048}
INFO:hf-to-gguf:blk.24.attn_v.weight,              torch.bfloat16 --> BF16, shape = {2560, 512}
INFO:hf-to-gguf:blk.25.attn_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.25.layer_output_scale.weight,  torch.bfloat16 --> F32, shape = {1}
INFO:hf-to-gguf:blk.25.ffn_down.weight,            torch.bfloat16 --> BF16, shape = {10240, 2560}
INFO:hf-to-gguf:blk.25.ffn_gate.weight,            torch.bfloat16 --> BF16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.25.ffn_up.weight,              torch.bfloat16 --> BF16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.25.inp_gate.weight,            torch.bfloat16 --> BF16, shape = {2560, 256}
INFO:hf-to-gguf:blk.25.proj.weight,                torch.bfloat16 --> BF16, shape = {256, 2560}
INFO:hf-to-gguf:blk.25.post_attention_norm.weight, torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.25.post_ffw_norm.weight,       torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.25.post_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.25.ffn_norm.weight,            torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.25.attn_k_norm.weight,         torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.25.attn_k.weight,              torch.bfloat16 --> BF16, shape = {2560, 512}
INFO:hf-to-gguf:blk.25.attn_output.weight,         torch.bfloat16 --> BF16, shape = {2048, 2560}
INFO:hf-to-gguf:blk.25.attn_q_norm.weight,         torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.25.attn_q.weight,              torch.bfloat16 --> BF16, shape = {2560, 2048}
INFO:hf-to-gguf:blk.25.attn_v.weight,              torch.bfloat16 --> BF16, shape = {2560, 512}
INFO:hf-to-gguf:blk.26.attn_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.26.layer_output_scale.weight,  torch.bfloat16 --> F32, shape = {1}
INFO:hf-to-gguf:blk.26.ffn_down.weight,            torch.bfloat16 --> BF16, shape = {10240, 2560}
INFO:hf-to-gguf:blk.26.ffn_gate.weight,            torch.bfloat16 --> BF16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.26.ffn_up.weight,              torch.bfloat16 --> BF16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.26.inp_gate.weight,            torch.bfloat16 --> BF16, shape = {2560, 256}
INFO:hf-to-gguf:blk.26.proj.weight,                torch.bfloat16 --> BF16, shape = {256, 2560}
INFO:hf-to-gguf:blk.26.post_attention_norm.weight, torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.26.post_ffw_norm.weight,       torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.26.post_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.26.ffn_norm.weight,            torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.26.attn_k_norm.weight,         torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.26.attn_k.weight,              torch.bfloat16 --> BF16, shape = {2560, 512}
INFO:hf-to-gguf:blk.26.attn_output.weight,         torch.bfloat16 --> BF16, shape = {2048, 2560}
INFO:hf-to-gguf:blk.26.attn_q_norm.weight,         torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.26.attn_q.weight,              torch.bfloat16 --> BF16, shape = {2560, 2048}
INFO:hf-to-gguf:blk.26.attn_v.weight,              torch.bfloat16 --> BF16, shape = {2560, 512}
INFO:hf-to-gguf:blk.27.attn_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.27.layer_output_scale.weight,  torch.bfloat16 --> F32, shape = {1}
INFO:hf-to-gguf:blk.27.ffn_down.weight,            torch.bfloat16 --> BF16, shape = {10240, 2560}
INFO:hf-to-gguf:blk.27.ffn_gate.weight,            torch.bfloat16 --> BF16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.27.ffn_up.weight,              torch.bfloat16 --> BF16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.27.inp_gate.weight,            torch.bfloat16 --> BF16, shape = {2560, 256}
INFO:hf-to-gguf:blk.27.proj.weight,                torch.bfloat16 --> BF16, shape = {256, 2560}
INFO:hf-to-gguf:blk.27.post_attention_norm.weight, torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.27.post_ffw_norm.weight,       torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.27.post_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.27.ffn_norm.weight,            torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.27.attn_k_norm.weight,         torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.27.attn_k.weight,              torch.bfloat16 --> BF16, shape = {2560, 512}
INFO:hf-to-gguf:blk.27.attn_output.weight,         torch.bfloat16 --> BF16, shape = {2048, 2560}
INFO:hf-to-gguf:blk.27.attn_q_norm.weight,         torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.27.attn_q.weight,              torch.bfloat16 --> BF16, shape = {2560, 2048}
INFO:hf-to-gguf:blk.27.attn_v.weight,              torch.bfloat16 --> BF16, shape = {2560, 512}
INFO:hf-to-gguf:blk.28.attn_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.28.layer_output_scale.weight,  torch.bfloat16 --> F32, shape = {1}
INFO:hf-to-gguf:blk.28.ffn_down.weight,            torch.bfloat16 --> BF16, shape = {10240, 2560}
INFO:hf-to-gguf:blk.28.ffn_gate.weight,            torch.bfloat16 --> BF16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.28.ffn_up.weight,              torch.bfloat16 --> BF16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.28.inp_gate.weight,            torch.bfloat16 --> BF16, shape = {2560, 256}
INFO:hf-to-gguf:blk.28.proj.weight,                torch.bfloat16 --> BF16, shape = {256, 2560}
INFO:hf-to-gguf:blk.28.post_attention_norm.weight, torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.28.post_ffw_norm.weight,       torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.28.post_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.28.ffn_norm.weight,            torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.28.attn_k_norm.weight,         torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.28.attn_k.weight,              torch.bfloat16 --> BF16, shape = {2560, 512}
INFO:hf-to-gguf:blk.28.attn_output.weight,         torch.bfloat16 --> BF16, shape = {2048, 2560}
INFO:hf-to-gguf:blk.28.attn_q_norm.weight,         torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.28.attn_q.weight,              torch.bfloat16 --> BF16, shape = {2560, 2048}
INFO:hf-to-gguf:blk.28.attn_v.weight,              torch.bfloat16 --> BF16, shape = {2560, 512}
INFO:hf-to-gguf:blk.29.attn_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.29.layer_output_scale.weight,  torch.bfloat16 --> F32, shape = {1}
INFO:hf-to-gguf:blk.29.ffn_down.weight,            torch.bfloat16 --> BF16, shape = {10240, 2560}
INFO:hf-to-gguf:blk.29.ffn_gate.weight,            torch.bfloat16 --> BF16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.29.ffn_up.weight,              torch.bfloat16 --> BF16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.29.inp_gate.weight,            torch.bfloat16 --> BF16, shape = {2560, 256}
INFO:hf-to-gguf:blk.29.proj.weight,                torch.bfloat16 --> BF16, shape = {256, 2560}
INFO:hf-to-gguf:blk.29.post_attention_norm.weight, torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.29.post_ffw_norm.weight,       torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.29.post_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.29.ffn_norm.weight,            torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.29.attn_k_norm.weight,         torch.bfloat16 --> F32, shape = {512}
INFO:hf-to-gguf:blk.29.attn_k.weight,              torch.bfloat16 --> BF16, shape = {2560, 1024}
INFO:hf-to-gguf:blk.29.attn_output.weight,         torch.bfloat16 --> BF16, shape = {4096, 2560}
INFO:hf-to-gguf:blk.29.attn_q_norm.weight,         torch.bfloat16 --> F32, shape = {512}
INFO:hf-to-gguf:blk.29.attn_q.weight,              torch.bfloat16 --> BF16, shape = {2560, 4096}
INFO:hf-to-gguf:blk.29.attn_v.weight,              torch.bfloat16 --> BF16, shape = {2560, 1024}
INFO:hf-to-gguf:blk.3.attn_norm.weight,            torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.3.layer_output_scale.weight,   torch.bfloat16 --> F32, shape = {1}
INFO:hf-to-gguf:blk.3.ffn_down.weight,             torch.bfloat16 --> BF16, shape = {10240, 2560}
INFO:hf-to-gguf:blk.3.ffn_gate.weight,             torch.bfloat16 --> BF16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.3.ffn_up.weight,               torch.bfloat16 --> BF16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.3.inp_gate.weight,             torch.bfloat16 --> BF16, shape = {2560, 256}
INFO:hf-to-gguf:blk.3.proj.weight,                 torch.bfloat16 --> BF16, shape = {256, 2560}
INFO:hf-to-gguf:blk.3.post_attention_norm.weight,  torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.3.post_ffw_norm.weight,        torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.3.post_norm.weight,            torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.3.ffn_norm.weight,             torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.3.attn_k_norm.weight,          torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.3.attn_k.weight,               torch.bfloat16 --> BF16, shape = {2560, 512}
INFO:hf-to-gguf:blk.3.attn_output.weight,          torch.bfloat16 --> BF16, shape = {2048, 2560}
INFO:hf-to-gguf:blk.3.attn_q_norm.weight,          torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.3.attn_q.weight,               torch.bfloat16 --> BF16, shape = {2560, 2048}
INFO:hf-to-gguf:blk.3.attn_v.weight,               torch.bfloat16 --> BF16, shape = {2560, 512}
INFO:hf-to-gguf:blk.30.attn_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.30.layer_output_scale.weight,  torch.bfloat16 --> F32, shape = {1}
INFO:hf-to-gguf:blk.30.ffn_down.weight,            torch.bfloat16 --> BF16, shape = {10240, 2560}
INFO:hf-to-gguf:blk.30.ffn_gate.weight,            torch.bfloat16 --> BF16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.30.ffn_up.weight,              torch.bfloat16 --> BF16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.30.inp_gate.weight,            torch.bfloat16 --> BF16, shape = {2560, 256}
INFO:hf-to-gguf:blk.30.proj.weight,                torch.bfloat16 --> BF16, shape = {256, 2560}
INFO:hf-to-gguf:blk.30.post_attention_norm.weight, torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.30.post_ffw_norm.weight,       torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.30.post_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.30.ffn_norm.weight,            torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.30.attn_k_norm.weight,         torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.30.attn_k.weight,              torch.bfloat16 --> BF16, shape = {2560, 512}
INFO:hf-to-gguf:blk.30.attn_output.weight,         torch.bfloat16 --> BF16, shape = {2048, 2560}
INFO:hf-to-gguf:blk.30.attn_q_norm.weight,         torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.30.attn_q.weight,              torch.bfloat16 --> BF16, shape = {2560, 2048}
INFO:hf-to-gguf:blk.30.attn_v.weight,              torch.bfloat16 --> BF16, shape = {2560, 512}
INFO:hf-to-gguf:blk.31.attn_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.31.layer_output_scale.weight,  torch.bfloat16 --> F32, shape = {1}
INFO:hf-to-gguf:blk.31.ffn_down.weight,            torch.bfloat16 --> BF16, shape = {10240, 2560}
INFO:hf-to-gguf:blk.31.ffn_gate.weight,            torch.bfloat16 --> BF16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.31.ffn_up.weight,              torch.bfloat16 --> BF16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.31.inp_gate.weight,            torch.bfloat16 --> BF16, shape = {2560, 256}
INFO:hf-to-gguf:blk.31.proj.weight,                torch.bfloat16 --> BF16, shape = {256, 2560}
INFO:hf-to-gguf:blk.31.post_attention_norm.weight, torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.31.post_ffw_norm.weight,       torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.31.post_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.31.ffn_norm.weight,            torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.31.attn_k_norm.weight,         torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.31.attn_k.weight,              torch.bfloat16 --> BF16, shape = {2560, 512}
INFO:hf-to-gguf:blk.31.attn_output.weight,         torch.bfloat16 --> BF16, shape = {2048, 2560}
INFO:hf-to-gguf:blk.31.attn_q_norm.weight,         torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.31.attn_q.weight,              torch.bfloat16 --> BF16, shape = {2560, 2048}
INFO:hf-to-gguf:blk.31.attn_v.weight,              torch.bfloat16 --> BF16, shape = {2560, 512}
INFO:hf-to-gguf:blk.32.attn_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.32.layer_output_scale.weight,  torch.bfloat16 --> F32, shape = {1}
INFO:hf-to-gguf:blk.32.ffn_down.weight,            torch.bfloat16 --> BF16, shape = {10240, 2560}
INFO:hf-to-gguf:blk.32.ffn_gate.weight,            torch.bfloat16 --> BF16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.32.ffn_up.weight,              torch.bfloat16 --> BF16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.32.inp_gate.weight,            torch.bfloat16 --> BF16, shape = {2560, 256}
INFO:hf-to-gguf:blk.32.proj.weight,                torch.bfloat16 --> BF16, shape = {256, 2560}
INFO:hf-to-gguf:blk.32.post_attention_norm.weight, torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.32.post_ffw_norm.weight,       torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.32.post_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.32.ffn_norm.weight,            torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.32.attn_k_norm.weight,         torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.32.attn_k.weight,              torch.bfloat16 --> BF16, shape = {2560, 512}
INFO:hf-to-gguf:blk.32.attn_output.weight,         torch.bfloat16 --> BF16, shape = {2048, 2560}
INFO:hf-to-gguf:blk.32.attn_q_norm.weight,         torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.32.attn_q.weight,              torch.bfloat16 --> BF16, shape = {2560, 2048}
INFO:hf-to-gguf:blk.32.attn_v.weight,              torch.bfloat16 --> BF16, shape = {2560, 512}
INFO:hf-to-gguf:blk.33.attn_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.33.layer_output_scale.weight,  torch.bfloat16 --> F32, shape = {1}
INFO:hf-to-gguf:blk.33.ffn_down.weight,            torch.bfloat16 --> BF16, shape = {10240, 2560}
INFO:hf-to-gguf:blk.33.ffn_gate.weight,            torch.bfloat16 --> BF16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.33.ffn_up.weight,              torch.bfloat16 --> BF16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.33.inp_gate.weight,            torch.bfloat16 --> BF16, shape = {2560, 256}
INFO:hf-to-gguf:blk.33.proj.weight,                torch.bfloat16 --> BF16, shape = {256, 2560}
INFO:hf-to-gguf:blk.33.post_attention_norm.weight, torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.33.post_ffw_norm.weight,       torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.33.post_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.33.ffn_norm.weight,            torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.33.attn_k_norm.weight,         torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.33.attn_k.weight,              torch.bfloat16 --> BF16, shape = {2560, 512}
INFO:hf-to-gguf:blk.33.attn_output.weight,         torch.bfloat16 --> BF16, shape = {2048, 2560}
INFO:hf-to-gguf:blk.33.attn_q_norm.weight,         torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.33.attn_q.weight,              torch.bfloat16 --> BF16, shape = {2560, 2048}
INFO:hf-to-gguf:blk.33.attn_v.weight,              torch.bfloat16 --> BF16, shape = {2560, 512}
INFO:hf-to-gguf:blk.34.attn_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.34.layer_output_scale.weight,  torch.bfloat16 --> F32, shape = {1}
INFO:hf-to-gguf:blk.34.ffn_down.weight,            torch.bfloat16 --> BF16, shape = {10240, 2560}
INFO:hf-to-gguf:blk.34.ffn_gate.weight,            torch.bfloat16 --> BF16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.34.ffn_up.weight,              torch.bfloat16 --> BF16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.34.inp_gate.weight,            torch.bfloat16 --> BF16, shape = {2560, 256}
INFO:hf-to-gguf:blk.34.proj.weight,                torch.bfloat16 --> BF16, shape = {256, 2560}
INFO:hf-to-gguf:blk.34.post_attention_norm.weight, torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.34.post_ffw_norm.weight,       torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.34.post_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.34.ffn_norm.weight,            torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.34.attn_k_norm.weight,         torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.34.attn_k.weight,              torch.bfloat16 --> BF16, shape = {2560, 512}
INFO:hf-to-gguf:blk.34.attn_output.weight,         torch.bfloat16 --> BF16, shape = {2048, 2560}
INFO:hf-to-gguf:blk.34.attn_q_norm.weight,         torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.34.attn_q.weight,              torch.bfloat16 --> BF16, shape = {2560, 2048}
INFO:hf-to-gguf:blk.34.attn_v.weight,              torch.bfloat16 --> BF16, shape = {2560, 512}
INFO:hf-to-gguf:blk.35.attn_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.35.layer_output_scale.weight,  torch.bfloat16 --> F32, shape = {1}
INFO:hf-to-gguf:blk.35.ffn_down.weight,            torch.bfloat16 --> BF16, shape = {10240, 2560}
INFO:hf-to-gguf:blk.35.ffn_gate.weight,            torch.bfloat16 --> BF16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.35.ffn_up.weight,              torch.bfloat16 --> BF16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.35.inp_gate.weight,            torch.bfloat16 --> BF16, shape = {2560, 256}
INFO:hf-to-gguf:blk.35.proj.weight,                torch.bfloat16 --> BF16, shape = {256, 2560}
INFO:hf-to-gguf:blk.35.post_attention_norm.weight, torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.35.post_ffw_norm.weight,       torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.35.post_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.35.ffn_norm.weight,            torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.35.attn_k_norm.weight,         torch.bfloat16 --> F32, shape = {512}
INFO:hf-to-gguf:blk.35.attn_k.weight,              torch.bfloat16 --> BF16, shape = {2560, 1024}
INFO:hf-to-gguf:blk.35.attn_output.weight,         torch.bfloat16 --> BF16, shape = {4096, 2560}
INFO:hf-to-gguf:blk.35.attn_q_norm.weight,         torch.bfloat16 --> F32, shape = {512}
INFO:hf-to-gguf:blk.35.attn_q.weight,              torch.bfloat16 --> BF16, shape = {2560, 4096}
INFO:hf-to-gguf:blk.35.attn_v.weight,              torch.bfloat16 --> BF16, shape = {2560, 1024}
INFO:hf-to-gguf:blk.36.attn_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.36.layer_output_scale.weight,  torch.bfloat16 --> F32, shape = {1}
INFO:hf-to-gguf:blk.36.ffn_down.weight,            torch.bfloat16 --> BF16, shape = {10240, 2560}
INFO:hf-to-gguf:blk.36.ffn_gate.weight,            torch.bfloat16 --> BF16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.36.ffn_up.weight,              torch.bfloat16 --> BF16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.36.inp_gate.weight,            torch.bfloat16 --> BF16, shape = {2560, 256}
INFO:hf-to-gguf:blk.36.proj.weight,                torch.bfloat16 --> BF16, shape = {256, 2560}
INFO:hf-to-gguf:blk.36.post_attention_norm.weight, torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.36.post_ffw_norm.weight,       torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.36.post_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.36.ffn_norm.weight,            torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.36.attn_k_norm.weight,         torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.36.attn_k.weight,              torch.bfloat16 --> BF16, shape = {2560, 512}
INFO:hf-to-gguf:blk.36.attn_output.weight,         torch.bfloat16 --> BF16, shape = {2048, 2560}
INFO:hf-to-gguf:blk.36.attn_q_norm.weight,         torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.36.attn_q.weight,              torch.bfloat16 --> BF16, shape = {2560, 2048}
INFO:hf-to-gguf:blk.36.attn_v.weight,              torch.bfloat16 --> BF16, shape = {2560, 512}
INFO:hf-to-gguf:blk.37.attn_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.37.layer_output_scale.weight,  torch.bfloat16 --> F32, shape = {1}
INFO:hf-to-gguf:blk.37.ffn_down.weight,            torch.bfloat16 --> BF16, shape = {10240, 2560}
INFO:hf-to-gguf:blk.37.ffn_gate.weight,            torch.bfloat16 --> BF16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.37.ffn_up.weight,              torch.bfloat16 --> BF16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.37.inp_gate.weight,            torch.bfloat16 --> BF16, shape = {2560, 256}
INFO:hf-to-gguf:blk.37.proj.weight,                torch.bfloat16 --> BF16, shape = {256, 2560}
INFO:hf-to-gguf:blk.37.post_attention_norm.weight, torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.37.post_ffw_norm.weight,       torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.37.post_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.37.ffn_norm.weight,            torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.37.attn_k_norm.weight,         torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.37.attn_k.weight,              torch.bfloat16 --> BF16, shape = {2560, 512}
INFO:hf-to-gguf:blk.37.attn_output.weight,         torch.bfloat16 --> BF16, shape = {2048, 2560}
INFO:hf-to-gguf:blk.37.attn_q_norm.weight,         torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.37.attn_q.weight,              torch.bfloat16 --> BF16, shape = {2560, 2048}
INFO:hf-to-gguf:blk.37.attn_v.weight,              torch.bfloat16 --> BF16, shape = {2560, 512}
INFO:hf-to-gguf:blk.38.attn_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.38.layer_output_scale.weight,  torch.bfloat16 --> F32, shape = {1}
INFO:hf-to-gguf:blk.38.ffn_down.weight,            torch.bfloat16 --> BF16, shape = {10240, 2560}
INFO:hf-to-gguf:blk.38.ffn_gate.weight,            torch.bfloat16 --> BF16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.38.ffn_up.weight,              torch.bfloat16 --> BF16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.38.inp_gate.weight,            torch.bfloat16 --> BF16, shape = {2560, 256}
INFO:hf-to-gguf:blk.38.proj.weight,                torch.bfloat16 --> BF16, shape = {256, 2560}
INFO:hf-to-gguf:blk.38.post_attention_norm.weight, torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.38.post_ffw_norm.weight,       torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.38.post_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.38.ffn_norm.weight,            torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.38.attn_k_norm.weight,         torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.38.attn_k.weight,              torch.bfloat16 --> BF16, shape = {2560, 512}
INFO:hf-to-gguf:blk.38.attn_output.weight,         torch.bfloat16 --> BF16, shape = {2048, 2560}
INFO:hf-to-gguf:blk.38.attn_q_norm.weight,         torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.38.attn_q.weight,              torch.bfloat16 --> BF16, shape = {2560, 2048}
INFO:hf-to-gguf:blk.38.attn_v.weight,              torch.bfloat16 --> BF16, shape = {2560, 512}
INFO:hf-to-gguf:blk.39.attn_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.39.layer_output_scale.weight,  torch.bfloat16 --> F32, shape = {1}
INFO:hf-to-gguf:blk.39.ffn_down.weight,            torch.bfloat16 --> BF16, shape = {10240, 2560}
INFO:hf-to-gguf:blk.39.ffn_gate.weight,            torch.bfloat16 --> BF16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.39.ffn_up.weight,              torch.bfloat16 --> BF16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.39.inp_gate.weight,            torch.bfloat16 --> BF16, shape = {2560, 256}
INFO:hf-to-gguf:blk.39.proj.weight,                torch.bfloat16 --> BF16, shape = {256, 2560}
INFO:hf-to-gguf:blk.39.post_attention_norm.weight, torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.39.post_ffw_norm.weight,       torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.39.post_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.39.ffn_norm.weight,            torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.39.attn_k_norm.weight,         torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.39.attn_k.weight,              torch.bfloat16 --> BF16, shape = {2560, 512}
INFO:hf-to-gguf:blk.39.attn_output.weight,         torch.bfloat16 --> BF16, shape = {2048, 2560}
INFO:hf-to-gguf:blk.39.attn_q_norm.weight,         torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.39.attn_q.weight,              torch.bfloat16 --> BF16, shape = {2560, 2048}
INFO:hf-to-gguf:blk.39.attn_v.weight,              torch.bfloat16 --> BF16, shape = {2560, 512}
INFO:hf-to-gguf:blk.4.attn_norm.weight,            torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.4.layer_output_scale.weight,   torch.bfloat16 --> F32, shape = {1}
INFO:hf-to-gguf:blk.4.ffn_down.weight,             torch.bfloat16 --> BF16, shape = {10240, 2560}
INFO:hf-to-gguf:blk.4.ffn_gate.weight,             torch.bfloat16 --> BF16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.4.ffn_up.weight,               torch.bfloat16 --> BF16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.4.inp_gate.weight,             torch.bfloat16 --> BF16, shape = {2560, 256}
INFO:hf-to-gguf:blk.4.proj.weight,                 torch.bfloat16 --> BF16, shape = {256, 2560}
INFO:hf-to-gguf:blk.4.post_attention_norm.weight,  torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.4.post_ffw_norm.weight,        torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.4.post_norm.weight,            torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.4.ffn_norm.weight,             torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.4.attn_k_norm.weight,          torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.4.attn_k.weight,               torch.bfloat16 --> BF16, shape = {2560, 512}
INFO:hf-to-gguf:blk.4.attn_output.weight,          torch.bfloat16 --> BF16, shape = {2048, 2560}
INFO:hf-to-gguf:blk.4.attn_q_norm.weight,          torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.4.attn_q.weight,               torch.bfloat16 --> BF16, shape = {2560, 2048}
INFO:hf-to-gguf:blk.4.attn_v.weight,               torch.bfloat16 --> BF16, shape = {2560, 512}
INFO:hf-to-gguf:blk.40.attn_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.40.layer_output_scale.weight,  torch.bfloat16 --> F32, shape = {1}
INFO:hf-to-gguf:blk.40.ffn_down.weight,            torch.bfloat16 --> BF16, shape = {10240, 2560}
INFO:hf-to-gguf:blk.40.ffn_gate.weight,            torch.bfloat16 --> BF16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.40.ffn_up.weight,              torch.bfloat16 --> BF16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.40.inp_gate.weight,            torch.bfloat16 --> BF16, shape = {2560, 256}
INFO:hf-to-gguf:blk.40.proj.weight,                torch.bfloat16 --> BF16, shape = {256, 2560}
INFO:hf-to-gguf:blk.40.post_attention_norm.weight, torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.40.post_ffw_norm.weight,       torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.40.post_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.40.ffn_norm.weight,            torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.40.attn_k_norm.weight,         torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.40.attn_k.weight,              torch.bfloat16 --> BF16, shape = {2560, 512}
INFO:hf-to-gguf:blk.40.attn_output.weight,         torch.bfloat16 --> BF16, shape = {2048, 2560}
INFO:hf-to-gguf:blk.40.attn_q_norm.weight,         torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.40.attn_q.weight,              torch.bfloat16 --> BF16, shape = {2560, 2048}
INFO:hf-to-gguf:blk.40.attn_v.weight,              torch.bfloat16 --> BF16, shape = {2560, 512}
INFO:hf-to-gguf:blk.41.attn_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.41.layer_output_scale.weight,  torch.bfloat16 --> F32, shape = {1}
INFO:hf-to-gguf:blk.41.ffn_down.weight,            torch.bfloat16 --> BF16, shape = {10240, 2560}
INFO:hf-to-gguf:blk.41.ffn_gate.weight,            torch.bfloat16 --> BF16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.41.ffn_up.weight,              torch.bfloat16 --> BF16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.41.inp_gate.weight,            torch.bfloat16 --> BF16, shape = {2560, 256}
INFO:hf-to-gguf:blk.41.proj.weight,                torch.bfloat16 --> BF16, shape = {256, 2560}
INFO:hf-to-gguf:blk.41.post_attention_norm.weight, torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.41.post_ffw_norm.weight,       torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.41.post_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.41.ffn_norm.weight,            torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.41.attn_k_norm.weight,         torch.bfloat16 --> F32, shape = {512}
INFO:hf-to-gguf:blk.41.attn_k.weight,              torch.bfloat16 --> BF16, shape = {2560, 1024}
INFO:hf-to-gguf:blk.41.attn_output.weight,         torch.bfloat16 --> BF16, shape = {4096, 2560}
INFO:hf-to-gguf:blk.41.attn_q_norm.weight,         torch.bfloat16 --> F32, shape = {512}
INFO:hf-to-gguf:blk.41.attn_q.weight,              torch.bfloat16 --> BF16, shape = {2560, 4096}
INFO:hf-to-gguf:blk.41.attn_v.weight,              torch.bfloat16 --> BF16, shape = {2560, 1024}
INFO:hf-to-gguf:blk.5.attn_norm.weight,            torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.5.layer_output_scale.weight,   torch.bfloat16 --> F32, shape = {1}
INFO:hf-to-gguf:blk.5.ffn_down.weight,             torch.bfloat16 --> BF16, shape = {10240, 2560}
INFO:hf-to-gguf:blk.5.ffn_gate.weight,             torch.bfloat16 --> BF16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.5.ffn_up.weight,               torch.bfloat16 --> BF16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.5.inp_gate.weight,             torch.bfloat16 --> BF16, shape = {2560, 256}
INFO:hf-to-gguf:blk.5.proj.weight,                 torch.bfloat16 --> BF16, shape = {256, 2560}
INFO:hf-to-gguf:blk.5.post_attention_norm.weight,  torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.5.post_ffw_norm.weight,        torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.5.post_norm.weight,            torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.5.ffn_norm.weight,             torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.5.attn_k_norm.weight,          torch.bfloat16 --> F32, shape = {512}
INFO:hf-to-gguf:blk.5.attn_k.weight,               torch.bfloat16 --> BF16, shape = {2560, 1024}
INFO:hf-to-gguf:blk.5.attn_output.weight,          torch.bfloat16 --> BF16, shape = {4096, 2560}
INFO:hf-to-gguf:blk.5.attn_q_norm.weight,          torch.bfloat16 --> F32, shape = {512}
INFO:hf-to-gguf:blk.5.attn_q.weight,               torch.bfloat16 --> BF16, shape = {2560, 4096}
INFO:hf-to-gguf:blk.5.attn_v.weight,               torch.bfloat16 --> BF16, shape = {2560, 1024}
INFO:hf-to-gguf:blk.6.attn_norm.weight,            torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.6.layer_output_scale.weight,   torch.bfloat16 --> F32, shape = {1}
INFO:hf-to-gguf:blk.6.ffn_down.weight,             torch.bfloat16 --> BF16, shape = {10240, 2560}
INFO:hf-to-gguf:blk.6.ffn_gate.weight,             torch.bfloat16 --> BF16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.6.ffn_up.weight,               torch.bfloat16 --> BF16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.6.inp_gate.weight,             torch.bfloat16 --> BF16, shape = {2560, 256}
INFO:hf-to-gguf:blk.6.proj.weight,                 torch.bfloat16 --> BF16, shape = {256, 2560}
INFO:hf-to-gguf:blk.6.post_attention_norm.weight,  torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.6.post_ffw_norm.weight,        torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.6.post_norm.weight,            torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.6.ffn_norm.weight,             torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.6.attn_k_norm.weight,          torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.6.attn_k.weight,               torch.bfloat16 --> BF16, shape = {2560, 512}
INFO:hf-to-gguf:blk.6.attn_output.weight,          torch.bfloat16 --> BF16, shape = {2048, 2560}
INFO:hf-to-gguf:blk.6.attn_q_norm.weight,          torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.6.attn_q.weight,               torch.bfloat16 --> BF16, shape = {2560, 2048}
INFO:hf-to-gguf:blk.6.attn_v.weight,               torch.bfloat16 --> BF16, shape = {2560, 512}
INFO:hf-to-gguf:blk.7.attn_norm.weight,            torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.7.layer_output_scale.weight,   torch.bfloat16 --> F32, shape = {1}
INFO:hf-to-gguf:blk.7.ffn_down.weight,             torch.bfloat16 --> BF16, shape = {10240, 2560}
INFO:hf-to-gguf:blk.7.ffn_gate.weight,             torch.bfloat16 --> BF16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.7.ffn_up.weight,               torch.bfloat16 --> BF16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.7.inp_gate.weight,             torch.bfloat16 --> BF16, shape = {2560, 256}
INFO:hf-to-gguf:blk.7.proj.weight,                 torch.bfloat16 --> BF16, shape = {256, 2560}
INFO:hf-to-gguf:blk.7.post_attention_norm.weight,  torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.7.post_ffw_norm.weight,        torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.7.post_norm.weight,            torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.7.ffn_norm.weight,             torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.7.attn_k_norm.weight,          torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.7.attn_k.weight,               torch.bfloat16 --> BF16, shape = {2560, 512}
INFO:hf-to-gguf:blk.7.attn_output.weight,          torch.bfloat16 --> BF16, shape = {2048, 2560}
INFO:hf-to-gguf:blk.7.attn_q_norm.weight,          torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.7.attn_q.weight,               torch.bfloat16 --> BF16, shape = {2560, 2048}
INFO:hf-to-gguf:blk.7.attn_v.weight,               torch.bfloat16 --> BF16, shape = {2560, 512}
INFO:hf-to-gguf:blk.8.attn_norm.weight,            torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.8.layer_output_scale.weight,   torch.bfloat16 --> F32, shape = {1}
INFO:hf-to-gguf:blk.8.ffn_down.weight,             torch.bfloat16 --> BF16, shape = {10240, 2560}
INFO:hf-to-gguf:blk.8.ffn_gate.weight,             torch.bfloat16 --> BF16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.8.ffn_up.weight,               torch.bfloat16 --> BF16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.8.inp_gate.weight,             torch.bfloat16 --> BF16, shape = {2560, 256}
INFO:hf-to-gguf:blk.8.proj.weight,                 torch.bfloat16 --> BF16, shape = {256, 2560}
INFO:hf-to-gguf:blk.8.post_attention_norm.weight,  torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.8.post_ffw_norm.weight,        torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.8.post_norm.weight,            torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.8.ffn_norm.weight,             torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.8.attn_k_norm.weight,          torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.8.attn_k.weight,               torch.bfloat16 --> BF16, shape = {2560, 512}
INFO:hf-to-gguf:blk.8.attn_output.weight,          torch.bfloat16 --> BF16, shape = {2048, 2560}
INFO:hf-to-gguf:blk.8.attn_q_norm.weight,          torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.8.attn_q.weight,               torch.bfloat16 --> BF16, shape = {2560, 2048}
INFO:hf-to-gguf:blk.8.attn_v.weight,               torch.bfloat16 --> BF16, shape = {2560, 512}
INFO:hf-to-gguf:blk.9.attn_norm.weight,            torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.9.layer_output_scale.weight,   torch.bfloat16 --> F32, shape = {1}
INFO:hf-to-gguf:blk.9.ffn_down.weight,             torch.bfloat16 --> BF16, shape = {10240, 2560}
INFO:hf-to-gguf:blk.9.ffn_gate.weight,             torch.bfloat16 --> BF16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.9.ffn_up.weight,               torch.bfloat16 --> BF16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.9.inp_gate.weight,             torch.bfloat16 --> BF16, shape = {2560, 256}
INFO:hf-to-gguf:blk.9.proj.weight,                 torch.bfloat16 --> BF16, shape = {256, 2560}
INFO:hf-to-gguf:blk.9.post_attention_norm.weight,  torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.9.post_ffw_norm.weight,        torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.9.post_norm.weight,            torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.9.ffn_norm.weight,             torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.9.attn_k_norm.weight,          torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.9.attn_k.weight,               torch.bfloat16 --> BF16, shape = {2560, 512}
INFO:hf-to-gguf:blk.9.attn_output.weight,          torch.bfloat16 --> BF16, shape = {2048, 2560}
INFO:hf-to-gguf:blk.9.attn_q_norm.weight,          torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.9.attn_q.weight,               torch.bfloat16 --> BF16, shape = {2560, 2048}
INFO:hf-to-gguf:blk.9.attn_v.weight,               torch.bfloat16 --> BF16, shape = {2560, 512}
INFO:hf-to-gguf:output_norm.weight,                torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:per_layer_model_proj.weight,       torch.bfloat16 --> BF16, shape = {2560, 10752}
INFO:hf-to-gguf:per_layer_proj_norm.weight,        torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:Set meta model
INFO:hf-to-gguf:Set model parameters
INFO:hf-to-gguf:gguf: context length = 131072
INFO:hf-to-gguf:gguf: embedding length = 2560
INFO:hf-to-gguf:gguf: feed forward length = 10240
INFO:hf-to-gguf:gguf: head count = 8
INFO:hf-to-gguf:gguf: key-value head count = 2
WARNING:hf-to-gguf:Unknown RoPE type: proportional
INFO:hf-to-gguf:gguf: rope scaling type = NONE
INFO:hf-to-gguf:gguf: rope theta = 1000000.0
INFO:hf-to-gguf:gguf: rope theta swa = 10000.0
INFO:hf-to-gguf:gguf: rms norm epsilon = 1e-06
INFO:hf-to-gguf:gguf: file type = 32
WARNING:gguf.gguf_writer:Duplicated key name 'gemma4.context_length', overwriting it with new value 131072 of type UINT32
WARNING:gguf.gguf_writer:Duplicated key name 'gemma4.attention.head_count', overwriting it with new value 8 of type UINT32
WARNING:gguf.gguf_writer:Duplicated key name 'gemma4.attention.layer_norm_rms_epsilon', overwriting it with new value 1e-06 of type FLOAT32
WARNING:gguf.gguf_writer:Duplicated key name 'gemma4.attention.key_length', overwriting it with new value 256 of type UINT32
WARNING:gguf.gguf_writer:Duplicated key name 'gemma4.attention.value_length', overwriting it with new value 256 of type UINT32
WARNING:gguf.gguf_writer:Duplicated key name 'gemma4.rope.freq_base', overwriting it with new value 1000000.0 of type FLOAT32
WARNING:gguf.gguf_writer:Duplicated key name 'gemma4.attention.head_count_kv', overwriting it with new value 2 of type UINT32
WARNING:gguf.gguf_writer:Duplicated key name 'gemma4.attention.key_length', overwriting it with new value 512 of type UINT32
WARNING:gguf.gguf_writer:Duplicated key name 'gemma4.attention.value_length', overwriting it with new value 512 of type UINT32
INFO:hf-to-gguf:Set model quantization version
INFO:hf-to-gguf:Set model tokenizer
INFO:hf-to-gguf:Token '<|tool_call>' is set to USER_DEFINED
INFO:hf-to-gguf:Token '<tool_call|>' is set to USER_DEFINED
INFO:hf-to-gguf:Token '<|tool_response>' is set to USER_DEFINED
INFO:hf-to-gguf:Token '<tool_response|>' is set to USER_DEFINED
INFO:hf-to-gguf:Token '<|"|>' is set to USER_DEFINED
INFO:hf-to-gguf:Token '<|channel>' is set to USER_DEFINED
INFO:hf-to-gguf:Token '<channel|>' is set to USER_DEFINED
WARNING:gguf.vocab:Unknown separator token '<bos>' in TemplateProcessing<pair>
INFO:gguf.vocab:Adding 514906 merge(s).
INFO:gguf.vocab:Setting special token type bos to 2
INFO:gguf.vocab:Setting special token type eos to 1
INFO:gguf.vocab:Setting special token type pad to 0
INFO:gguf.vocab:Setting add_bos_token to True
INFO:gguf.vocab:Setting add_sep_token to False
WARNING:gguf.gguf_writer:Duplicated key name 'tokenizer.ggml.add_bos_token', overwriting it with new value True of type BOOL
INFO:gguf.gguf_writer:Writing the following files:
INFO:gguf.gguf_writer:gguf.gguf: n_tensors = 720, total_size = 15.0G

 

둘 다 머.. 거의 동일한 사이즈로 나왔다. 1기가도 안줄었네

drwxrwxr-x 2 minimonk minimonk        4096  6월  1 13:52 ./
drwxrwxr-x 3 minimonk minimonk        4096  6월  1 13:52 ../
-rw-rw-r-- 1 minimonk minimonk        5105  6월  1 13:51 config.json
-rw-rw-r-- 1 minimonk minimonk 15992595884  6월  1 13:03 model.safetensors
-rw-rw-r-- 1 minimonk minimonk    32170070  6월  1 13:51 tokenizer.json
-rw-rw-r-- 1 minimonk minimonk 15053078208  6월  1 14:01 gguf.gguf

 

그래서 F16 으로 하게 했더니  BF16 이나 F16 이나 용량은 바이트 단위 까지 동일!

-rw-rw-r-- 1 minimonk minimonk 15053078208  6월  1 14:05 gguf.gguf

 

일단 옵션은 outtype 으로 주면된다.

$ /home/minimonk/src/llama.cpp/convert_hf_to_gguf.py ./google/ --outfile gguf.gguf --outtype f16
INFO:hf-to-gguf:Loading model: google
INFO:hf-to-gguf:Model architecture: Gemma4ForConditionalGeneration
INFO:hf-to-gguf:gguf: indexing model part 'model.safetensors'
INFO:gguf.gguf_writer:gguf: This GGUF file is for Little Endian only
INFO:hf-to-gguf:Exporting model...

Writing:   9%|██████▎                                                                | 1.34G/15.0G [00:02<00:27, 503Mbyte/s]

Writing: 100%|███████████████████████████████████████████████████████████████████████| 15.0G/15.0G [00:49<00:00, 305Mbyte/s]
INFO:hf-to-gguf:Model successfully exported to gguf.gguf

 

exporting model.. 과 wrting 사이 로그

더보기

INFO:hf-to-gguf:rope_freqs.weight,                 torch.float32 --> F32, shape = {256}
INFO:hf-to-gguf:token_embd.weight,                 torch.bfloat16 --> F16, shape = {2560, 262144}
INFO:hf-to-gguf:per_layer_token_embd.weight,       torch.bfloat16 --> F16, shape = {10752, 262144}
INFO:hf-to-gguf:blk.0.attn_norm.weight,            torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.0.layer_output_scale.weight,   torch.bfloat16 --> F32, shape = {1}
INFO:hf-to-gguf:blk.0.ffn_down.weight,             torch.bfloat16 --> F16, shape = {10240, 2560}
INFO:hf-to-gguf:blk.0.ffn_gate.weight,             torch.bfloat16 --> F16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.0.ffn_up.weight,               torch.bfloat16 --> F16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.0.inp_gate.weight,             torch.bfloat16 --> F16, shape = {2560, 256}
INFO:hf-to-gguf:blk.0.proj.weight,                 torch.bfloat16 --> F16, shape = {256, 2560}
INFO:hf-to-gguf:blk.0.post_attention_norm.weight,  torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.0.post_ffw_norm.weight,        torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.0.post_norm.weight,            torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.0.ffn_norm.weight,             torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.0.attn_k_norm.weight,          torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.0.attn_k.weight,               torch.bfloat16 --> F16, shape = {2560, 512}
INFO:hf-to-gguf:blk.0.attn_output.weight,          torch.bfloat16 --> F16, shape = {2048, 2560}
INFO:hf-to-gguf:blk.0.attn_q_norm.weight,          torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.0.attn_q.weight,               torch.bfloat16 --> F16, shape = {2560, 2048}
INFO:hf-to-gguf:blk.0.attn_v.weight,               torch.bfloat16 --> F16, shape = {2560, 512}
INFO:hf-to-gguf:blk.1.attn_norm.weight,            torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.1.layer_output_scale.weight,   torch.bfloat16 --> F32, shape = {1}
INFO:hf-to-gguf:blk.1.ffn_down.weight,             torch.bfloat16 --> F16, shape = {10240, 2560}
INFO:hf-to-gguf:blk.1.ffn_gate.weight,             torch.bfloat16 --> F16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.1.ffn_up.weight,               torch.bfloat16 --> F16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.1.inp_gate.weight,             torch.bfloat16 --> F16, shape = {2560, 256}
INFO:hf-to-gguf:blk.1.proj.weight,                 torch.bfloat16 --> F16, shape = {256, 2560}
INFO:hf-to-gguf:blk.1.post_attention_norm.weight,  torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.1.post_ffw_norm.weight,        torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.1.post_norm.weight,            torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.1.ffn_norm.weight,             torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.1.attn_k_norm.weight,          torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.1.attn_k.weight,               torch.bfloat16 --> F16, shape = {2560, 512}
INFO:hf-to-gguf:blk.1.attn_output.weight,          torch.bfloat16 --> F16, shape = {2048, 2560}
INFO:hf-to-gguf:blk.1.attn_q_norm.weight,          torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.1.attn_q.weight,               torch.bfloat16 --> F16, shape = {2560, 2048}
INFO:hf-to-gguf:blk.1.attn_v.weight,               torch.bfloat16 --> F16, shape = {2560, 512}
INFO:hf-to-gguf:blk.10.attn_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.10.layer_output_scale.weight,  torch.bfloat16 --> F32, shape = {1}
INFO:hf-to-gguf:blk.10.ffn_down.weight,            torch.bfloat16 --> F16, shape = {10240, 2560}
INFO:hf-to-gguf:blk.10.ffn_gate.weight,            torch.bfloat16 --> F16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.10.ffn_up.weight,              torch.bfloat16 --> F16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.10.inp_gate.weight,            torch.bfloat16 --> F16, shape = {2560, 256}
INFO:hf-to-gguf:blk.10.proj.weight,                torch.bfloat16 --> F16, shape = {256, 2560}
INFO:hf-to-gguf:blk.10.post_attention_norm.weight, torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.10.post_ffw_norm.weight,       torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.10.post_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.10.ffn_norm.weight,            torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.10.attn_k_norm.weight,         torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.10.attn_k.weight,              torch.bfloat16 --> F16, shape = {2560, 512}
INFO:hf-to-gguf:blk.10.attn_output.weight,         torch.bfloat16 --> F16, shape = {2048, 2560}
INFO:hf-to-gguf:blk.10.attn_q_norm.weight,         torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.10.attn_q.weight,              torch.bfloat16 --> F16, shape = {2560, 2048}
INFO:hf-to-gguf:blk.10.attn_v.weight,              torch.bfloat16 --> F16, shape = {2560, 512}
INFO:hf-to-gguf:blk.11.attn_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.11.layer_output_scale.weight,  torch.bfloat16 --> F32, shape = {1}
INFO:hf-to-gguf:blk.11.ffn_down.weight,            torch.bfloat16 --> F16, shape = {10240, 2560}
INFO:hf-to-gguf:blk.11.ffn_gate.weight,            torch.bfloat16 --> F16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.11.ffn_up.weight,              torch.bfloat16 --> F16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.11.inp_gate.weight,            torch.bfloat16 --> F16, shape = {2560, 256}
INFO:hf-to-gguf:blk.11.proj.weight,                torch.bfloat16 --> F16, shape = {256, 2560}
INFO:hf-to-gguf:blk.11.post_attention_norm.weight, torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.11.post_ffw_norm.weight,       torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.11.post_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.11.ffn_norm.weight,            torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.11.attn_k_norm.weight,         torch.bfloat16 --> F32, shape = {512}
INFO:hf-to-gguf:blk.11.attn_k.weight,              torch.bfloat16 --> F16, shape = {2560, 1024}
INFO:hf-to-gguf:blk.11.attn_output.weight,         torch.bfloat16 --> F16, shape = {4096, 2560}
INFO:hf-to-gguf:blk.11.attn_q_norm.weight,         torch.bfloat16 --> F32, shape = {512}
INFO:hf-to-gguf:blk.11.attn_q.weight,              torch.bfloat16 --> F16, shape = {2560, 4096}
INFO:hf-to-gguf:blk.11.attn_v.weight,              torch.bfloat16 --> F16, shape = {2560, 1024}
INFO:hf-to-gguf:blk.12.attn_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.12.layer_output_scale.weight,  torch.bfloat16 --> F32, shape = {1}
INFO:hf-to-gguf:blk.12.ffn_down.weight,            torch.bfloat16 --> F16, shape = {10240, 2560}
INFO:hf-to-gguf:blk.12.ffn_gate.weight,            torch.bfloat16 --> F16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.12.ffn_up.weight,              torch.bfloat16 --> F16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.12.inp_gate.weight,            torch.bfloat16 --> F16, shape = {2560, 256}
INFO:hf-to-gguf:blk.12.proj.weight,                torch.bfloat16 --> F16, shape = {256, 2560}
INFO:hf-to-gguf:blk.12.post_attention_norm.weight, torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.12.post_ffw_norm.weight,       torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.12.post_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.12.ffn_norm.weight,            torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.12.attn_k_norm.weight,         torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.12.attn_k.weight,              torch.bfloat16 --> F16, shape = {2560, 512}
INFO:hf-to-gguf:blk.12.attn_output.weight,         torch.bfloat16 --> F16, shape = {2048, 2560}
INFO:hf-to-gguf:blk.12.attn_q_norm.weight,         torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.12.attn_q.weight,              torch.bfloat16 --> F16, shape = {2560, 2048}
INFO:hf-to-gguf:blk.12.attn_v.weight,              torch.bfloat16 --> F16, shape = {2560, 512}
INFO:hf-to-gguf:blk.13.attn_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.13.layer_output_scale.weight,  torch.bfloat16 --> F32, shape = {1}
INFO:hf-to-gguf:blk.13.ffn_down.weight,            torch.bfloat16 --> F16, shape = {10240, 2560}
INFO:hf-to-gguf:blk.13.ffn_gate.weight,            torch.bfloat16 --> F16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.13.ffn_up.weight,              torch.bfloat16 --> F16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.13.inp_gate.weight,            torch.bfloat16 --> F16, shape = {2560, 256}
INFO:hf-to-gguf:blk.13.proj.weight,                torch.bfloat16 --> F16, shape = {256, 2560}
INFO:hf-to-gguf:blk.13.post_attention_norm.weight, torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.13.post_ffw_norm.weight,       torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.13.post_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.13.ffn_norm.weight,            torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.13.attn_k_norm.weight,         torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.13.attn_k.weight,              torch.bfloat16 --> F16, shape = {2560, 512}
INFO:hf-to-gguf:blk.13.attn_output.weight,         torch.bfloat16 --> F16, shape = {2048, 2560}
INFO:hf-to-gguf:blk.13.attn_q_norm.weight,         torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.13.attn_q.weight,              torch.bfloat16 --> F16, shape = {2560, 2048}
INFO:hf-to-gguf:blk.13.attn_v.weight,              torch.bfloat16 --> F16, shape = {2560, 512}
INFO:hf-to-gguf:blk.14.attn_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.14.layer_output_scale.weight,  torch.bfloat16 --> F32, shape = {1}
INFO:hf-to-gguf:blk.14.ffn_down.weight,            torch.bfloat16 --> F16, shape = {10240, 2560}
INFO:hf-to-gguf:blk.14.ffn_gate.weight,            torch.bfloat16 --> F16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.14.ffn_up.weight,              torch.bfloat16 --> F16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.14.inp_gate.weight,            torch.bfloat16 --> F16, shape = {2560, 256}
INFO:hf-to-gguf:blk.14.proj.weight,                torch.bfloat16 --> F16, shape = {256, 2560}
INFO:hf-to-gguf:blk.14.post_attention_norm.weight, torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.14.post_ffw_norm.weight,       torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.14.post_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.14.ffn_norm.weight,            torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.14.attn_k_norm.weight,         torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.14.attn_k.weight,              torch.bfloat16 --> F16, shape = {2560, 512}
INFO:hf-to-gguf:blk.14.attn_output.weight,         torch.bfloat16 --> F16, shape = {2048, 2560}
INFO:hf-to-gguf:blk.14.attn_q_norm.weight,         torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.14.attn_q.weight,              torch.bfloat16 --> F16, shape = {2560, 2048}
INFO:hf-to-gguf:blk.14.attn_v.weight,              torch.bfloat16 --> F16, shape = {2560, 512}
INFO:hf-to-gguf:blk.15.attn_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.15.layer_output_scale.weight,  torch.bfloat16 --> F32, shape = {1}
INFO:hf-to-gguf:blk.15.ffn_down.weight,            torch.bfloat16 --> F16, shape = {10240, 2560}
INFO:hf-to-gguf:blk.15.ffn_gate.weight,            torch.bfloat16 --> F16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.15.ffn_up.weight,              torch.bfloat16 --> F16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.15.inp_gate.weight,            torch.bfloat16 --> F16, shape = {2560, 256}
INFO:hf-to-gguf:blk.15.proj.weight,                torch.bfloat16 --> F16, shape = {256, 2560}
INFO:hf-to-gguf:blk.15.post_attention_norm.weight, torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.15.post_ffw_norm.weight,       torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.15.post_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.15.ffn_norm.weight,            torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.15.attn_k_norm.weight,         torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.15.attn_k.weight,              torch.bfloat16 --> F16, shape = {2560, 512}
INFO:hf-to-gguf:blk.15.attn_output.weight,         torch.bfloat16 --> F16, shape = {2048, 2560}
INFO:hf-to-gguf:blk.15.attn_q_norm.weight,         torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.15.attn_q.weight,              torch.bfloat16 --> F16, shape = {2560, 2048}
INFO:hf-to-gguf:blk.15.attn_v.weight,              torch.bfloat16 --> F16, shape = {2560, 512}
INFO:hf-to-gguf:blk.16.attn_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.16.layer_output_scale.weight,  torch.bfloat16 --> F32, shape = {1}
INFO:hf-to-gguf:blk.16.ffn_down.weight,            torch.bfloat16 --> F16, shape = {10240, 2560}
INFO:hf-to-gguf:blk.16.ffn_gate.weight,            torch.bfloat16 --> F16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.16.ffn_up.weight,              torch.bfloat16 --> F16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.16.inp_gate.weight,            torch.bfloat16 --> F16, shape = {2560, 256}
INFO:hf-to-gguf:blk.16.proj.weight,                torch.bfloat16 --> F16, shape = {256, 2560}
INFO:hf-to-gguf:blk.16.post_attention_norm.weight, torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.16.post_ffw_norm.weight,       torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.16.post_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.16.ffn_norm.weight,            torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.16.attn_k_norm.weight,         torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.16.attn_k.weight,              torch.bfloat16 --> F16, shape = {2560, 512}
INFO:hf-to-gguf:blk.16.attn_output.weight,         torch.bfloat16 --> F16, shape = {2048, 2560}
INFO:hf-to-gguf:blk.16.attn_q_norm.weight,         torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.16.attn_q.weight,              torch.bfloat16 --> F16, shape = {2560, 2048}
INFO:hf-to-gguf:blk.16.attn_v.weight,              torch.bfloat16 --> F16, shape = {2560, 512}
INFO:hf-to-gguf:blk.17.attn_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.17.layer_output_scale.weight,  torch.bfloat16 --> F32, shape = {1}
INFO:hf-to-gguf:blk.17.ffn_down.weight,            torch.bfloat16 --> F16, shape = {10240, 2560}
INFO:hf-to-gguf:blk.17.ffn_gate.weight,            torch.bfloat16 --> F16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.17.ffn_up.weight,              torch.bfloat16 --> F16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.17.inp_gate.weight,            torch.bfloat16 --> F16, shape = {2560, 256}
INFO:hf-to-gguf:blk.17.proj.weight,                torch.bfloat16 --> F16, shape = {256, 2560}
INFO:hf-to-gguf:blk.17.post_attention_norm.weight, torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.17.post_ffw_norm.weight,       torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.17.post_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.17.ffn_norm.weight,            torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.17.attn_k_norm.weight,         torch.bfloat16 --> F32, shape = {512}
INFO:hf-to-gguf:blk.17.attn_k.weight,              torch.bfloat16 --> F16, shape = {2560, 1024}
INFO:hf-to-gguf:blk.17.attn_output.weight,         torch.bfloat16 --> F16, shape = {4096, 2560}
INFO:hf-to-gguf:blk.17.attn_q_norm.weight,         torch.bfloat16 --> F32, shape = {512}
INFO:hf-to-gguf:blk.17.attn_q.weight,              torch.bfloat16 --> F16, shape = {2560, 4096}
INFO:hf-to-gguf:blk.17.attn_v.weight,              torch.bfloat16 --> F16, shape = {2560, 1024}
INFO:hf-to-gguf:blk.18.attn_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.18.layer_output_scale.weight,  torch.bfloat16 --> F32, shape = {1}
INFO:hf-to-gguf:blk.18.ffn_down.weight,            torch.bfloat16 --> F16, shape = {10240, 2560}
INFO:hf-to-gguf:blk.18.ffn_gate.weight,            torch.bfloat16 --> F16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.18.ffn_up.weight,              torch.bfloat16 --> F16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.18.inp_gate.weight,            torch.bfloat16 --> F16, shape = {2560, 256}
INFO:hf-to-gguf:blk.18.proj.weight,                torch.bfloat16 --> F16, shape = {256, 2560}
INFO:hf-to-gguf:blk.18.post_attention_norm.weight, torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.18.post_ffw_norm.weight,       torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.18.post_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.18.ffn_norm.weight,            torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.18.attn_k_norm.weight,         torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.18.attn_k.weight,              torch.bfloat16 --> F16, shape = {2560, 512}
INFO:hf-to-gguf:blk.18.attn_output.weight,         torch.bfloat16 --> F16, shape = {2048, 2560}
INFO:hf-to-gguf:blk.18.attn_q_norm.weight,         torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.18.attn_q.weight,              torch.bfloat16 --> F16, shape = {2560, 2048}
INFO:hf-to-gguf:blk.18.attn_v.weight,              torch.bfloat16 --> F16, shape = {2560, 512}
INFO:hf-to-gguf:blk.19.attn_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.19.layer_output_scale.weight,  torch.bfloat16 --> F32, shape = {1}
INFO:hf-to-gguf:blk.19.ffn_down.weight,            torch.bfloat16 --> F16, shape = {10240, 2560}
INFO:hf-to-gguf:blk.19.ffn_gate.weight,            torch.bfloat16 --> F16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.19.ffn_up.weight,              torch.bfloat16 --> F16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.19.inp_gate.weight,            torch.bfloat16 --> F16, shape = {2560, 256}
INFO:hf-to-gguf:blk.19.proj.weight,                torch.bfloat16 --> F16, shape = {256, 2560}
INFO:hf-to-gguf:blk.19.post_attention_norm.weight, torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.19.post_ffw_norm.weight,       torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.19.post_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.19.ffn_norm.weight,            torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.19.attn_k_norm.weight,         torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.19.attn_k.weight,              torch.bfloat16 --> F16, shape = {2560, 512}
INFO:hf-to-gguf:blk.19.attn_output.weight,         torch.bfloat16 --> F16, shape = {2048, 2560}
INFO:hf-to-gguf:blk.19.attn_q_norm.weight,         torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.19.attn_q.weight,              torch.bfloat16 --> F16, shape = {2560, 2048}
INFO:hf-to-gguf:blk.19.attn_v.weight,              torch.bfloat16 --> F16, shape = {2560, 512}
INFO:hf-to-gguf:blk.2.attn_norm.weight,            torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.2.layer_output_scale.weight,   torch.bfloat16 --> F32, shape = {1}
INFO:hf-to-gguf:blk.2.ffn_down.weight,             torch.bfloat16 --> F16, shape = {10240, 2560}
INFO:hf-to-gguf:blk.2.ffn_gate.weight,             torch.bfloat16 --> F16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.2.ffn_up.weight,               torch.bfloat16 --> F16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.2.inp_gate.weight,             torch.bfloat16 --> F16, shape = {2560, 256}
INFO:hf-to-gguf:blk.2.proj.weight,                 torch.bfloat16 --> F16, shape = {256, 2560}
INFO:hf-to-gguf:blk.2.post_attention_norm.weight,  torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.2.post_ffw_norm.weight,        torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.2.post_norm.weight,            torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.2.ffn_norm.weight,             torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.2.attn_k_norm.weight,          torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.2.attn_k.weight,               torch.bfloat16 --> F16, shape = {2560, 512}
INFO:hf-to-gguf:blk.2.attn_output.weight,          torch.bfloat16 --> F16, shape = {2048, 2560}
INFO:hf-to-gguf:blk.2.attn_q_norm.weight,          torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.2.attn_q.weight,               torch.bfloat16 --> F16, shape = {2560, 2048}
INFO:hf-to-gguf:blk.2.attn_v.weight,               torch.bfloat16 --> F16, shape = {2560, 512}
INFO:hf-to-gguf:blk.20.attn_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.20.layer_output_scale.weight,  torch.bfloat16 --> F32, shape = {1}
INFO:hf-to-gguf:blk.20.ffn_down.weight,            torch.bfloat16 --> F16, shape = {10240, 2560}
INFO:hf-to-gguf:blk.20.ffn_gate.weight,            torch.bfloat16 --> F16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.20.ffn_up.weight,              torch.bfloat16 --> F16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.20.inp_gate.weight,            torch.bfloat16 --> F16, shape = {2560, 256}
INFO:hf-to-gguf:blk.20.proj.weight,                torch.bfloat16 --> F16, shape = {256, 2560}
INFO:hf-to-gguf:blk.20.post_attention_norm.weight, torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.20.post_ffw_norm.weight,       torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.20.post_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.20.ffn_norm.weight,            torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.20.attn_k_norm.weight,         torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.20.attn_k.weight,              torch.bfloat16 --> F16, shape = {2560, 512}
INFO:hf-to-gguf:blk.20.attn_output.weight,         torch.bfloat16 --> F16, shape = {2048, 2560}
INFO:hf-to-gguf:blk.20.attn_q_norm.weight,         torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.20.attn_q.weight,              torch.bfloat16 --> F16, shape = {2560, 2048}
INFO:hf-to-gguf:blk.20.attn_v.weight,              torch.bfloat16 --> F16, shape = {2560, 512}
INFO:hf-to-gguf:blk.21.attn_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.21.layer_output_scale.weight,  torch.bfloat16 --> F32, shape = {1}
INFO:hf-to-gguf:blk.21.ffn_down.weight,            torch.bfloat16 --> F16, shape = {10240, 2560}
INFO:hf-to-gguf:blk.21.ffn_gate.weight,            torch.bfloat16 --> F16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.21.ffn_up.weight,              torch.bfloat16 --> F16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.21.inp_gate.weight,            torch.bfloat16 --> F16, shape = {2560, 256}
INFO:hf-to-gguf:blk.21.proj.weight,                torch.bfloat16 --> F16, shape = {256, 2560}
INFO:hf-to-gguf:blk.21.post_attention_norm.weight, torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.21.post_ffw_norm.weight,       torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.21.post_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.21.ffn_norm.weight,            torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.21.attn_k_norm.weight,         torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.21.attn_k.weight,              torch.bfloat16 --> F16, shape = {2560, 512}
INFO:hf-to-gguf:blk.21.attn_output.weight,         torch.bfloat16 --> F16, shape = {2048, 2560}
INFO:hf-to-gguf:blk.21.attn_q_norm.weight,         torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.21.attn_q.weight,              torch.bfloat16 --> F16, shape = {2560, 2048}
INFO:hf-to-gguf:blk.21.attn_v.weight,              torch.bfloat16 --> F16, shape = {2560, 512}
INFO:hf-to-gguf:blk.22.attn_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.22.layer_output_scale.weight,  torch.bfloat16 --> F32, shape = {1}
INFO:hf-to-gguf:blk.22.ffn_down.weight,            torch.bfloat16 --> F16, shape = {10240, 2560}
INFO:hf-to-gguf:blk.22.ffn_gate.weight,            torch.bfloat16 --> F16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.22.ffn_up.weight,              torch.bfloat16 --> F16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.22.inp_gate.weight,            torch.bfloat16 --> F16, shape = {2560, 256}
INFO:hf-to-gguf:blk.22.proj.weight,                torch.bfloat16 --> F16, shape = {256, 2560}
INFO:hf-to-gguf:blk.22.post_attention_norm.weight, torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.22.post_ffw_norm.weight,       torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.22.post_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.22.ffn_norm.weight,            torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.22.attn_k_norm.weight,         torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.22.attn_k.weight,              torch.bfloat16 --> F16, shape = {2560, 512}
INFO:hf-to-gguf:blk.22.attn_output.weight,         torch.bfloat16 --> F16, shape = {2048, 2560}
INFO:hf-to-gguf:blk.22.attn_q_norm.weight,         torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.22.attn_q.weight,              torch.bfloat16 --> F16, shape = {2560, 2048}
INFO:hf-to-gguf:blk.22.attn_v.weight,              torch.bfloat16 --> F16, shape = {2560, 512}
INFO:hf-to-gguf:blk.23.attn_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.23.layer_output_scale.weight,  torch.bfloat16 --> F32, shape = {1}
INFO:hf-to-gguf:blk.23.ffn_down.weight,            torch.bfloat16 --> F16, shape = {10240, 2560}
INFO:hf-to-gguf:blk.23.ffn_gate.weight,            torch.bfloat16 --> F16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.23.ffn_up.weight,              torch.bfloat16 --> F16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.23.inp_gate.weight,            torch.bfloat16 --> F16, shape = {2560, 256}
INFO:hf-to-gguf:blk.23.proj.weight,                torch.bfloat16 --> F16, shape = {256, 2560}
INFO:hf-to-gguf:blk.23.post_attention_norm.weight, torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.23.post_ffw_norm.weight,       torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.23.post_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.23.ffn_norm.weight,            torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.23.attn_k_norm.weight,         torch.bfloat16 --> F32, shape = {512}
INFO:hf-to-gguf:blk.23.attn_k.weight,              torch.bfloat16 --> F16, shape = {2560, 1024}
INFO:hf-to-gguf:blk.23.attn_output.weight,         torch.bfloat16 --> F16, shape = {4096, 2560}
INFO:hf-to-gguf:blk.23.attn_q_norm.weight,         torch.bfloat16 --> F32, shape = {512}
INFO:hf-to-gguf:blk.23.attn_q.weight,              torch.bfloat16 --> F16, shape = {2560, 4096}
INFO:hf-to-gguf:blk.23.attn_v.weight,              torch.bfloat16 --> F16, shape = {2560, 1024}
INFO:hf-to-gguf:blk.24.attn_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.24.layer_output_scale.weight,  torch.bfloat16 --> F32, shape = {1}
INFO:hf-to-gguf:blk.24.ffn_down.weight,            torch.bfloat16 --> F16, shape = {10240, 2560}
INFO:hf-to-gguf:blk.24.ffn_gate.weight,            torch.bfloat16 --> F16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.24.ffn_up.weight,              torch.bfloat16 --> F16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.24.inp_gate.weight,            torch.bfloat16 --> F16, shape = {2560, 256}
INFO:hf-to-gguf:blk.24.proj.weight,                torch.bfloat16 --> F16, shape = {256, 2560}
INFO:hf-to-gguf:blk.24.post_attention_norm.weight, torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.24.post_ffw_norm.weight,       torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.24.post_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.24.ffn_norm.weight,            torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.24.attn_k_norm.weight,         torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.24.attn_k.weight,              torch.bfloat16 --> F16, shape = {2560, 512}
INFO:hf-to-gguf:blk.24.attn_output.weight,         torch.bfloat16 --> F16, shape = {2048, 2560}
INFO:hf-to-gguf:blk.24.attn_q_norm.weight,         torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.24.attn_q.weight,              torch.bfloat16 --> F16, shape = {2560, 2048}
INFO:hf-to-gguf:blk.24.attn_v.weight,              torch.bfloat16 --> F16, shape = {2560, 512}
INFO:hf-to-gguf:blk.25.attn_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.25.layer_output_scale.weight,  torch.bfloat16 --> F32, shape = {1}
INFO:hf-to-gguf:blk.25.ffn_down.weight,            torch.bfloat16 --> F16, shape = {10240, 2560}
INFO:hf-to-gguf:blk.25.ffn_gate.weight,            torch.bfloat16 --> F16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.25.ffn_up.weight,              torch.bfloat16 --> F16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.25.inp_gate.weight,            torch.bfloat16 --> F16, shape = {2560, 256}
INFO:hf-to-gguf:blk.25.proj.weight,                torch.bfloat16 --> F16, shape = {256, 2560}
INFO:hf-to-gguf:blk.25.post_attention_norm.weight, torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.25.post_ffw_norm.weight,       torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.25.post_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.25.ffn_norm.weight,            torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.25.attn_k_norm.weight,         torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.25.attn_k.weight,              torch.bfloat16 --> F16, shape = {2560, 512}
INFO:hf-to-gguf:blk.25.attn_output.weight,         torch.bfloat16 --> F16, shape = {2048, 2560}
INFO:hf-to-gguf:blk.25.attn_q_norm.weight,         torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.25.attn_q.weight,              torch.bfloat16 --> F16, shape = {2560, 2048}
INFO:hf-to-gguf:blk.25.attn_v.weight,              torch.bfloat16 --> F16, shape = {2560, 512}
INFO:hf-to-gguf:blk.26.attn_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.26.layer_output_scale.weight,  torch.bfloat16 --> F32, shape = {1}
INFO:hf-to-gguf:blk.26.ffn_down.weight,            torch.bfloat16 --> F16, shape = {10240, 2560}
INFO:hf-to-gguf:blk.26.ffn_gate.weight,            torch.bfloat16 --> F16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.26.ffn_up.weight,              torch.bfloat16 --> F16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.26.inp_gate.weight,            torch.bfloat16 --> F16, shape = {2560, 256}
INFO:hf-to-gguf:blk.26.proj.weight,                torch.bfloat16 --> F16, shape = {256, 2560}
INFO:hf-to-gguf:blk.26.post_attention_norm.weight, torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.26.post_ffw_norm.weight,       torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.26.post_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.26.ffn_norm.weight,            torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.26.attn_k_norm.weight,         torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.26.attn_k.weight,              torch.bfloat16 --> F16, shape = {2560, 512}
INFO:hf-to-gguf:blk.26.attn_output.weight,         torch.bfloat16 --> F16, shape = {2048, 2560}
INFO:hf-to-gguf:blk.26.attn_q_norm.weight,         torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.26.attn_q.weight,              torch.bfloat16 --> F16, shape = {2560, 2048}
INFO:hf-to-gguf:blk.26.attn_v.weight,              torch.bfloat16 --> F16, shape = {2560, 512}
INFO:hf-to-gguf:blk.27.attn_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.27.layer_output_scale.weight,  torch.bfloat16 --> F32, shape = {1}
INFO:hf-to-gguf:blk.27.ffn_down.weight,            torch.bfloat16 --> F16, shape = {10240, 2560}
INFO:hf-to-gguf:blk.27.ffn_gate.weight,            torch.bfloat16 --> F16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.27.ffn_up.weight,              torch.bfloat16 --> F16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.27.inp_gate.weight,            torch.bfloat16 --> F16, shape = {2560, 256}
INFO:hf-to-gguf:blk.27.proj.weight,                torch.bfloat16 --> F16, shape = {256, 2560}
INFO:hf-to-gguf:blk.27.post_attention_norm.weight, torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.27.post_ffw_norm.weight,       torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.27.post_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.27.ffn_norm.weight,            torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.27.attn_k_norm.weight,         torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.27.attn_k.weight,              torch.bfloat16 --> F16, shape = {2560, 512}
INFO:hf-to-gguf:blk.27.attn_output.weight,         torch.bfloat16 --> F16, shape = {2048, 2560}
INFO:hf-to-gguf:blk.27.attn_q_norm.weight,         torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.27.attn_q.weight,              torch.bfloat16 --> F16, shape = {2560, 2048}
INFO:hf-to-gguf:blk.27.attn_v.weight,              torch.bfloat16 --> F16, shape = {2560, 512}
INFO:hf-to-gguf:blk.28.attn_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.28.layer_output_scale.weight,  torch.bfloat16 --> F32, shape = {1}
INFO:hf-to-gguf:blk.28.ffn_down.weight,            torch.bfloat16 --> F16, shape = {10240, 2560}
INFO:hf-to-gguf:blk.28.ffn_gate.weight,            torch.bfloat16 --> F16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.28.ffn_up.weight,              torch.bfloat16 --> F16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.28.inp_gate.weight,            torch.bfloat16 --> F16, shape = {2560, 256}
INFO:hf-to-gguf:blk.28.proj.weight,                torch.bfloat16 --> F16, shape = {256, 2560}
INFO:hf-to-gguf:blk.28.post_attention_norm.weight, torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.28.post_ffw_norm.weight,       torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.28.post_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.28.ffn_norm.weight,            torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.28.attn_k_norm.weight,         torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.28.attn_k.weight,              torch.bfloat16 --> F16, shape = {2560, 512}
INFO:hf-to-gguf:blk.28.attn_output.weight,         torch.bfloat16 --> F16, shape = {2048, 2560}
INFO:hf-to-gguf:blk.28.attn_q_norm.weight,         torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.28.attn_q.weight,              torch.bfloat16 --> F16, shape = {2560, 2048}
INFO:hf-to-gguf:blk.28.attn_v.weight,              torch.bfloat16 --> F16, shape = {2560, 512}
INFO:hf-to-gguf:blk.29.attn_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.29.layer_output_scale.weight,  torch.bfloat16 --> F32, shape = {1}
INFO:hf-to-gguf:blk.29.ffn_down.weight,            torch.bfloat16 --> F16, shape = {10240, 2560}
INFO:hf-to-gguf:blk.29.ffn_gate.weight,            torch.bfloat16 --> F16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.29.ffn_up.weight,              torch.bfloat16 --> F16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.29.inp_gate.weight,            torch.bfloat16 --> F16, shape = {2560, 256}
INFO:hf-to-gguf:blk.29.proj.weight,                torch.bfloat16 --> F16, shape = {256, 2560}
INFO:hf-to-gguf:blk.29.post_attention_norm.weight, torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.29.post_ffw_norm.weight,       torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.29.post_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.29.ffn_norm.weight,            torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.29.attn_k_norm.weight,         torch.bfloat16 --> F32, shape = {512}
INFO:hf-to-gguf:blk.29.attn_k.weight,              torch.bfloat16 --> F16, shape = {2560, 1024}
INFO:hf-to-gguf:blk.29.attn_output.weight,         torch.bfloat16 --> F16, shape = {4096, 2560}
INFO:hf-to-gguf:blk.29.attn_q_norm.weight,         torch.bfloat16 --> F32, shape = {512}
INFO:hf-to-gguf:blk.29.attn_q.weight,              torch.bfloat16 --> F16, shape = {2560, 4096}
INFO:hf-to-gguf:blk.29.attn_v.weight,              torch.bfloat16 --> F16, shape = {2560, 1024}
INFO:hf-to-gguf:blk.3.attn_norm.weight,            torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.3.layer_output_scale.weight,   torch.bfloat16 --> F32, shape = {1}
INFO:hf-to-gguf:blk.3.ffn_down.weight,             torch.bfloat16 --> F16, shape = {10240, 2560}
INFO:hf-to-gguf:blk.3.ffn_gate.weight,             torch.bfloat16 --> F16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.3.ffn_up.weight,               torch.bfloat16 --> F16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.3.inp_gate.weight,             torch.bfloat16 --> F16, shape = {2560, 256}
INFO:hf-to-gguf:blk.3.proj.weight,                 torch.bfloat16 --> F16, shape = {256, 2560}
INFO:hf-to-gguf:blk.3.post_attention_norm.weight,  torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.3.post_ffw_norm.weight,        torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.3.post_norm.weight,            torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.3.ffn_norm.weight,             torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.3.attn_k_norm.weight,          torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.3.attn_k.weight,               torch.bfloat16 --> F16, shape = {2560, 512}
INFO:hf-to-gguf:blk.3.attn_output.weight,          torch.bfloat16 --> F16, shape = {2048, 2560}
INFO:hf-to-gguf:blk.3.attn_q_norm.weight,          torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.3.attn_q.weight,               torch.bfloat16 --> F16, shape = {2560, 2048}
INFO:hf-to-gguf:blk.3.attn_v.weight,               torch.bfloat16 --> F16, shape = {2560, 512}
INFO:hf-to-gguf:blk.30.attn_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.30.layer_output_scale.weight,  torch.bfloat16 --> F32, shape = {1}
INFO:hf-to-gguf:blk.30.ffn_down.weight,            torch.bfloat16 --> F16, shape = {10240, 2560}
INFO:hf-to-gguf:blk.30.ffn_gate.weight,            torch.bfloat16 --> F16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.30.ffn_up.weight,              torch.bfloat16 --> F16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.30.inp_gate.weight,            torch.bfloat16 --> F16, shape = {2560, 256}
INFO:hf-to-gguf:blk.30.proj.weight,                torch.bfloat16 --> F16, shape = {256, 2560}
INFO:hf-to-gguf:blk.30.post_attention_norm.weight, torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.30.post_ffw_norm.weight,       torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.30.post_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.30.ffn_norm.weight,            torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.30.attn_k_norm.weight,         torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.30.attn_k.weight,              torch.bfloat16 --> F16, shape = {2560, 512}
INFO:hf-to-gguf:blk.30.attn_output.weight,         torch.bfloat16 --> F16, shape = {2048, 2560}
INFO:hf-to-gguf:blk.30.attn_q_norm.weight,         torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.30.attn_q.weight,              torch.bfloat16 --> F16, shape = {2560, 2048}
INFO:hf-to-gguf:blk.30.attn_v.weight,              torch.bfloat16 --> F16, shape = {2560, 512}
INFO:hf-to-gguf:blk.31.attn_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.31.layer_output_scale.weight,  torch.bfloat16 --> F32, shape = {1}
INFO:hf-to-gguf:blk.31.ffn_down.weight,            torch.bfloat16 --> F16, shape = {10240, 2560}
INFO:hf-to-gguf:blk.31.ffn_gate.weight,            torch.bfloat16 --> F16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.31.ffn_up.weight,              torch.bfloat16 --> F16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.31.inp_gate.weight,            torch.bfloat16 --> F16, shape = {2560, 256}
INFO:hf-to-gguf:blk.31.proj.weight,                torch.bfloat16 --> F16, shape = {256, 2560}
INFO:hf-to-gguf:blk.31.post_attention_norm.weight, torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.31.post_ffw_norm.weight,       torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.31.post_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.31.ffn_norm.weight,            torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.31.attn_k_norm.weight,         torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.31.attn_k.weight,              torch.bfloat16 --> F16, shape = {2560, 512}
INFO:hf-to-gguf:blk.31.attn_output.weight,         torch.bfloat16 --> F16, shape = {2048, 2560}
INFO:hf-to-gguf:blk.31.attn_q_norm.weight,         torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.31.attn_q.weight,              torch.bfloat16 --> F16, shape = {2560, 2048}
INFO:hf-to-gguf:blk.31.attn_v.weight,              torch.bfloat16 --> F16, shape = {2560, 512}
INFO:hf-to-gguf:blk.32.attn_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.32.layer_output_scale.weight,  torch.bfloat16 --> F32, shape = {1}
INFO:hf-to-gguf:blk.32.ffn_down.weight,            torch.bfloat16 --> F16, shape = {10240, 2560}
INFO:hf-to-gguf:blk.32.ffn_gate.weight,            torch.bfloat16 --> F16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.32.ffn_up.weight,              torch.bfloat16 --> F16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.32.inp_gate.weight,            torch.bfloat16 --> F16, shape = {2560, 256}
INFO:hf-to-gguf:blk.32.proj.weight,                torch.bfloat16 --> F16, shape = {256, 2560}
INFO:hf-to-gguf:blk.32.post_attention_norm.weight, torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.32.post_ffw_norm.weight,       torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.32.post_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.32.ffn_norm.weight,            torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.32.attn_k_norm.weight,         torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.32.attn_k.weight,              torch.bfloat16 --> F16, shape = {2560, 512}
INFO:hf-to-gguf:blk.32.attn_output.weight,         torch.bfloat16 --> F16, shape = {2048, 2560}
INFO:hf-to-gguf:blk.32.attn_q_norm.weight,         torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.32.attn_q.weight,              torch.bfloat16 --> F16, shape = {2560, 2048}
INFO:hf-to-gguf:blk.32.attn_v.weight,              torch.bfloat16 --> F16, shape = {2560, 512}
INFO:hf-to-gguf:blk.33.attn_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.33.layer_output_scale.weight,  torch.bfloat16 --> F32, shape = {1}
INFO:hf-to-gguf:blk.33.ffn_down.weight,            torch.bfloat16 --> F16, shape = {10240, 2560}
INFO:hf-to-gguf:blk.33.ffn_gate.weight,            torch.bfloat16 --> F16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.33.ffn_up.weight,              torch.bfloat16 --> F16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.33.inp_gate.weight,            torch.bfloat16 --> F16, shape = {2560, 256}
INFO:hf-to-gguf:blk.33.proj.weight,                torch.bfloat16 --> F16, shape = {256, 2560}
INFO:hf-to-gguf:blk.33.post_attention_norm.weight, torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.33.post_ffw_norm.weight,       torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.33.post_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.33.ffn_norm.weight,            torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.33.attn_k_norm.weight,         torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.33.attn_k.weight,              torch.bfloat16 --> F16, shape = {2560, 512}
INFO:hf-to-gguf:blk.33.attn_output.weight,         torch.bfloat16 --> F16, shape = {2048, 2560}
INFO:hf-to-gguf:blk.33.attn_q_norm.weight,         torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.33.attn_q.weight,              torch.bfloat16 --> F16, shape = {2560, 2048}
INFO:hf-to-gguf:blk.33.attn_v.weight,              torch.bfloat16 --> F16, shape = {2560, 512}
INFO:hf-to-gguf:blk.34.attn_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.34.layer_output_scale.weight,  torch.bfloat16 --> F32, shape = {1}
INFO:hf-to-gguf:blk.34.ffn_down.weight,            torch.bfloat16 --> F16, shape = {10240, 2560}
INFO:hf-to-gguf:blk.34.ffn_gate.weight,            torch.bfloat16 --> F16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.34.ffn_up.weight,              torch.bfloat16 --> F16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.34.inp_gate.weight,            torch.bfloat16 --> F16, shape = {2560, 256}
INFO:hf-to-gguf:blk.34.proj.weight,                torch.bfloat16 --> F16, shape = {256, 2560}
INFO:hf-to-gguf:blk.34.post_attention_norm.weight, torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.34.post_ffw_norm.weight,       torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.34.post_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.34.ffn_norm.weight,            torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.34.attn_k_norm.weight,         torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.34.attn_k.weight,              torch.bfloat16 --> F16, shape = {2560, 512}
INFO:hf-to-gguf:blk.34.attn_output.weight,         torch.bfloat16 --> F16, shape = {2048, 2560}
INFO:hf-to-gguf:blk.34.attn_q_norm.weight,         torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.34.attn_q.weight,              torch.bfloat16 --> F16, shape = {2560, 2048}
INFO:hf-to-gguf:blk.34.attn_v.weight,              torch.bfloat16 --> F16, shape = {2560, 512}
INFO:hf-to-gguf:blk.35.attn_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.35.layer_output_scale.weight,  torch.bfloat16 --> F32, shape = {1}
INFO:hf-to-gguf:blk.35.ffn_down.weight,            torch.bfloat16 --> F16, shape = {10240, 2560}
INFO:hf-to-gguf:blk.35.ffn_gate.weight,            torch.bfloat16 --> F16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.35.ffn_up.weight,              torch.bfloat16 --> F16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.35.inp_gate.weight,            torch.bfloat16 --> F16, shape = {2560, 256}
INFO:hf-to-gguf:blk.35.proj.weight,                torch.bfloat16 --> F16, shape = {256, 2560}
INFO:hf-to-gguf:blk.35.post_attention_norm.weight, torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.35.post_ffw_norm.weight,       torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.35.post_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.35.ffn_norm.weight,            torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.35.attn_k_norm.weight,         torch.bfloat16 --> F32, shape = {512}
INFO:hf-to-gguf:blk.35.attn_k.weight,              torch.bfloat16 --> F16, shape = {2560, 1024}
INFO:hf-to-gguf:blk.35.attn_output.weight,         torch.bfloat16 --> F16, shape = {4096, 2560}
INFO:hf-to-gguf:blk.35.attn_q_norm.weight,         torch.bfloat16 --> F32, shape = {512}
INFO:hf-to-gguf:blk.35.attn_q.weight,              torch.bfloat16 --> F16, shape = {2560, 4096}
INFO:hf-to-gguf:blk.35.attn_v.weight,              torch.bfloat16 --> F16, shape = {2560, 1024}
INFO:hf-to-gguf:blk.36.attn_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.36.layer_output_scale.weight,  torch.bfloat16 --> F32, shape = {1}
INFO:hf-to-gguf:blk.36.ffn_down.weight,            torch.bfloat16 --> F16, shape = {10240, 2560}
INFO:hf-to-gguf:blk.36.ffn_gate.weight,            torch.bfloat16 --> F16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.36.ffn_up.weight,              torch.bfloat16 --> F16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.36.inp_gate.weight,            torch.bfloat16 --> F16, shape = {2560, 256}
INFO:hf-to-gguf:blk.36.proj.weight,                torch.bfloat16 --> F16, shape = {256, 2560}
INFO:hf-to-gguf:blk.36.post_attention_norm.weight, torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.36.post_ffw_norm.weight,       torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.36.post_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.36.ffn_norm.weight,            torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.36.attn_k_norm.weight,         torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.36.attn_k.weight,              torch.bfloat16 --> F16, shape = {2560, 512}
INFO:hf-to-gguf:blk.36.attn_output.weight,         torch.bfloat16 --> F16, shape = {2048, 2560}
INFO:hf-to-gguf:blk.36.attn_q_norm.weight,         torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.36.attn_q.weight,              torch.bfloat16 --> F16, shape = {2560, 2048}
INFO:hf-to-gguf:blk.36.attn_v.weight,              torch.bfloat16 --> F16, shape = {2560, 512}
INFO:hf-to-gguf:blk.37.attn_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.37.layer_output_scale.weight,  torch.bfloat16 --> F32, shape = {1}
INFO:hf-to-gguf:blk.37.ffn_down.weight,            torch.bfloat16 --> F16, shape = {10240, 2560}
INFO:hf-to-gguf:blk.37.ffn_gate.weight,            torch.bfloat16 --> F16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.37.ffn_up.weight,              torch.bfloat16 --> F16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.37.inp_gate.weight,            torch.bfloat16 --> F16, shape = {2560, 256}
INFO:hf-to-gguf:blk.37.proj.weight,                torch.bfloat16 --> F16, shape = {256, 2560}
INFO:hf-to-gguf:blk.37.post_attention_norm.weight, torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.37.post_ffw_norm.weight,       torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.37.post_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.37.ffn_norm.weight,            torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.37.attn_k_norm.weight,         torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.37.attn_k.weight,              torch.bfloat16 --> F16, shape = {2560, 512}
INFO:hf-to-gguf:blk.37.attn_output.weight,         torch.bfloat16 --> F16, shape = {2048, 2560}
INFO:hf-to-gguf:blk.37.attn_q_norm.weight,         torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.37.attn_q.weight,              torch.bfloat16 --> F16, shape = {2560, 2048}
INFO:hf-to-gguf:blk.37.attn_v.weight,              torch.bfloat16 --> F16, shape = {2560, 512}
INFO:hf-to-gguf:blk.38.attn_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.38.layer_output_scale.weight,  torch.bfloat16 --> F32, shape = {1}
INFO:hf-to-gguf:blk.38.ffn_down.weight,            torch.bfloat16 --> F16, shape = {10240, 2560}
INFO:hf-to-gguf:blk.38.ffn_gate.weight,            torch.bfloat16 --> F16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.38.ffn_up.weight,              torch.bfloat16 --> F16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.38.inp_gate.weight,            torch.bfloat16 --> F16, shape = {2560, 256}
INFO:hf-to-gguf:blk.38.proj.weight,                torch.bfloat16 --> F16, shape = {256, 2560}
INFO:hf-to-gguf:blk.38.post_attention_norm.weight, torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.38.post_ffw_norm.weight,       torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.38.post_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.38.ffn_norm.weight,            torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.38.attn_k_norm.weight,         torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.38.attn_k.weight,              torch.bfloat16 --> F16, shape = {2560, 512}
INFO:hf-to-gguf:blk.38.attn_output.weight,         torch.bfloat16 --> F16, shape = {2048, 2560}
INFO:hf-to-gguf:blk.38.attn_q_norm.weight,         torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.38.attn_q.weight,              torch.bfloat16 --> F16, shape = {2560, 2048}
INFO:hf-to-gguf:blk.38.attn_v.weight,              torch.bfloat16 --> F16, shape = {2560, 512}
INFO:hf-to-gguf:blk.39.attn_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.39.layer_output_scale.weight,  torch.bfloat16 --> F32, shape = {1}
INFO:hf-to-gguf:blk.39.ffn_down.weight,            torch.bfloat16 --> F16, shape = {10240, 2560}
INFO:hf-to-gguf:blk.39.ffn_gate.weight,            torch.bfloat16 --> F16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.39.ffn_up.weight,              torch.bfloat16 --> F16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.39.inp_gate.weight,            torch.bfloat16 --> F16, shape = {2560, 256}
INFO:hf-to-gguf:blk.39.proj.weight,                torch.bfloat16 --> F16, shape = {256, 2560}
INFO:hf-to-gguf:blk.39.post_attention_norm.weight, torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.39.post_ffw_norm.weight,       torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.39.post_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.39.ffn_norm.weight,            torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.39.attn_k_norm.weight,         torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.39.attn_k.weight,              torch.bfloat16 --> F16, shape = {2560, 512}
INFO:hf-to-gguf:blk.39.attn_output.weight,         torch.bfloat16 --> F16, shape = {2048, 2560}
INFO:hf-to-gguf:blk.39.attn_q_norm.weight,         torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.39.attn_q.weight,              torch.bfloat16 --> F16, shape = {2560, 2048}
INFO:hf-to-gguf:blk.39.attn_v.weight,              torch.bfloat16 --> F16, shape = {2560, 512}
INFO:hf-to-gguf:blk.4.attn_norm.weight,            torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.4.layer_output_scale.weight,   torch.bfloat16 --> F32, shape = {1}
INFO:hf-to-gguf:blk.4.ffn_down.weight,             torch.bfloat16 --> F16, shape = {10240, 2560}
INFO:hf-to-gguf:blk.4.ffn_gate.weight,             torch.bfloat16 --> F16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.4.ffn_up.weight,               torch.bfloat16 --> F16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.4.inp_gate.weight,             torch.bfloat16 --> F16, shape = {2560, 256}
INFO:hf-to-gguf:blk.4.proj.weight,                 torch.bfloat16 --> F16, shape = {256, 2560}
INFO:hf-to-gguf:blk.4.post_attention_norm.weight,  torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.4.post_ffw_norm.weight,        torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.4.post_norm.weight,            torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.4.ffn_norm.weight,             torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.4.attn_k_norm.weight,          torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.4.attn_k.weight,               torch.bfloat16 --> F16, shape = {2560, 512}
INFO:hf-to-gguf:blk.4.attn_output.weight,          torch.bfloat16 --> F16, shape = {2048, 2560}
INFO:hf-to-gguf:blk.4.attn_q_norm.weight,          torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.4.attn_q.weight,               torch.bfloat16 --> F16, shape = {2560, 2048}
INFO:hf-to-gguf:blk.4.attn_v.weight,               torch.bfloat16 --> F16, shape = {2560, 512}
INFO:hf-to-gguf:blk.40.attn_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.40.layer_output_scale.weight,  torch.bfloat16 --> F32, shape = {1}
INFO:hf-to-gguf:blk.40.ffn_down.weight,            torch.bfloat16 --> F16, shape = {10240, 2560}
INFO:hf-to-gguf:blk.40.ffn_gate.weight,            torch.bfloat16 --> F16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.40.ffn_up.weight,              torch.bfloat16 --> F16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.40.inp_gate.weight,            torch.bfloat16 --> F16, shape = {2560, 256}
INFO:hf-to-gguf:blk.40.proj.weight,                torch.bfloat16 --> F16, shape = {256, 2560}
INFO:hf-to-gguf:blk.40.post_attention_norm.weight, torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.40.post_ffw_norm.weight,       torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.40.post_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.40.ffn_norm.weight,            torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.40.attn_k_norm.weight,         torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.40.attn_k.weight,              torch.bfloat16 --> F16, shape = {2560, 512}
INFO:hf-to-gguf:blk.40.attn_output.weight,         torch.bfloat16 --> F16, shape = {2048, 2560}
INFO:hf-to-gguf:blk.40.attn_q_norm.weight,         torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.40.attn_q.weight,              torch.bfloat16 --> F16, shape = {2560, 2048}
INFO:hf-to-gguf:blk.40.attn_v.weight,              torch.bfloat16 --> F16, shape = {2560, 512}
INFO:hf-to-gguf:blk.41.attn_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.41.layer_output_scale.weight,  torch.bfloat16 --> F32, shape = {1}
INFO:hf-to-gguf:blk.41.ffn_down.weight,            torch.bfloat16 --> F16, shape = {10240, 2560}
INFO:hf-to-gguf:blk.41.ffn_gate.weight,            torch.bfloat16 --> F16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.41.ffn_up.weight,              torch.bfloat16 --> F16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.41.inp_gate.weight,            torch.bfloat16 --> F16, shape = {2560, 256}
INFO:hf-to-gguf:blk.41.proj.weight,                torch.bfloat16 --> F16, shape = {256, 2560}
INFO:hf-to-gguf:blk.41.post_attention_norm.weight, torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.41.post_ffw_norm.weight,       torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.41.post_norm.weight,           torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.41.ffn_norm.weight,            torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.41.attn_k_norm.weight,         torch.bfloat16 --> F32, shape = {512}
INFO:hf-to-gguf:blk.41.attn_k.weight,              torch.bfloat16 --> F16, shape = {2560, 1024}
INFO:hf-to-gguf:blk.41.attn_output.weight,         torch.bfloat16 --> F16, shape = {4096, 2560}
INFO:hf-to-gguf:blk.41.attn_q_norm.weight,         torch.bfloat16 --> F32, shape = {512}
INFO:hf-to-gguf:blk.41.attn_q.weight,              torch.bfloat16 --> F16, shape = {2560, 4096}
INFO:hf-to-gguf:blk.41.attn_v.weight,              torch.bfloat16 --> F16, shape = {2560, 1024}
INFO:hf-to-gguf:blk.5.attn_norm.weight,            torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.5.layer_output_scale.weight,   torch.bfloat16 --> F32, shape = {1}
INFO:hf-to-gguf:blk.5.ffn_down.weight,             torch.bfloat16 --> F16, shape = {10240, 2560}
INFO:hf-to-gguf:blk.5.ffn_gate.weight,             torch.bfloat16 --> F16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.5.ffn_up.weight,               torch.bfloat16 --> F16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.5.inp_gate.weight,             torch.bfloat16 --> F16, shape = {2560, 256}
INFO:hf-to-gguf:blk.5.proj.weight,                 torch.bfloat16 --> F16, shape = {256, 2560}
INFO:hf-to-gguf:blk.5.post_attention_norm.weight,  torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.5.post_ffw_norm.weight,        torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.5.post_norm.weight,            torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.5.ffn_norm.weight,             torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.5.attn_k_norm.weight,          torch.bfloat16 --> F32, shape = {512}
INFO:hf-to-gguf:blk.5.attn_k.weight,               torch.bfloat16 --> F16, shape = {2560, 1024}
INFO:hf-to-gguf:blk.5.attn_output.weight,          torch.bfloat16 --> F16, shape = {4096, 2560}
INFO:hf-to-gguf:blk.5.attn_q_norm.weight,          torch.bfloat16 --> F32, shape = {512}
INFO:hf-to-gguf:blk.5.attn_q.weight,               torch.bfloat16 --> F16, shape = {2560, 4096}
INFO:hf-to-gguf:blk.5.attn_v.weight,               torch.bfloat16 --> F16, shape = {2560, 1024}
INFO:hf-to-gguf:blk.6.attn_norm.weight,            torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.6.layer_output_scale.weight,   torch.bfloat16 --> F32, shape = {1}
INFO:hf-to-gguf:blk.6.ffn_down.weight,             torch.bfloat16 --> F16, shape = {10240, 2560}
INFO:hf-to-gguf:blk.6.ffn_gate.weight,             torch.bfloat16 --> F16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.6.ffn_up.weight,               torch.bfloat16 --> F16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.6.inp_gate.weight,             torch.bfloat16 --> F16, shape = {2560, 256}
INFO:hf-to-gguf:blk.6.proj.weight,                 torch.bfloat16 --> F16, shape = {256, 2560}
INFO:hf-to-gguf:blk.6.post_attention_norm.weight,  torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.6.post_ffw_norm.weight,        torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.6.post_norm.weight,            torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.6.ffn_norm.weight,             torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.6.attn_k_norm.weight,          torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.6.attn_k.weight,               torch.bfloat16 --> F16, shape = {2560, 512}
INFO:hf-to-gguf:blk.6.attn_output.weight,          torch.bfloat16 --> F16, shape = {2048, 2560}
INFO:hf-to-gguf:blk.6.attn_q_norm.weight,          torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.6.attn_q.weight,               torch.bfloat16 --> F16, shape = {2560, 2048}
INFO:hf-to-gguf:blk.6.attn_v.weight,               torch.bfloat16 --> F16, shape = {2560, 512}
INFO:hf-to-gguf:blk.7.attn_norm.weight,            torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.7.layer_output_scale.weight,   torch.bfloat16 --> F32, shape = {1}
INFO:hf-to-gguf:blk.7.ffn_down.weight,             torch.bfloat16 --> F16, shape = {10240, 2560}
INFO:hf-to-gguf:blk.7.ffn_gate.weight,             torch.bfloat16 --> F16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.7.ffn_up.weight,               torch.bfloat16 --> F16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.7.inp_gate.weight,             torch.bfloat16 --> F16, shape = {2560, 256}
INFO:hf-to-gguf:blk.7.proj.weight,                 torch.bfloat16 --> F16, shape = {256, 2560}
INFO:hf-to-gguf:blk.7.post_attention_norm.weight,  torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.7.post_ffw_norm.weight,        torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.7.post_norm.weight,            torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.7.ffn_norm.weight,             torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.7.attn_k_norm.weight,          torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.7.attn_k.weight,               torch.bfloat16 --> F16, shape = {2560, 512}
INFO:hf-to-gguf:blk.7.attn_output.weight,          torch.bfloat16 --> F16, shape = {2048, 2560}
INFO:hf-to-gguf:blk.7.attn_q_norm.weight,          torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.7.attn_q.weight,               torch.bfloat16 --> F16, shape = {2560, 2048}
INFO:hf-to-gguf:blk.7.attn_v.weight,               torch.bfloat16 --> F16, shape = {2560, 512}
INFO:hf-to-gguf:blk.8.attn_norm.weight,            torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.8.layer_output_scale.weight,   torch.bfloat16 --> F32, shape = {1}
INFO:hf-to-gguf:blk.8.ffn_down.weight,             torch.bfloat16 --> F16, shape = {10240, 2560}
INFO:hf-to-gguf:blk.8.ffn_gate.weight,             torch.bfloat16 --> F16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.8.ffn_up.weight,               torch.bfloat16 --> F16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.8.inp_gate.weight,             torch.bfloat16 --> F16, shape = {2560, 256}
INFO:hf-to-gguf:blk.8.proj.weight,                 torch.bfloat16 --> F16, shape = {256, 2560}
INFO:hf-to-gguf:blk.8.post_attention_norm.weight,  torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.8.post_ffw_norm.weight,        torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.8.post_norm.weight,            torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.8.ffn_norm.weight,             torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.8.attn_k_norm.weight,          torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.8.attn_k.weight,               torch.bfloat16 --> F16, shape = {2560, 512}
INFO:hf-to-gguf:blk.8.attn_output.weight,          torch.bfloat16 --> F16, shape = {2048, 2560}
INFO:hf-to-gguf:blk.8.attn_q_norm.weight,          torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.8.attn_q.weight,               torch.bfloat16 --> F16, shape = {2560, 2048}
INFO:hf-to-gguf:blk.8.attn_v.weight,               torch.bfloat16 --> F16, shape = {2560, 512}
INFO:hf-to-gguf:blk.9.attn_norm.weight,            torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.9.layer_output_scale.weight,   torch.bfloat16 --> F32, shape = {1}
INFO:hf-to-gguf:blk.9.ffn_down.weight,             torch.bfloat16 --> F16, shape = {10240, 2560}
INFO:hf-to-gguf:blk.9.ffn_gate.weight,             torch.bfloat16 --> F16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.9.ffn_up.weight,               torch.bfloat16 --> F16, shape = {2560, 10240}
INFO:hf-to-gguf:blk.9.inp_gate.weight,             torch.bfloat16 --> F16, shape = {2560, 256}
INFO:hf-to-gguf:blk.9.proj.weight,                 torch.bfloat16 --> F16, shape = {256, 2560}
INFO:hf-to-gguf:blk.9.post_attention_norm.weight,  torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.9.post_ffw_norm.weight,        torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.9.post_norm.weight,            torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.9.ffn_norm.weight,             torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:blk.9.attn_k_norm.weight,          torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.9.attn_k.weight,               torch.bfloat16 --> F16, shape = {2560, 512}
INFO:hf-to-gguf:blk.9.attn_output.weight,          torch.bfloat16 --> F16, shape = {2048, 2560}
INFO:hf-to-gguf:blk.9.attn_q_norm.weight,          torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:blk.9.attn_q.weight,               torch.bfloat16 --> F16, shape = {2560, 2048}
INFO:hf-to-gguf:blk.9.attn_v.weight,               torch.bfloat16 --> F16, shape = {2560, 512}
INFO:hf-to-gguf:output_norm.weight,                torch.bfloat16 --> F32, shape = {2560}
INFO:hf-to-gguf:per_layer_model_proj.weight,       torch.bfloat16 --> F16, shape = {2560, 10752}
INFO:hf-to-gguf:per_layer_proj_norm.weight,        torch.bfloat16 --> F32, shape = {256}
INFO:hf-to-gguf:Set meta model
INFO:hf-to-gguf:Set model parameters
INFO:hf-to-gguf:gguf: context length = 131072
INFO:hf-to-gguf:gguf: embedding length = 2560
INFO:hf-to-gguf:gguf: feed forward length = 10240
INFO:hf-to-gguf:gguf: head count = 8
INFO:hf-to-gguf:gguf: key-value head count = 2
WARNING:hf-to-gguf:Unknown RoPE type: proportional
INFO:hf-to-gguf:gguf: rope scaling type = NONE
INFO:hf-to-gguf:gguf: rope theta = 1000000.0
INFO:hf-to-gguf:gguf: rope theta swa = 10000.0
INFO:hf-to-gguf:gguf: rms norm epsilon = 1e-06
INFO:hf-to-gguf:gguf: file type = 1
WARNING:gguf.gguf_writer:Duplicated key name 'gemma4.context_length', overwriting it with new value 131072 of type UINT32
WARNING:gguf.gguf_writer:Duplicated key name 'gemma4.attention.head_count', overwriting it with new value 8 of type UINT32
WARNING:gguf.gguf_writer:Duplicated key name 'gemma4.attention.layer_norm_rms_epsilon', overwriting it with new value 1e-06 of type FLOAT32
WARNING:gguf.gguf_writer:Duplicated key name 'gemma4.attention.key_length', overwriting it with new value 256 of type UINT32
WARNING:gguf.gguf_writer:Duplicated key name 'gemma4.attention.value_length', overwriting it with new value 256 of type UINT32
WARNING:gguf.gguf_writer:Duplicated key name 'gemma4.rope.freq_base', overwriting it with new value 1000000.0 of type FLOAT32
WARNING:gguf.gguf_writer:Duplicated key name 'gemma4.attention.head_count_kv', overwriting it with new value 2 of type UINT32
WARNING:gguf.gguf_writer:Duplicated key name 'gemma4.attention.key_length', overwriting it with new value 512 of type UINT32
WARNING:gguf.gguf_writer:Duplicated key name 'gemma4.attention.value_length', overwriting it with new value 512 of type UINT32
INFO:hf-to-gguf:Set model quantization version
INFO:hf-to-gguf:Set model tokenizer
INFO:hf-to-gguf:Token '<|tool_call>' is set to USER_DEFINED
INFO:hf-to-gguf:Token '<tool_call|>' is set to USER_DEFINED</tool_call|>
INFO:hf-to-gguf:Token '<|tool_response>' is set to USER_DEFINED
INFO:hf-to-gguf:Token '<tool_response|>' is set to USER_DEFINED</tool_response|>
INFO:hf-to-gguf:Token '<|"|>' is set to USER_DEFINED
INFO:hf-to-gguf:Token '<|channel>' is set to USER_DEFINED
INFO:hf-to-gguf:Token '<channel|>' is set to USER_DEFINED</channel|>
WARNING:gguf.vocab:Unknown separator token '' in TemplateProcessing
INFO:gguf.vocab:Adding 514906 merge(s).
INFO:gguf.vocab:Setting special token type bos to 2
INFO:gguf.vocab:Setting special token type eos to 1
INFO:gguf.vocab:Setting special token type pad to 0
INFO:gguf.vocab:Setting add_bos_token to True
INFO:gguf.vocab:Setting add_sep_token to False
WARNING:gguf.gguf_writer:Duplicated key name 'tokenizer.ggml.add_bos_token', overwriting it with new value True of type BOOL
INFO:gguf.gguf_writer:Writing the following files:
INFO:gguf.gguf_writer:gguf.gguf: n_tensors = 720, total_size = 15.0G

 

양자화 F16 으로 했더니 14GB 정도 합산해서 먹고 있고

 

llama.cpp로 해보니 애가 오작동한다. 왜 저러니 ㅠㅠ

 

+

$ /mnt/Downloads/llama-b9305/llama-quantize 
usage: /mnt/Downloads/llama-b9305/llama-quantize [--help] [--allow-requantize] [--leave-output-tensor] [--pure] [--imatrix] [--include-weights]
       [--exclude-weights] [--output-tensor-type] [--token-embedding-type] [--tensor-type] [--tensor-type-file]
       [--prune-layers] [--keep-split] [--override-kv] [--dry-run]
       model-f32.gguf [model-quant.gguf] type [nthreads]

  --allow-requantize
                                      allow requantizing tensors that have already been quantized
                                      WARNING: this can severely reduce quality compared to quantizing
                                               from 16bit or 32bit!
  --leave-output-tensor
                                      leave output.weight un(re)quantized
                                      increases model size but may also increase quality, especially when requantizing
  --pure
                                      disable k-quant mixtures and quantize all tensors to the same type
  --imatrix file_name
                                      use data in file_name as importance matrix for quant optimizations
  --include-weights tensor_name
                                      use importance matrix for this/these tensor(s)
  --exclude-weights tensor_name
                                      do not use importance matrix for this/these tensor(s)
  --output-tensor-type ggml_type
                                      use this ggml_type for the output.weight tensor
  --token-embedding-type ggml_type
                                      use this ggml_type for the token embeddings tensor
  --tensor-type tensor_name=ggml_type
                                      quantize this tensor to this ggml_type
                                      this is an advanced option to selectively quantize tensors. may be specified multiple times.
                                      example: --tensor-type attn_q=q8_0
  --tensor-type-file tensor_types.txt
                                      list of tensors to quantize to a specific ggml_type
                                      this is an advanced option to selectively quantize a long list of tensors.
                                      the file should use the same format as above, separated by spaces or newlines.
  --prune-layers L0,L1,L2...
                                      comma-separated list of layer numbers to prune from the model
                                      WARNING: this is an advanced option, use with care.
  --keep-split
                                      generate quantized model in the same shards as input
  --override-kv KEY=TYPE:VALUE
                                      override model metadata by key in the quantized model. may be specified multiple times.
                                      WARNING: this is an advanced option, use with care.
  --dry-run
                                      calculate and show the final quantization size without performing quantization
                                      example: llama-quantize --dry-run model-f32.gguf Q4_K

note: --include-weights and --exclude-weights cannot be used together

-----------------------------------------------------------------------------
 allowed quantization types
-----------------------------------------------------------------------------

  40  or  Q1_0    :  1.125 bpw quantization
   2  or  Q4_0    :  4.34G, +0.4685 ppl @ Llama-3-8B
   3  or  Q4_1    :  4.78G, +0.4511 ppl @ Llama-3-8B
  38  or  MXFP4_MOE :  MXFP4 MoE
   8  or  Q5_0    :  5.21G, +0.1316 ppl @ Llama-3-8B
   9  or  Q5_1    :  5.65G, +0.1062 ppl @ Llama-3-8B
  19  or  IQ2_XXS :  2.06 bpw quantization
  20  or  IQ2_XS  :  2.31 bpw quantization
  28  or  IQ2_S   :  2.5  bpw quantization
  29  or  IQ2_M   :  2.7  bpw quantization
  24  or  IQ1_S   :  1.56 bpw quantization
  31  or  IQ1_M   :  1.75 bpw quantization
  36  or  TQ1_0   :  1.69 bpw ternarization
  37  or  TQ2_0   :  2.06 bpw ternarization
  10  or  Q2_K    :  2.96G, +3.5199 ppl @ Llama-3-8B
  21  or  Q2_K_S  :  2.96G, +3.1836 ppl @ Llama-3-8B
  23  or  IQ3_XXS :  3.06 bpw quantization
  26  or  IQ3_S   :  3.44 bpw quantization
  27  or  IQ3_M   :  3.66 bpw quantization mix
  12  or  Q3_K    : alias for Q3_K_M
  22  or  IQ3_XS  :  3.3 bpw quantization
  11  or  Q3_K_S  :  3.41G, +1.6321 ppl @ Llama-3-8B
  12  or  Q3_K_M  :  3.74G, +0.6569 ppl @ Llama-3-8B
  13  or  Q3_K_L  :  4.03G, +0.5562 ppl @ Llama-3-8B
  25  or  IQ4_NL  :  4.50 bpw non-linear quantization
  30  or  IQ4_XS  :  4.25 bpw non-linear quantization
  15  or  Q4_K    : alias for Q4_K_M
  14  or  Q4_K_S  :  4.37G, +0.2689 ppl @ Llama-3-8B
  15  or  Q4_K_M  :  4.58G, +0.1754 ppl @ Llama-3-8B
  17  or  Q5_K    : alias for Q5_K_M
  16  or  Q5_K_S  :  5.21G, +0.1049 ppl @ Llama-3-8B
  17  or  Q5_K_M  :  5.33G, +0.0569 ppl @ Llama-3-8B
  18  or  Q6_K    :  6.14G, +0.0217 ppl @ Llama-3-8B
   7  or  Q8_0    :  7.96G, +0.0026 ppl @ Llama-3-8B
   1  or  F16     : 14.00G, +0.0020 ppl @ Mistral-7B
  32  or  BF16    : 14.00G, -0.0050 ppl @ Mistral-7B
   0  or  F32     : 26.00G              @ 7B
          COPY    : only copy tensors, no quantizing 

 

F16 에서 Q8로 바꾸니 용량이 절반으로!

-rw-rw-r-- 1 minimonk minimonk  4977169088  4월 29 10:43 gemma-4-E4B-it-Q4_K_M.gguf

-rw-rw-r-- 1 minimonk minimonk 15053078208  6월  1 14:24 gguf.gguf
-rw-rw-r-- 1 minimonk minimonk  5335273152  6월  1 14:32 Q4_K_M.gguf
-rw-rw-r-- 1 minimonk minimonk  8031223488  6월  1 14:27 Q8.gguf

 

$ /mnt/Downloads/llama-b9305/llama-quantize  ./gguf.gguf ./Q8.gguf Q8_0
load_backend: loaded RPC backend from /mnt/Downloads/llama-b9305/libggml-rpc.so
ggml_vulkan: Found 3 Vulkan devices:
ggml_vulkan: 0 = Intel(R) UHD Graphics 630 (CFL GT2) (Intel open-source Mesa driver) | uma: 1 | fp16: 1 | bf16: 0 | warp size: 32 | shared memory: 65536 | int dot: 0 | matrix cores: none
ggml_vulkan: 1 = NVIDIA GeForce GTX 1080 Ti (NVIDIA) | uma: 0 | fp16: 0 | bf16: 0 | warp size: 32 | shared memory: 49152 | int dot: 1 | matrix cores: none
ggml_vulkan: 2 = NVIDIA GeForce GTX 1080 Ti (NVIDIA) | uma: 0 | fp16: 0 | bf16: 0 | warp size: 32 | shared memory: 49152 | int dot: 1 | matrix cores: none
load_backend: loaded Vulkan backend from /mnt/Downloads/llama-b9305/libggml-vulkan.so
load_backend: loaded CPU backend from /mnt/Downloads/llama-b9305/libggml-cpu-haswell.so
llama_print_build_info: build = 9305 (63248fc3e)
llama_print_build_info: built with GNU 11.4.0 for Linux x86_64
llama_quantize: quantizing './gguf.gguf' to './Q8.gguf' as Q8_0
llama_model_loader: loaded meta data with 37 key-value pairs and 720 tensors from ./gguf.gguf (version GGUF V3 (latest))
llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
llama_model_loader: - kv   0:                       general.architecture str              = gemma4
llama_model_loader: - kv   1:                               general.type str              = model
llama_model_loader: - kv   2:                               general.name str              = Google
llama_model_loader: - kv   3:                         general.size_label str              = 7.5B
llama_model_loader: - kv   4:                         gemma4.block_count u32              = 42
llama_model_loader: - kv   5:                      gemma4.context_length u32              = 131072
llama_model_loader: - kv   6:                    gemma4.embedding_length u32              = 2560
llama_model_loader: - kv   7:                 gemma4.feed_forward_length u32              = 10240
llama_model_loader: - kv   8:                gemma4.attention.head_count u32              = 8
llama_model_loader: - kv   9:             gemma4.attention.head_count_kv u32              = 2
llama_model_loader: - kv  10:                      gemma4.rope.freq_base f32              = 1000000.000000
llama_model_loader: - kv  11:                  gemma4.rope.freq_base_swa f32              = 10000.000000
llama_model_loader: - kv  12:    gemma4.attention.layer_norm_rms_epsilon f32              = 0.000001
llama_model_loader: - kv  13:                gemma4.attention.key_length u32              = 512
llama_model_loader: - kv  14:              gemma4.attention.value_length u32              = 512
llama_model_loader: - kv  15:                          general.file_type u32              = 1
llama_model_loader: - kv  16:             gemma4.final_logit_softcapping f32              = 30.000000
llama_model_loader: - kv  17:            gemma4.attention.sliding_window u32              = 512
llama_model_loader: - kv  18:          gemma4.attention.shared_kv_layers u32              = 18
llama_model_loader: - kv  19:    gemma4.embedding_length_per_layer_input u32              = 256
llama_model_loader: - kv  20:    gemma4.attention.sliding_window_pattern arr[bool,42]     = [true, true, true, true, true, false,...
llama_model_loader: - kv  21:            gemma4.attention.key_length_swa u32              = 256
llama_model_loader: - kv  22:          gemma4.attention.value_length_swa u32              = 256
llama_model_loader: - kv  23:                gemma4.rope.dimension_count u32              = 512
llama_model_loader: - kv  24:            gemma4.rope.dimension_count_swa u32              = 256
llama_model_loader: - kv  25:               general.quantization_version u32              = 2
llama_model_loader: - kv  26:                       tokenizer.ggml.model str              = gemma4
llama_model_loader: - kv  27:                      tokenizer.ggml.tokens arr[str,262144]  = ["<pad>", "<eos>", "<bos>", "<unk>", ...
llama_model_loader: - kv  28:                      tokenizer.ggml.scores arr[f32,262144]  = [-1000.000000, -1000.000000, -1000.00...
llama_model_loader: - kv  29:                  tokenizer.ggml.token_type arr[i32,262144]  = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv  30:                      tokenizer.ggml.merges arr[str,514906]  = ["\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n", ...
llama_model_loader: - kv  31:                tokenizer.ggml.bos_token_id u32              = 2
llama_model_loader: - kv  32:                tokenizer.ggml.eos_token_id u32              = 1
llama_model_loader: - kv  33:            tokenizer.ggml.padding_token_id u32              = 0
llama_model_loader: - kv  34:               tokenizer.ggml.add_bos_token bool             = true
llama_model_loader: - kv  35:               tokenizer.ggml.add_sep_token bool             = false
llama_model_loader: - kv  36:            tokenizer.ggml.add_space_prefix bool             = false
llama_model_loader: - type  f32:  339 tensors
llama_model_loader: - type  f16:  381 tensors

/// 여기 생략됨 

llama_model_quantize_impl: model size  = 14340.66 MiB (16.00 BPW)
llama_model_quantize_impl: quant size  =  7644.10 MiB (8.53 BPW)

llama_quantize: quantize time = 99905.90 ms
llama_quantize:    total time = 99905.90 ms

 

더보기

[   1/ 720] output_norm.weight                   - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[   2/ 720] per_layer_model_proj.weight          - [  2560,  10752,      1,      1], type =    f16, size =   52.500 MiB
[   3/ 720] per_layer_proj_norm.weight           - [   256,      1,      1,      1], type =    f32, size =    0.001 MiB
[   4/ 720] per_layer_token_embd.weight          - [ 10752, 262144,      1,      1], type =    f16, converting to q8_0 .. size =  5376.00 MiB ->  2856.00 MiB
[   5/ 720] rope_freqs.weight                    - [   256,      1,      1,      1], type =    f32, size =    0.001 MiB
[   6/ 720] token_embd.weight                    - [  2560, 262144,      1,      1], type =    f16, converting to q8_0 .. size =  1280.00 MiB ->   680.00 MiB
[   7/ 720] blk.0.attn_k.weight                  - [  2560,    512,      1,      1], type =    f16, converting to q8_0 .. size =     2.50 MiB ->     1.33 MiB
[   8/ 720] blk.0.attn_k_norm.weight             - [   256,      1,      1,      1], type =    f32, size =    0.001 MiB
[   9/ 720] blk.0.attn_norm.weight               - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[  10/ 720] blk.0.attn_output.weight             - [  2048,   2560,      1,      1], type =    f16, converting to q8_0 .. size =    10.00 MiB ->     5.31 MiB
[  11/ 720] blk.0.attn_q.weight                  - [  2560,   2048,      1,      1], type =    f16, converting to q8_0 .. size =    10.00 MiB ->     5.31 MiB
[  12/ 720] blk.0.attn_q_norm.weight             - [   256,      1,      1,      1], type =    f32, size =    0.001 MiB
[  13/ 720] blk.0.attn_v.weight                  - [  2560,    512,      1,      1], type =    f16, converting to q8_0 .. size =     2.50 MiB ->     1.33 MiB
[  14/ 720] blk.0.ffn_down.weight                - [ 10240,   2560,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[  15/ 720] blk.0.ffn_gate.weight                - [  2560,  10240,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[  16/ 720] blk.0.ffn_norm.weight                - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[  17/ 720] blk.0.ffn_up.weight                  - [  2560,  10240,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[  18/ 720] blk.0.inp_gate.weight                - [  2560,    256,      1,      1], type =    f16, converting to q8_0 .. size =     1.25 MiB ->     0.66 MiB
[  19/ 720] blk.0.layer_output_scale.weight      - [     1,      1,      1,      1], type =    f32, size =    0.000 MiB
[  20/ 720] blk.0.post_attention_norm.weight     - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[  21/ 720] blk.0.post_ffw_norm.weight           - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[  22/ 720] blk.0.post_norm.weight               - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[  23/ 720] blk.0.proj.weight                    - [   256,   2560,      1,      1], type =    f16, converting to q8_0 .. size =     1.25 MiB ->     0.66 MiB
[  24/ 720] blk.1.attn_k.weight                  - [  2560,    512,      1,      1], type =    f16, converting to q8_0 .. size =     2.50 MiB ->     1.33 MiB
[  25/ 720] blk.1.attn_k_norm.weight             - [   256,      1,      1,      1], type =    f32, size =    0.001 MiB
[  26/ 720] blk.1.attn_norm.weight               - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[  27/ 720] blk.1.attn_output.weight             - [  2048,   2560,      1,      1], type =    f16, converting to q8_0 .. size =    10.00 MiB ->     5.31 MiB
[  28/ 720] blk.1.attn_q.weight                  - [  2560,   2048,      1,      1], type =    f16, converting to q8_0 .. size =    10.00 MiB ->     5.31 MiB
[  29/ 720] blk.1.attn_q_norm.weight             - [   256,      1,      1,      1], type =    f32, size =    0.001 MiB
[  30/ 720] blk.1.attn_v.weight                  - [  2560,    512,      1,      1], type =    f16, converting to q8_0 .. size =     2.50 MiB ->     1.33 MiB
[  31/ 720] blk.1.ffn_down.weight                - [ 10240,   2560,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[  32/ 720] blk.1.ffn_gate.weight                - [  2560,  10240,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[  33/ 720] blk.1.ffn_norm.weight                - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[  34/ 720] blk.1.ffn_up.weight                  - [  2560,  10240,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[  35/ 720] blk.1.inp_gate.weight                - [  2560,    256,      1,      1], type =    f16, converting to q8_0 .. size =     1.25 MiB ->     0.66 MiB
[  36/ 720] blk.1.layer_output_scale.weight      - [     1,      1,      1,      1], type =    f32, size =    0.000 MiB
[  37/ 720] blk.1.post_attention_norm.weight     - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[  38/ 720] blk.1.post_ffw_norm.weight           - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[  39/ 720] blk.1.post_norm.weight               - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[  40/ 720] blk.1.proj.weight                    - [   256,   2560,      1,      1], type =    f16, converting to q8_0 .. size =     1.25 MiB ->     0.66 MiB
[  41/ 720] blk.2.attn_k.weight                  - [  2560,    512,      1,      1], type =    f16, converting to q8_0 .. size =     2.50 MiB ->     1.33 MiB
[  42/ 720] blk.2.attn_k_norm.weight             - [   256,      1,      1,      1], type =    f32, size =    0.001 MiB
[  43/ 720] blk.2.attn_norm.weight               - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[  44/ 720] blk.2.attn_output.weight             - [  2048,   2560,      1,      1], type =    f16, converting to q8_0 .. size =    10.00 MiB ->     5.31 MiB
[  45/ 720] blk.2.attn_q.weight                  - [  2560,   2048,      1,      1], type =    f16, converting to q8_0 .. size =    10.00 MiB ->     5.31 MiB
[  46/ 720] blk.2.attn_q_norm.weight             - [   256,      1,      1,      1], type =    f32, size =    0.001 MiB
[  47/ 720] blk.2.attn_v.weight                  - [  2560,    512,      1,      1], type =    f16, converting to q8_0 .. size =     2.50 MiB ->     1.33 MiB
[  48/ 720] blk.2.ffn_down.weight                - [ 10240,   2560,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[  49/ 720] blk.2.ffn_gate.weight                - [  2560,  10240,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[  50/ 720] blk.2.ffn_norm.weight                - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[  51/ 720] blk.2.ffn_up.weight                  - [  2560,  10240,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[  52/ 720] blk.2.inp_gate.weight                - [  2560,    256,      1,      1], type =    f16, converting to q8_0 .. size =     1.25 MiB ->     0.66 MiB
[  53/ 720] blk.2.layer_output_scale.weight      - [     1,      1,      1,      1], type =    f32, size =    0.000 MiB
[  54/ 720] blk.2.post_attention_norm.weight     - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[  55/ 720] blk.2.post_ffw_norm.weight           - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[  56/ 720] blk.2.post_norm.weight               - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[  57/ 720] blk.2.proj.weight                    - [   256,   2560,      1,      1], type =    f16, converting to q8_0 .. size =     1.25 MiB ->     0.66 MiB
[  58/ 720] blk.3.attn_k.weight                  - [  2560,    512,      1,      1], type =    f16, converting to q8_0 .. size =     2.50 MiB ->     1.33 MiB
[  59/ 720] blk.3.attn_k_norm.weight             - [   256,      1,      1,      1], type =    f32, size =    0.001 MiB
[  60/ 720] blk.3.attn_norm.weight               - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[  61/ 720] blk.3.attn_output.weight             - [  2048,   2560,      1,      1], type =    f16, converting to q8_0 .. size =    10.00 MiB ->     5.31 MiB
[  62/ 720] blk.3.attn_q.weight                  - [  2560,   2048,      1,      1], type =    f16, converting to q8_0 .. size =    10.00 MiB ->     5.31 MiB
[  63/ 720] blk.3.attn_q_norm.weight             - [   256,      1,      1,      1], type =    f32, size =    0.001 MiB
[  64/ 720] blk.3.attn_v.weight                  - [  2560,    512,      1,      1], type =    f16, converting to q8_0 .. size =     2.50 MiB ->     1.33 MiB
[  65/ 720] blk.3.ffn_down.weight                - [ 10240,   2560,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[  66/ 720] blk.3.ffn_gate.weight                - [  2560,  10240,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[  67/ 720] blk.3.ffn_norm.weight                - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[  68/ 720] blk.3.ffn_up.weight                  - [  2560,  10240,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[  69/ 720] blk.3.inp_gate.weight                - [  2560,    256,      1,      1], type =    f16, converting to q8_0 .. size =     1.25 MiB ->     0.66 MiB
[  70/ 720] blk.3.layer_output_scale.weight      - [     1,      1,      1,      1], type =    f32, size =    0.000 MiB
[  71/ 720] blk.3.post_attention_norm.weight     - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[  72/ 720] blk.3.post_ffw_norm.weight           - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[  73/ 720] blk.3.post_norm.weight               - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[  74/ 720] blk.3.proj.weight                    - [   256,   2560,      1,      1], type =    f16, converting to q8_0 .. size =     1.25 MiB ->     0.66 MiB
[  75/ 720] blk.4.attn_k.weight                  - [  2560,    512,      1,      1], type =    f16, converting to q8_0 .. size =     2.50 MiB ->     1.33 MiB
[  76/ 720] blk.4.attn_k_norm.weight             - [   256,      1,      1,      1], type =    f32, size =    0.001 MiB
[  77/ 720] blk.4.attn_norm.weight               - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[  78/ 720] blk.4.attn_output.weight             - [  2048,   2560,      1,      1], type =    f16, converting to q8_0 .. size =    10.00 MiB ->     5.31 MiB
[  79/ 720] blk.4.attn_q.weight                  - [  2560,   2048,      1,      1], type =    f16, converting to q8_0 .. size =    10.00 MiB ->     5.31 MiB
[  80/ 720] blk.4.attn_q_norm.weight             - [   256,      1,      1,      1], type =    f32, size =    0.001 MiB
[  81/ 720] blk.4.attn_v.weight                  - [  2560,    512,      1,      1], type =    f16, converting to q8_0 .. size =     2.50 MiB ->     1.33 MiB
[  82/ 720] blk.4.ffn_down.weight                - [ 10240,   2560,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[  83/ 720] blk.4.ffn_gate.weight                - [  2560,  10240,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[  84/ 720] blk.4.ffn_norm.weight                - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[  85/ 720] blk.4.ffn_up.weight                  - [  2560,  10240,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[  86/ 720] blk.4.inp_gate.weight                - [  2560,    256,      1,      1], type =    f16, converting to q8_0 .. size =     1.25 MiB ->     0.66 MiB
[  87/ 720] blk.4.layer_output_scale.weight      - [     1,      1,      1,      1], type =    f32, size =    0.000 MiB
[  88/ 720] blk.4.post_attention_norm.weight     - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[  89/ 720] blk.4.post_ffw_norm.weight           - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[  90/ 720] blk.4.post_norm.weight               - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[  91/ 720] blk.4.proj.weight                    - [   256,   2560,      1,      1], type =    f16, converting to q8_0 .. size =     1.25 MiB ->     0.66 MiB
[  92/ 720] blk.5.attn_k.weight                  - [  2560,   1024,      1,      1], type =    f16, converting to q8_0 .. size =     5.00 MiB ->     2.66 MiB
[  93/ 720] blk.5.attn_k_norm.weight             - [   512,      1,      1,      1], type =    f32, size =    0.002 MiB
[  94/ 720] blk.5.attn_norm.weight               - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[  95/ 720] blk.5.attn_output.weight             - [  4096,   2560,      1,      1], type =    f16, converting to q8_0 .. size =    20.00 MiB ->    10.62 MiB
[  96/ 720] blk.5.attn_q.weight                  - [  2560,   4096,      1,      1], type =    f16, converting to q8_0 .. size =    20.00 MiB ->    10.62 MiB
[  97/ 720] blk.5.attn_q_norm.weight             - [   512,      1,      1,      1], type =    f32, size =    0.002 MiB
[  98/ 720] blk.5.attn_v.weight                  - [  2560,   1024,      1,      1], type =    f16, converting to q8_0 .. size =     5.00 MiB ->     2.66 MiB
[  99/ 720] blk.5.ffn_down.weight                - [ 10240,   2560,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 100/ 720] blk.5.ffn_gate.weight                - [  2560,  10240,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 101/ 720] blk.5.ffn_norm.weight                - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 102/ 720] blk.5.ffn_up.weight                  - [  2560,  10240,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 103/ 720] blk.5.inp_gate.weight                - [  2560,    256,      1,      1], type =    f16, converting to q8_0 .. size =     1.25 MiB ->     0.66 MiB
[ 104/ 720] blk.5.layer_output_scale.weight      - [     1,      1,      1,      1], type =    f32, size =    0.000 MiB
[ 105/ 720] blk.5.post_attention_norm.weight     - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 106/ 720] blk.5.post_ffw_norm.weight           - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 107/ 720] blk.5.post_norm.weight               - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 108/ 720] blk.5.proj.weight                    - [   256,   2560,      1,      1], type =    f16, converting to q8_0 .. size =     1.25 MiB ->     0.66 MiB
[ 109/ 720] blk.6.attn_k.weight                  - [  2560,    512,      1,      1], type =    f16, converting to q8_0 .. size =     2.50 MiB ->     1.33 MiB
[ 110/ 720] blk.6.attn_k_norm.weight             - [   256,      1,      1,      1], type =    f32, size =    0.001 MiB
[ 111/ 720] blk.6.attn_norm.weight               - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 112/ 720] blk.6.attn_output.weight             - [  2048,   2560,      1,      1], type =    f16, converting to q8_0 .. size =    10.00 MiB ->     5.31 MiB
[ 113/ 720] blk.6.attn_q.weight                  - [  2560,   2048,      1,      1], type =    f16, converting to q8_0 .. size =    10.00 MiB ->     5.31 MiB
[ 114/ 720] blk.6.attn_q_norm.weight             - [   256,      1,      1,      1], type =    f32, size =    0.001 MiB
[ 115/ 720] blk.6.attn_v.weight                  - [  2560,    512,      1,      1], type =    f16, converting to q8_0 .. size =     2.50 MiB ->     1.33 MiB
[ 116/ 720] blk.6.ffn_down.weight                - [ 10240,   2560,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 117/ 720] blk.6.ffn_gate.weight                - [  2560,  10240,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 118/ 720] blk.6.ffn_norm.weight                - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 119/ 720] blk.6.ffn_up.weight                  - [  2560,  10240,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 120/ 720] blk.6.inp_gate.weight                - [  2560,    256,      1,      1], type =    f16, converting to q8_0 .. size =     1.25 MiB ->     0.66 MiB
[ 121/ 720] blk.6.layer_output_scale.weight      - [     1,      1,      1,      1], type =    f32, size =    0.000 MiB
[ 122/ 720] blk.6.post_attention_norm.weight     - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 123/ 720] blk.6.post_ffw_norm.weight           - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 124/ 720] blk.6.post_norm.weight               - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 125/ 720] blk.6.proj.weight                    - [   256,   2560,      1,      1], type =    f16, converting to q8_0 .. size =     1.25 MiB ->     0.66 MiB
[ 126/ 720] blk.7.attn_k.weight                  - [  2560,    512,      1,      1], type =    f16, converting to q8_0 .. size =     2.50 MiB ->     1.33 MiB
[ 127/ 720] blk.7.attn_k_norm.weight             - [   256,      1,      1,      1], type =    f32, size =    0.001 MiB
[ 128/ 720] blk.7.attn_norm.weight               - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 129/ 720] blk.7.attn_output.weight             - [  2048,   2560,      1,      1], type =    f16, converting to q8_0 .. size =    10.00 MiB ->     5.31 MiB
[ 130/ 720] blk.7.attn_q.weight                  - [  2560,   2048,      1,      1], type =    f16, converting to q8_0 .. size =    10.00 MiB ->     5.31 MiB
[ 131/ 720] blk.7.attn_q_norm.weight             - [   256,      1,      1,      1], type =    f32, size =    0.001 MiB
[ 132/ 720] blk.7.attn_v.weight                  - [  2560,    512,      1,      1], type =    f16, converting to q8_0 .. size =     2.50 MiB ->     1.33 MiB
[ 133/ 720] blk.7.ffn_down.weight                - [ 10240,   2560,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 134/ 720] blk.7.ffn_gate.weight                - [  2560,  10240,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 135/ 720] blk.7.ffn_norm.weight                - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 136/ 720] blk.7.ffn_up.weight                  - [  2560,  10240,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 137/ 720] blk.7.inp_gate.weight                - [  2560,    256,      1,      1], type =    f16, converting to q8_0 .. size =     1.25 MiB ->     0.66 MiB
[ 138/ 720] blk.7.layer_output_scale.weight      - [     1,      1,      1,      1], type =    f32, size =    0.000 MiB
[ 139/ 720] blk.7.post_attention_norm.weight     - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 140/ 720] blk.7.post_ffw_norm.weight           - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 141/ 720] blk.7.post_norm.weight               - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 142/ 720] blk.7.proj.weight                    - [   256,   2560,      1,      1], type =    f16, converting to q8_0 .. size =     1.25 MiB ->     0.66 MiB
[ 143/ 720] blk.8.attn_k.weight                  - [  2560,    512,      1,      1], type =    f16, converting to q8_0 .. size =     2.50 MiB ->     1.33 MiB
[ 144/ 720] blk.8.attn_k_norm.weight             - [   256,      1,      1,      1], type =    f32, size =    0.001 MiB
[ 145/ 720] blk.8.attn_norm.weight               - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 146/ 720] blk.8.attn_output.weight             - [  2048,   2560,      1,      1], type =    f16, converting to q8_0 .. size =    10.00 MiB ->     5.31 MiB
[ 147/ 720] blk.8.attn_q.weight                  - [  2560,   2048,      1,      1], type =    f16, converting to q8_0 .. size =    10.00 MiB ->     5.31 MiB
[ 148/ 720] blk.8.attn_q_norm.weight             - [   256,      1,      1,      1], type =    f32, size =    0.001 MiB
[ 149/ 720] blk.8.attn_v.weight                  - [  2560,    512,      1,      1], type =    f16, converting to q8_0 .. size =     2.50 MiB ->     1.33 MiB
[ 150/ 720] blk.8.ffn_down.weight                - [ 10240,   2560,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 151/ 720] blk.8.ffn_gate.weight                - [  2560,  10240,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 152/ 720] blk.8.ffn_norm.weight                - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 153/ 720] blk.8.ffn_up.weight                  - [  2560,  10240,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 154/ 720] blk.8.inp_gate.weight                - [  2560,    256,      1,      1], type =    f16, converting to q8_0 .. size =     1.25 MiB ->     0.66 MiB
[ 155/ 720] blk.8.layer_output_scale.weight      - [     1,      1,      1,      1], type =    f32, size =    0.000 MiB
[ 156/ 720] blk.8.post_attention_norm.weight     - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 157/ 720] blk.8.post_ffw_norm.weight           - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 158/ 720] blk.8.post_norm.weight               - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 159/ 720] blk.8.proj.weight                    - [   256,   2560,      1,      1], type =    f16, converting to q8_0 .. size =     1.25 MiB ->     0.66 MiB
[ 160/ 720] blk.9.attn_k.weight                  - [  2560,    512,      1,      1], type =    f16, converting to q8_0 .. size =     2.50 MiB ->     1.33 MiB
[ 161/ 720] blk.9.attn_k_norm.weight             - [   256,      1,      1,      1], type =    f32, size =    0.001 MiB
[ 162/ 720] blk.9.attn_norm.weight               - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 163/ 720] blk.9.attn_output.weight             - [  2048,   2560,      1,      1], type =    f16, converting to q8_0 .. size =    10.00 MiB ->     5.31 MiB
[ 164/ 720] blk.9.attn_q.weight                  - [  2560,   2048,      1,      1], type =    f16, converting to q8_0 .. size =    10.00 MiB ->     5.31 MiB
[ 165/ 720] blk.9.attn_q_norm.weight             - [   256,      1,      1,      1], type =    f32, size =    0.001 MiB
[ 166/ 720] blk.9.attn_v.weight                  - [  2560,    512,      1,      1], type =    f16, converting to q8_0 .. size =     2.50 MiB ->     1.33 MiB
[ 167/ 720] blk.9.ffn_down.weight                - [ 10240,   2560,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 168/ 720] blk.9.ffn_gate.weight                - [  2560,  10240,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 169/ 720] blk.9.ffn_norm.weight                - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 170/ 720] blk.9.ffn_up.weight                  - [  2560,  10240,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 171/ 720] blk.9.inp_gate.weight                - [  2560,    256,      1,      1], type =    f16, converting to q8_0 .. size =     1.25 MiB ->     0.66 MiB
[ 172/ 720] blk.9.layer_output_scale.weight      - [     1,      1,      1,      1], type =    f32, size =    0.000 MiB
[ 173/ 720] blk.9.post_attention_norm.weight     - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 174/ 720] blk.9.post_ffw_norm.weight           - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 175/ 720] blk.9.post_norm.weight               - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 176/ 720] blk.9.proj.weight                    - [   256,   2560,      1,      1], type =    f16, converting to q8_0 .. size =     1.25 MiB ->     0.66 MiB
[ 177/ 720] blk.10.attn_k.weight                 - [  2560,    512,      1,      1], type =    f16, converting to q8_0 .. size =     2.50 MiB ->     1.33 MiB
[ 178/ 720] blk.10.attn_k_norm.weight            - [   256,      1,      1,      1], type =    f32, size =    0.001 MiB
[ 179/ 720] blk.10.attn_norm.weight              - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 180/ 720] blk.10.attn_output.weight            - [  2048,   2560,      1,      1], type =    f16, converting to q8_0 .. size =    10.00 MiB ->     5.31 MiB
[ 181/ 720] blk.10.attn_q.weight                 - [  2560,   2048,      1,      1], type =    f16, converting to q8_0 .. size =    10.00 MiB ->     5.31 MiB
[ 182/ 720] blk.10.attn_q_norm.weight            - [   256,      1,      1,      1], type =    f32, size =    0.001 MiB
[ 183/ 720] blk.10.attn_v.weight                 - [  2560,    512,      1,      1], type =    f16, converting to q8_0 .. size =     2.50 MiB ->     1.33 MiB
[ 184/ 720] blk.10.ffn_down.weight               - [ 10240,   2560,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 185/ 720] blk.10.ffn_gate.weight               - [  2560,  10240,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 186/ 720] blk.10.ffn_norm.weight               - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 187/ 720] blk.10.ffn_up.weight                 - [  2560,  10240,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 188/ 720] blk.10.inp_gate.weight               - [  2560,    256,      1,      1], type =    f16, converting to q8_0 .. size =     1.25 MiB ->     0.66 MiB
[ 189/ 720] blk.10.layer_output_scale.weight     - [     1,      1,      1,      1], type =    f32, size =    0.000 MiB
[ 190/ 720] blk.10.post_attention_norm.weight    - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 191/ 720] blk.10.post_ffw_norm.weight          - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 192/ 720] blk.10.post_norm.weight              - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 193/ 720] blk.10.proj.weight                   - [   256,   2560,      1,      1], type =    f16, converting to q8_0 .. size =     1.25 MiB ->     0.66 MiB
[ 194/ 720] blk.11.attn_k.weight                 - [  2560,   1024,      1,      1], type =    f16, converting to q8_0 .. size =     5.00 MiB ->     2.66 MiB
[ 195/ 720] blk.11.attn_k_norm.weight            - [   512,      1,      1,      1], type =    f32, size =    0.002 MiB
[ 196/ 720] blk.11.attn_norm.weight              - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 197/ 720] blk.11.attn_output.weight            - [  4096,   2560,      1,      1], type =    f16, converting to q8_0 .. size =    20.00 MiB ->    10.62 MiB
[ 198/ 720] blk.11.attn_q.weight                 - [  2560,   4096,      1,      1], type =    f16, converting to q8_0 .. size =    20.00 MiB ->    10.62 MiB
[ 199/ 720] blk.11.attn_q_norm.weight            - [   512,      1,      1,      1], type =    f32, size =    0.002 MiB
[ 200/ 720] blk.11.attn_v.weight                 - [  2560,   1024,      1,      1], type =    f16, converting to q8_0 .. size =     5.00 MiB ->     2.66 MiB
[ 201/ 720] blk.11.ffn_down.weight               - [ 10240,   2560,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 202/ 720] blk.11.ffn_gate.weight               - [  2560,  10240,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 203/ 720] blk.11.ffn_norm.weight               - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 204/ 720] blk.11.ffn_up.weight                 - [  2560,  10240,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 205/ 720] blk.11.inp_gate.weight               - [  2560,    256,      1,      1], type =    f16, converting to q8_0 .. size =     1.25 MiB ->     0.66 MiB
[ 206/ 720] blk.11.layer_output_scale.weight     - [     1,      1,      1,      1], type =    f32, size =    0.000 MiB
[ 207/ 720] blk.11.post_attention_norm.weight    - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 208/ 720] blk.11.post_ffw_norm.weight          - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 209/ 720] blk.11.post_norm.weight              - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 210/ 720] blk.11.proj.weight                   - [   256,   2560,      1,      1], type =    f16, converting to q8_0 .. size =     1.25 MiB ->     0.66 MiB
[ 211/ 720] blk.12.attn_k.weight                 - [  2560,    512,      1,      1], type =    f16, converting to q8_0 .. size =     2.50 MiB ->     1.33 MiB
[ 212/ 720] blk.12.attn_k_norm.weight            - [   256,      1,      1,      1], type =    f32, size =    0.001 MiB
[ 213/ 720] blk.12.attn_norm.weight              - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 214/ 720] blk.12.attn_output.weight            - [  2048,   2560,      1,      1], type =    f16, converting to q8_0 .. size =    10.00 MiB ->     5.31 MiB
[ 215/ 720] blk.12.attn_q.weight                 - [  2560,   2048,      1,      1], type =    f16, converting to q8_0 .. size =    10.00 MiB ->     5.31 MiB
[ 216/ 720] blk.12.attn_q_norm.weight            - [   256,      1,      1,      1], type =    f32, size =    0.001 MiB
[ 217/ 720] blk.12.attn_v.weight                 - [  2560,    512,      1,      1], type =    f16, converting to q8_0 .. size =     2.50 MiB ->     1.33 MiB
[ 218/ 720] blk.12.ffn_down.weight               - [ 10240,   2560,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 219/ 720] blk.12.ffn_gate.weight               - [  2560,  10240,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 220/ 720] blk.12.ffn_norm.weight               - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 221/ 720] blk.12.ffn_up.weight                 - [  2560,  10240,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 222/ 720] blk.12.inp_gate.weight               - [  2560,    256,      1,      1], type =    f16, converting to q8_0 .. size =     1.25 MiB ->     0.66 MiB
[ 223/ 720] blk.12.layer_output_scale.weight     - [     1,      1,      1,      1], type =    f32, size =    0.000 MiB
[ 224/ 720] blk.12.post_attention_norm.weight    - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 225/ 720] blk.12.post_ffw_norm.weight          - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 226/ 720] blk.12.post_norm.weight              - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 227/ 720] blk.12.proj.weight                   - [   256,   2560,      1,      1], type =    f16, converting to q8_0 .. size =     1.25 MiB ->     0.66 MiB
[ 228/ 720] blk.13.attn_k.weight                 - [  2560,    512,      1,      1], type =    f16, converting to q8_0 .. size =     2.50 MiB ->     1.33 MiB
[ 229/ 720] blk.13.attn_k_norm.weight            - [   256,      1,      1,      1], type =    f32, size =    0.001 MiB
[ 230/ 720] blk.13.attn_norm.weight              - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 231/ 720] blk.13.attn_output.weight            - [  2048,   2560,      1,      1], type =    f16, converting to q8_0 .. size =    10.00 MiB ->     5.31 MiB
[ 232/ 720] blk.13.attn_q.weight                 - [  2560,   2048,      1,      1], type =    f16, converting to q8_0 .. size =    10.00 MiB ->     5.31 MiB
[ 233/ 720] blk.13.attn_q_norm.weight            - [   256,      1,      1,      1], type =    f32, size =    0.001 MiB
[ 234/ 720] blk.13.attn_v.weight                 - [  2560,    512,      1,      1], type =    f16, converting to q8_0 .. size =     2.50 MiB ->     1.33 MiB
[ 235/ 720] blk.13.ffn_down.weight               - [ 10240,   2560,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 236/ 720] blk.13.ffn_gate.weight               - [  2560,  10240,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 237/ 720] blk.13.ffn_norm.weight               - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 238/ 720] blk.13.ffn_up.weight                 - [  2560,  10240,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 239/ 720] blk.13.inp_gate.weight               - [  2560,    256,      1,      1], type =    f16, converting to q8_0 .. size =     1.25 MiB ->     0.66 MiB
[ 240/ 720] blk.13.layer_output_scale.weight     - [     1,      1,      1,      1], type =    f32, size =    0.000 MiB
[ 241/ 720] blk.13.post_attention_norm.weight    - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 242/ 720] blk.13.post_ffw_norm.weight          - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 243/ 720] blk.13.post_norm.weight              - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 244/ 720] blk.13.proj.weight                   - [   256,   2560,      1,      1], type =    f16, converting to q8_0 .. size =     1.25 MiB ->     0.66 MiB
[ 245/ 720] blk.14.attn_k.weight                 - [  2560,    512,      1,      1], type =    f16, converting to q8_0 .. size =     2.50 MiB ->     1.33 MiB
[ 246/ 720] blk.14.attn_k_norm.weight            - [   256,      1,      1,      1], type =    f32, size =    0.001 MiB
[ 247/ 720] blk.14.attn_norm.weight              - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 248/ 720] blk.14.attn_output.weight            - [  2048,   2560,      1,      1], type =    f16, converting to q8_0 .. size =    10.00 MiB ->     5.31 MiB
[ 249/ 720] blk.14.attn_q.weight                 - [  2560,   2048,      1,      1], type =    f16, converting to q8_0 .. size =    10.00 MiB ->     5.31 MiB
[ 250/ 720] blk.14.attn_q_norm.weight            - [   256,      1,      1,      1], type =    f32, size =    0.001 MiB
[ 251/ 720] blk.14.attn_v.weight                 - [  2560,    512,      1,      1], type =    f16, converting to q8_0 .. size =     2.50 MiB ->     1.33 MiB
[ 252/ 720] blk.14.ffn_down.weight               - [ 10240,   2560,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 253/ 720] blk.14.ffn_gate.weight               - [  2560,  10240,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 254/ 720] blk.14.ffn_norm.weight               - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 255/ 720] blk.14.ffn_up.weight                 - [  2560,  10240,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 256/ 720] blk.14.inp_gate.weight               - [  2560,    256,      1,      1], type =    f16, converting to q8_0 .. size =     1.25 MiB ->     0.66 MiB
[ 257/ 720] blk.14.layer_output_scale.weight     - [     1,      1,      1,      1], type =    f32, size =    0.000 MiB
[ 258/ 720] blk.14.post_attention_norm.weight    - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 259/ 720] blk.14.post_ffw_norm.weight          - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 260/ 720] blk.14.post_norm.weight              - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 261/ 720] blk.14.proj.weight                   - [   256,   2560,      1,      1], type =    f16, converting to q8_0 .. size =     1.25 MiB ->     0.66 MiB
[ 262/ 720] blk.15.attn_k.weight                 - [  2560,    512,      1,      1], type =    f16, converting to q8_0 .. size =     2.50 MiB ->     1.33 MiB
[ 263/ 720] blk.15.attn_k_norm.weight            - [   256,      1,      1,      1], type =    f32, size =    0.001 MiB
[ 264/ 720] blk.15.attn_norm.weight              - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 265/ 720] blk.15.attn_output.weight            - [  2048,   2560,      1,      1], type =    f16, converting to q8_0 .. size =    10.00 MiB ->     5.31 MiB
[ 266/ 720] blk.15.attn_q.weight                 - [  2560,   2048,      1,      1], type =    f16, converting to q8_0 .. size =    10.00 MiB ->     5.31 MiB
[ 267/ 720] blk.15.attn_q_norm.weight            - [   256,      1,      1,      1], type =    f32, size =    0.001 MiB
[ 268/ 720] blk.15.attn_v.weight                 - [  2560,    512,      1,      1], type =    f16, converting to q8_0 .. size =     2.50 MiB ->     1.33 MiB
[ 269/ 720] blk.15.ffn_down.weight               - [ 10240,   2560,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 270/ 720] blk.15.ffn_gate.weight               - [  2560,  10240,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 271/ 720] blk.15.ffn_norm.weight               - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 272/ 720] blk.15.ffn_up.weight                 - [  2560,  10240,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 273/ 720] blk.15.inp_gate.weight               - [  2560,    256,      1,      1], type =    f16, converting to q8_0 .. size =     1.25 MiB ->     0.66 MiB
[ 274/ 720] blk.15.layer_output_scale.weight     - [     1,      1,      1,      1], type =    f32, size =    0.000 MiB
[ 275/ 720] blk.15.post_attention_norm.weight    - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 276/ 720] blk.15.post_ffw_norm.weight          - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 277/ 720] blk.15.post_norm.weight              - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 278/ 720] blk.15.proj.weight                   - [   256,   2560,      1,      1], type =    f16, converting to q8_0 .. size =     1.25 MiB ->     0.66 MiB
[ 279/ 720] blk.16.attn_k.weight                 - [  2560,    512,      1,      1], type =    f16, converting to q8_0 .. size =     2.50 MiB ->     1.33 MiB
[ 280/ 720] blk.16.attn_k_norm.weight            - [   256,      1,      1,      1], type =    f32, size =    0.001 MiB
[ 281/ 720] blk.16.attn_norm.weight              - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 282/ 720] blk.16.attn_output.weight            - [  2048,   2560,      1,      1], type =    f16, converting to q8_0 .. size =    10.00 MiB ->     5.31 MiB
[ 283/ 720] blk.16.attn_q.weight                 - [  2560,   2048,      1,      1], type =    f16, converting to q8_0 .. size =    10.00 MiB ->     5.31 MiB
[ 284/ 720] blk.16.attn_q_norm.weight            - [   256,      1,      1,      1], type =    f32, size =    0.001 MiB
[ 285/ 720] blk.16.attn_v.weight                 - [  2560,    512,      1,      1], type =    f16, converting to q8_0 .. size =     2.50 MiB ->     1.33 MiB
[ 286/ 720] blk.16.ffn_down.weight               - [ 10240,   2560,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 287/ 720] blk.16.ffn_gate.weight               - [  2560,  10240,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 288/ 720] blk.16.ffn_norm.weight               - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 289/ 720] blk.16.ffn_up.weight                 - [  2560,  10240,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 290/ 720] blk.16.inp_gate.weight               - [  2560,    256,      1,      1], type =    f16, converting to q8_0 .. size =     1.25 MiB ->     0.66 MiB
[ 291/ 720] blk.16.layer_output_scale.weight     - [     1,      1,      1,      1], type =    f32, size =    0.000 MiB
[ 292/ 720] blk.16.post_attention_norm.weight    - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 293/ 720] blk.16.post_ffw_norm.weight          - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 294/ 720] blk.16.post_norm.weight              - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 295/ 720] blk.16.proj.weight                   - [   256,   2560,      1,      1], type =    f16, converting to q8_0 .. size =     1.25 MiB ->     0.66 MiB
[ 296/ 720] blk.17.attn_k.weight                 - [  2560,   1024,      1,      1], type =    f16, converting to q8_0 .. size =     5.00 MiB ->     2.66 MiB
[ 297/ 720] blk.17.attn_k_norm.weight            - [   512,      1,      1,      1], type =    f32, size =    0.002 MiB
[ 298/ 720] blk.17.attn_norm.weight              - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 299/ 720] blk.17.attn_output.weight            - [  4096,   2560,      1,      1], type =    f16, converting to q8_0 .. size =    20.00 MiB ->    10.62 MiB
[ 300/ 720] blk.17.attn_q.weight                 - [  2560,   4096,      1,      1], type =    f16, converting to q8_0 .. size =    20.00 MiB ->    10.62 MiB
[ 301/ 720] blk.17.attn_q_norm.weight            - [   512,      1,      1,      1], type =    f32, size =    0.002 MiB
[ 302/ 720] blk.17.attn_v.weight                 - [  2560,   1024,      1,      1], type =    f16, converting to q8_0 .. size =     5.00 MiB ->     2.66 MiB
[ 303/ 720] blk.17.ffn_down.weight               - [ 10240,   2560,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 304/ 720] blk.17.ffn_gate.weight               - [  2560,  10240,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 305/ 720] blk.17.ffn_norm.weight               - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 306/ 720] blk.17.ffn_up.weight                 - [  2560,  10240,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 307/ 720] blk.17.inp_gate.weight               - [  2560,    256,      1,      1], type =    f16, converting to q8_0 .. size =     1.25 MiB ->     0.66 MiB
[ 308/ 720] blk.17.layer_output_scale.weight     - [     1,      1,      1,      1], type =    f32, size =    0.000 MiB
[ 309/ 720] blk.17.post_attention_norm.weight    - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 310/ 720] blk.17.post_ffw_norm.weight          - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 311/ 720] blk.17.post_norm.weight              - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 312/ 720] blk.17.proj.weight                   - [   256,   2560,      1,      1], type =    f16, converting to q8_0 .. size =     1.25 MiB ->     0.66 MiB
[ 313/ 720] blk.18.attn_k.weight                 - [  2560,    512,      1,      1], type =    f16, converting to q8_0 .. size =     2.50 MiB ->     1.33 MiB
[ 314/ 720] blk.18.attn_k_norm.weight            - [   256,      1,      1,      1], type =    f32, size =    0.001 MiB
[ 315/ 720] blk.18.attn_norm.weight              - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 316/ 720] blk.18.attn_output.weight            - [  2048,   2560,      1,      1], type =    f16, converting to q8_0 .. size =    10.00 MiB ->     5.31 MiB
[ 317/ 720] blk.18.attn_q.weight                 - [  2560,   2048,      1,      1], type =    f16, converting to q8_0 .. size =    10.00 MiB ->     5.31 MiB
[ 318/ 720] blk.18.attn_q_norm.weight            - [   256,      1,      1,      1], type =    f32, size =    0.001 MiB
[ 319/ 720] blk.18.attn_v.weight                 - [  2560,    512,      1,      1], type =    f16, converting to q8_0 .. size =     2.50 MiB ->     1.33 MiB
[ 320/ 720] blk.18.ffn_down.weight               - [ 10240,   2560,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 321/ 720] blk.18.ffn_gate.weight               - [  2560,  10240,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 322/ 720] blk.18.ffn_norm.weight               - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 323/ 720] blk.18.ffn_up.weight                 - [  2560,  10240,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 324/ 720] blk.18.inp_gate.weight               - [  2560,    256,      1,      1], type =    f16, converting to q8_0 .. size =     1.25 MiB ->     0.66 MiB
[ 325/ 720] blk.18.layer_output_scale.weight     - [     1,      1,      1,      1], type =    f32, size =    0.000 MiB
[ 326/ 720] blk.18.post_attention_norm.weight    - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 327/ 720] blk.18.post_ffw_norm.weight          - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 328/ 720] blk.18.post_norm.weight              - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 329/ 720] blk.18.proj.weight                   - [   256,   2560,      1,      1], type =    f16, converting to q8_0 .. size =     1.25 MiB ->     0.66 MiB
[ 330/ 720] blk.19.attn_k.weight                 - [  2560,    512,      1,      1], type =    f16, converting to q8_0 .. size =     2.50 MiB ->     1.33 MiB
[ 331/ 720] blk.19.attn_k_norm.weight            - [   256,      1,      1,      1], type =    f32, size =    0.001 MiB
[ 332/ 720] blk.19.attn_norm.weight              - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 333/ 720] blk.19.attn_output.weight            - [  2048,   2560,      1,      1], type =    f16, converting to q8_0 .. size =    10.00 MiB ->     5.31 MiB
[ 334/ 720] blk.19.attn_q.weight                 - [  2560,   2048,      1,      1], type =    f16, converting to q8_0 .. size =    10.00 MiB ->     5.31 MiB
[ 335/ 720] blk.19.attn_q_norm.weight            - [   256,      1,      1,      1], type =    f32, size =    0.001 MiB
[ 336/ 720] blk.19.attn_v.weight                 - [  2560,    512,      1,      1], type =    f16, converting to q8_0 .. size =     2.50 MiB ->     1.33 MiB
[ 337/ 720] blk.19.ffn_down.weight               - [ 10240,   2560,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 338/ 720] blk.19.ffn_gate.weight               - [  2560,  10240,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 339/ 720] blk.19.ffn_norm.weight               - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 340/ 720] blk.19.ffn_up.weight                 - [  2560,  10240,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 341/ 720] blk.19.inp_gate.weight               - [  2560,    256,      1,      1], type =    f16, converting to q8_0 .. size =     1.25 MiB ->     0.66 MiB
[ 342/ 720] blk.19.layer_output_scale.weight     - [     1,      1,      1,      1], type =    f32, size =    0.000 MiB
[ 343/ 720] blk.19.post_attention_norm.weight    - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 344/ 720] blk.19.post_ffw_norm.weight          - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 345/ 720] blk.19.post_norm.weight              - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 346/ 720] blk.19.proj.weight                   - [   256,   2560,      1,      1], type =    f16, converting to q8_0 .. size =     1.25 MiB ->     0.66 MiB
[ 347/ 720] blk.20.attn_k.weight                 - [  2560,    512,      1,      1], type =    f16, converting to q8_0 .. size =     2.50 MiB ->     1.33 MiB
[ 348/ 720] blk.20.attn_k_norm.weight            - [   256,      1,      1,      1], type =    f32, size =    0.001 MiB
[ 349/ 720] blk.20.attn_norm.weight              - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 350/ 720] blk.20.attn_output.weight            - [  2048,   2560,      1,      1], type =    f16, converting to q8_0 .. size =    10.00 MiB ->     5.31 MiB
[ 351/ 720] blk.20.attn_q.weight                 - [  2560,   2048,      1,      1], type =    f16, converting to q8_0 .. size =    10.00 MiB ->     5.31 MiB
[ 352/ 720] blk.20.attn_q_norm.weight            - [   256,      1,      1,      1], type =    f32, size =    0.001 MiB
[ 353/ 720] blk.20.attn_v.weight                 - [  2560,    512,      1,      1], type =    f16, converting to q8_0 .. size =     2.50 MiB ->     1.33 MiB
[ 354/ 720] blk.20.ffn_down.weight               - [ 10240,   2560,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 355/ 720] blk.20.ffn_gate.weight               - [  2560,  10240,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 356/ 720] blk.20.ffn_norm.weight               - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 357/ 720] blk.20.ffn_up.weight                 - [  2560,  10240,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 358/ 720] blk.20.inp_gate.weight               - [  2560,    256,      1,      1], type =    f16, converting to q8_0 .. size =     1.25 MiB ->     0.66 MiB
[ 359/ 720] blk.20.layer_output_scale.weight     - [     1,      1,      1,      1], type =    f32, size =    0.000 MiB
[ 360/ 720] blk.20.post_attention_norm.weight    - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 361/ 720] blk.20.post_ffw_norm.weight          - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 362/ 720] blk.20.post_norm.weight              - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 363/ 720] blk.20.proj.weight                   - [   256,   2560,      1,      1], type =    f16, converting to q8_0 .. size =     1.25 MiB ->     0.66 MiB
[ 364/ 720] blk.21.attn_k.weight                 - [  2560,    512,      1,      1], type =    f16, converting to q8_0 .. size =     2.50 MiB ->     1.33 MiB
[ 365/ 720] blk.21.attn_k_norm.weight            - [   256,      1,      1,      1], type =    f32, size =    0.001 MiB
[ 366/ 720] blk.21.attn_norm.weight              - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 367/ 720] blk.21.attn_output.weight            - [  2048,   2560,      1,      1], type =    f16, converting to q8_0 .. size =    10.00 MiB ->     5.31 MiB
[ 368/ 720] blk.21.attn_q.weight                 - [  2560,   2048,      1,      1], type =    f16, converting to q8_0 .. size =    10.00 MiB ->     5.31 MiB
[ 369/ 720] blk.21.attn_q_norm.weight            - [   256,      1,      1,      1], type =    f32, size =    0.001 MiB
[ 370/ 720] blk.21.attn_v.weight                 - [  2560,    512,      1,      1], type =    f16, converting to q8_0 .. size =     2.50 MiB ->     1.33 MiB
[ 371/ 720] blk.21.ffn_down.weight               - [ 10240,   2560,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 372/ 720] blk.21.ffn_gate.weight               - [  2560,  10240,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 373/ 720] blk.21.ffn_norm.weight               - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 374/ 720] blk.21.ffn_up.weight                 - [  2560,  10240,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 375/ 720] blk.21.inp_gate.weight               - [  2560,    256,      1,      1], type =    f16, converting to q8_0 .. size =     1.25 MiB ->     0.66 MiB
[ 376/ 720] blk.21.layer_output_scale.weight     - [     1,      1,      1,      1], type =    f32, size =    0.000 MiB
[ 377/ 720] blk.21.post_attention_norm.weight    - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 378/ 720] blk.21.post_ffw_norm.weight          - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 379/ 720] blk.21.post_norm.weight              - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 380/ 720] blk.21.proj.weight                   - [   256,   2560,      1,      1], type =    f16, converting to q8_0 .. size =     1.25 MiB ->     0.66 MiB
[ 381/ 720] blk.22.attn_k.weight                 - [  2560,    512,      1,      1], type =    f16, converting to q8_0 .. size =     2.50 MiB ->     1.33 MiB
[ 382/ 720] blk.22.attn_k_norm.weight            - [   256,      1,      1,      1], type =    f32, size =    0.001 MiB
[ 383/ 720] blk.22.attn_norm.weight              - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 384/ 720] blk.22.attn_output.weight            - [  2048,   2560,      1,      1], type =    f16, converting to q8_0 .. size =    10.00 MiB ->     5.31 MiB
[ 385/ 720] blk.22.attn_q.weight                 - [  2560,   2048,      1,      1], type =    f16, converting to q8_0 .. size =    10.00 MiB ->     5.31 MiB
[ 386/ 720] blk.22.attn_q_norm.weight            - [   256,      1,      1,      1], type =    f32, size =    0.001 MiB
[ 387/ 720] blk.22.attn_v.weight                 - [  2560,    512,      1,      1], type =    f16, converting to q8_0 .. size =     2.50 MiB ->     1.33 MiB
[ 388/ 720] blk.22.ffn_down.weight               - [ 10240,   2560,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 389/ 720] blk.22.ffn_gate.weight               - [  2560,  10240,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 390/ 720] blk.22.ffn_norm.weight               - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 391/ 720] blk.22.ffn_up.weight                 - [  2560,  10240,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 392/ 720] blk.22.inp_gate.weight               - [  2560,    256,      1,      1], type =    f16, converting to q8_0 .. size =     1.25 MiB ->     0.66 MiB
[ 393/ 720] blk.22.layer_output_scale.weight     - [     1,      1,      1,      1], type =    f32, size =    0.000 MiB
[ 394/ 720] blk.22.post_attention_norm.weight    - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 395/ 720] blk.22.post_ffw_norm.weight          - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 396/ 720] blk.22.post_norm.weight              - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 397/ 720] blk.22.proj.weight                   - [   256,   2560,      1,      1], type =    f16, converting to q8_0 .. size =     1.25 MiB ->     0.66 MiB
[ 398/ 720] blk.23.attn_k.weight                 - [  2560,   1024,      1,      1], type =    f16, converting to q8_0 .. size =     5.00 MiB ->     2.66 MiB
[ 399/ 720] blk.23.attn_k_norm.weight            - [   512,      1,      1,      1], type =    f32, size =    0.002 MiB
[ 400/ 720] blk.23.attn_norm.weight              - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 401/ 720] blk.23.attn_output.weight            - [  4096,   2560,      1,      1], type =    f16, converting to q8_0 .. size =    20.00 MiB ->    10.62 MiB
[ 402/ 720] blk.23.attn_q.weight                 - [  2560,   4096,      1,      1], type =    f16, converting to q8_0 .. size =    20.00 MiB ->    10.62 MiB
[ 403/ 720] blk.23.attn_q_norm.weight            - [   512,      1,      1,      1], type =    f32, size =    0.002 MiB
[ 404/ 720] blk.23.attn_v.weight                 - [  2560,   1024,      1,      1], type =    f16, converting to q8_0 .. size =     5.00 MiB ->     2.66 MiB
[ 405/ 720] blk.23.ffn_down.weight               - [ 10240,   2560,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 406/ 720] blk.23.ffn_gate.weight               - [  2560,  10240,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 407/ 720] blk.23.ffn_norm.weight               - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 408/ 720] blk.23.ffn_up.weight                 - [  2560,  10240,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 409/ 720] blk.23.inp_gate.weight               - [  2560,    256,      1,      1], type =    f16, converting to q8_0 .. size =     1.25 MiB ->     0.66 MiB
[ 410/ 720] blk.23.layer_output_scale.weight     - [     1,      1,      1,      1], type =    f32, size =    0.000 MiB
[ 411/ 720] blk.23.post_attention_norm.weight    - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 412/ 720] blk.23.post_ffw_norm.weight          - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 413/ 720] blk.23.post_norm.weight              - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 414/ 720] blk.23.proj.weight                   - [   256,   2560,      1,      1], type =    f16, converting to q8_0 .. size =     1.25 MiB ->     0.66 MiB
[ 415/ 720] blk.24.attn_k.weight                 - [  2560,    512,      1,      1], type =    f16, converting to q8_0 .. size =     2.50 MiB ->     1.33 MiB
[ 416/ 720] blk.24.attn_k_norm.weight            - [   256,      1,      1,      1], type =    f32, size =    0.001 MiB
[ 417/ 720] blk.24.attn_norm.weight              - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 418/ 720] blk.24.attn_output.weight            - [  2048,   2560,      1,      1], type =    f16, converting to q8_0 .. size =    10.00 MiB ->     5.31 MiB
[ 419/ 720] blk.24.attn_q.weight                 - [  2560,   2048,      1,      1], type =    f16, converting to q8_0 .. size =    10.00 MiB ->     5.31 MiB
[ 420/ 720] blk.24.attn_q_norm.weight            - [   256,      1,      1,      1], type =    f32, size =    0.001 MiB
[ 421/ 720] blk.24.attn_v.weight                 - [  2560,    512,      1,      1], type =    f16, converting to q8_0 .. size =     2.50 MiB ->     1.33 MiB
[ 422/ 720] blk.24.ffn_down.weight               - [ 10240,   2560,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 423/ 720] blk.24.ffn_gate.weight               - [  2560,  10240,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 424/ 720] blk.24.ffn_norm.weight               - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 425/ 720] blk.24.ffn_up.weight                 - [  2560,  10240,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 426/ 720] blk.24.inp_gate.weight               - [  2560,    256,      1,      1], type =    f16, converting to q8_0 .. size =     1.25 MiB ->     0.66 MiB
[ 427/ 720] blk.24.layer_output_scale.weight     - [     1,      1,      1,      1], type =    f32, size =    0.000 MiB
[ 428/ 720] blk.24.post_attention_norm.weight    - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 429/ 720] blk.24.post_ffw_norm.weight          - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 430/ 720] blk.24.post_norm.weight              - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 431/ 720] blk.24.proj.weight                   - [   256,   2560,      1,      1], type =    f16, converting to q8_0 .. size =     1.25 MiB ->     0.66 MiB
[ 432/ 720] blk.25.attn_k.weight                 - [  2560,    512,      1,      1], type =    f16, converting to q8_0 .. size =     2.50 MiB ->     1.33 MiB
[ 433/ 720] blk.25.attn_k_norm.weight            - [   256,      1,      1,      1], type =    f32, size =    0.001 MiB
[ 434/ 720] blk.25.attn_norm.weight              - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 435/ 720] blk.25.attn_output.weight            - [  2048,   2560,      1,      1], type =    f16, converting to q8_0 .. size =    10.00 MiB ->     5.31 MiB
[ 436/ 720] blk.25.attn_q.weight                 - [  2560,   2048,      1,      1], type =    f16, converting to q8_0 .. size =    10.00 MiB ->     5.31 MiB
[ 437/ 720] blk.25.attn_q_norm.weight            - [   256,      1,      1,      1], type =    f32, size =    0.001 MiB
[ 438/ 720] blk.25.attn_v.weight                 - [  2560,    512,      1,      1], type =    f16, converting to q8_0 .. size =     2.50 MiB ->     1.33 MiB
[ 439/ 720] blk.25.ffn_down.weight               - [ 10240,   2560,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 440/ 720] blk.25.ffn_gate.weight               - [  2560,  10240,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 441/ 720] blk.25.ffn_norm.weight               - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 442/ 720] blk.25.ffn_up.weight                 - [  2560,  10240,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 443/ 720] blk.25.inp_gate.weight               - [  2560,    256,      1,      1], type =    f16, converting to q8_0 .. size =     1.25 MiB ->     0.66 MiB
[ 444/ 720] blk.25.layer_output_scale.weight     - [     1,      1,      1,      1], type =    f32, size =    0.000 MiB
[ 445/ 720] blk.25.post_attention_norm.weight    - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 446/ 720] blk.25.post_ffw_norm.weight          - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 447/ 720] blk.25.post_norm.weight              - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 448/ 720] blk.25.proj.weight                   - [   256,   2560,      1,      1], type =    f16, converting to q8_0 .. size =     1.25 MiB ->     0.66 MiB
[ 449/ 720] blk.26.attn_k.weight                 - [  2560,    512,      1,      1], type =    f16, converting to q8_0 .. size =     2.50 MiB ->     1.33 MiB
[ 450/ 720] blk.26.attn_k_norm.weight            - [   256,      1,      1,      1], type =    f32, size =    0.001 MiB
[ 451/ 720] blk.26.attn_norm.weight              - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 452/ 720] blk.26.attn_output.weight            - [  2048,   2560,      1,      1], type =    f16, converting to q8_0 .. size =    10.00 MiB ->     5.31 MiB
[ 453/ 720] blk.26.attn_q.weight                 - [  2560,   2048,      1,      1], type =    f16, converting to q8_0 .. size =    10.00 MiB ->     5.31 MiB
[ 454/ 720] blk.26.attn_q_norm.weight            - [   256,      1,      1,      1], type =    f32, size =    0.001 MiB
[ 455/ 720] blk.26.attn_v.weight                 - [  2560,    512,      1,      1], type =    f16, converting to q8_0 .. size =     2.50 MiB ->     1.33 MiB
[ 456/ 720] blk.26.ffn_down.weight               - [ 10240,   2560,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 457/ 720] blk.26.ffn_gate.weight               - [  2560,  10240,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 458/ 720] blk.26.ffn_norm.weight               - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 459/ 720] blk.26.ffn_up.weight                 - [  2560,  10240,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 460/ 720] blk.26.inp_gate.weight               - [  2560,    256,      1,      1], type =    f16, converting to q8_0 .. size =     1.25 MiB ->     0.66 MiB
[ 461/ 720] blk.26.layer_output_scale.weight     - [     1,      1,      1,      1], type =    f32, size =    0.000 MiB
[ 462/ 720] blk.26.post_attention_norm.weight    - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 463/ 720] blk.26.post_ffw_norm.weight          - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 464/ 720] blk.26.post_norm.weight              - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 465/ 720] blk.26.proj.weight                   - [   256,   2560,      1,      1], type =    f16, converting to q8_0 .. size =     1.25 MiB ->     0.66 MiB
[ 466/ 720] blk.27.attn_k.weight                 - [  2560,    512,      1,      1], type =    f16, converting to q8_0 .. size =     2.50 MiB ->     1.33 MiB
[ 467/ 720] blk.27.attn_k_norm.weight            - [   256,      1,      1,      1], type =    f32, size =    0.001 MiB
[ 468/ 720] blk.27.attn_norm.weight              - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 469/ 720] blk.27.attn_output.weight            - [  2048,   2560,      1,      1], type =    f16, converting to q8_0 .. size =    10.00 MiB ->     5.31 MiB
[ 470/ 720] blk.27.attn_q.weight                 - [  2560,   2048,      1,      1], type =    f16, converting to q8_0 .. size =    10.00 MiB ->     5.31 MiB
[ 471/ 720] blk.27.attn_q_norm.weight            - [   256,      1,      1,      1], type =    f32, size =    0.001 MiB
[ 472/ 720] blk.27.attn_v.weight                 - [  2560,    512,      1,      1], type =    f16, converting to q8_0 .. size =     2.50 MiB ->     1.33 MiB
[ 473/ 720] blk.27.ffn_down.weight               - [ 10240,   2560,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 474/ 720] blk.27.ffn_gate.weight               - [  2560,  10240,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 475/ 720] blk.27.ffn_norm.weight               - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 476/ 720] blk.27.ffn_up.weight                 - [  2560,  10240,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 477/ 720] blk.27.inp_gate.weight               - [  2560,    256,      1,      1], type =    f16, converting to q8_0 .. size =     1.25 MiB ->     0.66 MiB
[ 478/ 720] blk.27.layer_output_scale.weight     - [     1,      1,      1,      1], type =    f32, size =    0.000 MiB
[ 479/ 720] blk.27.post_attention_norm.weight    - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 480/ 720] blk.27.post_ffw_norm.weight          - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 481/ 720] blk.27.post_norm.weight              - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 482/ 720] blk.27.proj.weight                   - [   256,   2560,      1,      1], type =    f16, converting to q8_0 .. size =     1.25 MiB ->     0.66 MiB
[ 483/ 720] blk.28.attn_k.weight                 - [  2560,    512,      1,      1], type =    f16, converting to q8_0 .. size =     2.50 MiB ->     1.33 MiB
[ 484/ 720] blk.28.attn_k_norm.weight            - [   256,      1,      1,      1], type =    f32, size =    0.001 MiB
[ 485/ 720] blk.28.attn_norm.weight              - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 486/ 720] blk.28.attn_output.weight            - [  2048,   2560,      1,      1], type =    f16, converting to q8_0 .. size =    10.00 MiB ->     5.31 MiB
[ 487/ 720] blk.28.attn_q.weight                 - [  2560,   2048,      1,      1], type =    f16, converting to q8_0 .. size =    10.00 MiB ->     5.31 MiB
[ 488/ 720] blk.28.attn_q_norm.weight            - [   256,      1,      1,      1], type =    f32, size =    0.001 MiB
[ 489/ 720] blk.28.attn_v.weight                 - [  2560,    512,      1,      1], type =    f16, converting to q8_0 .. size =     2.50 MiB ->     1.33 MiB
[ 490/ 720] blk.28.ffn_down.weight               - [ 10240,   2560,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 491/ 720] blk.28.ffn_gate.weight               - [  2560,  10240,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 492/ 720] blk.28.ffn_norm.weight               - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 493/ 720] blk.28.ffn_up.weight                 - [  2560,  10240,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 494/ 720] blk.28.inp_gate.weight               - [  2560,    256,      1,      1], type =    f16, converting to q8_0 .. size =     1.25 MiB ->     0.66 MiB
[ 495/ 720] blk.28.layer_output_scale.weight     - [     1,      1,      1,      1], type =    f32, size =    0.000 MiB
[ 496/ 720] blk.28.post_attention_norm.weight    - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 497/ 720] blk.28.post_ffw_norm.weight          - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 498/ 720] blk.28.post_norm.weight              - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 499/ 720] blk.28.proj.weight                   - [   256,   2560,      1,      1], type =    f16, converting to q8_0 .. size =     1.25 MiB ->     0.66 MiB
[ 500/ 720] blk.29.attn_k.weight                 - [  2560,   1024,      1,      1], type =    f16, converting to q8_0 .. size =     5.00 MiB ->     2.66 MiB
[ 501/ 720] blk.29.attn_k_norm.weight            - [   512,      1,      1,      1], type =    f32, size =    0.002 MiB
[ 502/ 720] blk.29.attn_norm.weight              - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 503/ 720] blk.29.attn_output.weight            - [  4096,   2560,      1,      1], type =    f16, converting to q8_0 .. size =    20.00 MiB ->    10.62 MiB
[ 504/ 720] blk.29.attn_q.weight                 - [  2560,   4096,      1,      1], type =    f16, converting to q8_0 .. size =    20.00 MiB ->    10.62 MiB
[ 505/ 720] blk.29.attn_q_norm.weight            - [   512,      1,      1,      1], type =    f32, size =    0.002 MiB
[ 506/ 720] blk.29.attn_v.weight                 - [  2560,   1024,      1,      1], type =    f16, converting to q8_0 .. size =     5.00 MiB ->     2.66 MiB
[ 507/ 720] blk.29.ffn_down.weight               - [ 10240,   2560,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 508/ 720] blk.29.ffn_gate.weight               - [  2560,  10240,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 509/ 720] blk.29.ffn_norm.weight               - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 510/ 720] blk.29.ffn_up.weight                 - [  2560,  10240,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 511/ 720] blk.29.inp_gate.weight               - [  2560,    256,      1,      1], type =    f16, converting to q8_0 .. size =     1.25 MiB ->     0.66 MiB
[ 512/ 720] blk.29.layer_output_scale.weight     - [     1,      1,      1,      1], type =    f32, size =    0.000 MiB
[ 513/ 720] blk.29.post_attention_norm.weight    - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 514/ 720] blk.29.post_ffw_norm.weight          - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 515/ 720] blk.29.post_norm.weight              - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 516/ 720] blk.29.proj.weight                   - [   256,   2560,      1,      1], type =    f16, converting to q8_0 .. size =     1.25 MiB ->     0.66 MiB
[ 517/ 720] blk.30.attn_k.weight                 - [  2560,    512,      1,      1], type =    f16, converting to q8_0 .. size =     2.50 MiB ->     1.33 MiB
[ 518/ 720] blk.30.attn_k_norm.weight            - [   256,      1,      1,      1], type =    f32, size =    0.001 MiB
[ 519/ 720] blk.30.attn_norm.weight              - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 520/ 720] blk.30.attn_output.weight            - [  2048,   2560,      1,      1], type =    f16, converting to q8_0 .. size =    10.00 MiB ->     5.31 MiB
[ 521/ 720] blk.30.attn_q.weight                 - [  2560,   2048,      1,      1], type =    f16, converting to q8_0 .. size =    10.00 MiB ->     5.31 MiB
[ 522/ 720] blk.30.attn_q_norm.weight            - [   256,      1,      1,      1], type =    f32, size =    0.001 MiB
[ 523/ 720] blk.30.attn_v.weight                 - [  2560,    512,      1,      1], type =    f16, converting to q8_0 .. size =     2.50 MiB ->     1.33 MiB
[ 524/ 720] blk.30.ffn_down.weight               - [ 10240,   2560,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 525/ 720] blk.30.ffn_gate.weight               - [  2560,  10240,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 526/ 720] blk.30.ffn_norm.weight               - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 527/ 720] blk.30.ffn_up.weight                 - [  2560,  10240,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 528/ 720] blk.30.inp_gate.weight               - [  2560,    256,      1,      1], type =    f16, converting to q8_0 .. size =     1.25 MiB ->     0.66 MiB
[ 529/ 720] blk.30.layer_output_scale.weight     - [     1,      1,      1,      1], type =    f32, size =    0.000 MiB
[ 530/ 720] blk.30.post_attention_norm.weight    - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 531/ 720] blk.30.post_ffw_norm.weight          - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 532/ 720] blk.30.post_norm.weight              - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 533/ 720] blk.30.proj.weight                   - [   256,   2560,      1,      1], type =    f16, converting to q8_0 .. size =     1.25 MiB ->     0.66 MiB
[ 534/ 720] blk.31.attn_k.weight                 - [  2560,    512,      1,      1], type =    f16, converting to q8_0 .. size =     2.50 MiB ->     1.33 MiB
[ 535/ 720] blk.31.attn_k_norm.weight            - [   256,      1,      1,      1], type =    f32, size =    0.001 MiB
[ 536/ 720] blk.31.attn_norm.weight              - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 537/ 720] blk.31.attn_output.weight            - [  2048,   2560,      1,      1], type =    f16, converting to q8_0 .. size =    10.00 MiB ->     5.31 MiB
[ 538/ 720] blk.31.attn_q.weight                 - [  2560,   2048,      1,      1], type =    f16, converting to q8_0 .. size =    10.00 MiB ->     5.31 MiB
[ 539/ 720] blk.31.attn_q_norm.weight            - [   256,      1,      1,      1], type =    f32, size =    0.001 MiB
[ 540/ 720] blk.31.attn_v.weight                 - [  2560,    512,      1,      1], type =    f16, converting to q8_0 .. size =     2.50 MiB ->     1.33 MiB
[ 541/ 720] blk.31.ffn_down.weight               - [ 10240,   2560,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 542/ 720] blk.31.ffn_gate.weight               - [  2560,  10240,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 543/ 720] blk.31.ffn_norm.weight               - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 544/ 720] blk.31.ffn_up.weight                 - [  2560,  10240,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 545/ 720] blk.31.inp_gate.weight               - [  2560,    256,      1,      1], type =    f16, converting to q8_0 .. size =     1.25 MiB ->     0.66 MiB
[ 546/ 720] blk.31.layer_output_scale.weight     - [     1,      1,      1,      1], type =    f32, size =    0.000 MiB
[ 547/ 720] blk.31.post_attention_norm.weight    - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 548/ 720] blk.31.post_ffw_norm.weight          - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 549/ 720] blk.31.post_norm.weight              - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 550/ 720] blk.31.proj.weight                   - [   256,   2560,      1,      1], type =    f16, converting to q8_0 .. size =     1.25 MiB ->     0.66 MiB
[ 551/ 720] blk.32.attn_k.weight                 - [  2560,    512,      1,      1], type =    f16, converting to q8_0 .. size =     2.50 MiB ->     1.33 MiB
[ 552/ 720] blk.32.attn_k_norm.weight            - [   256,      1,      1,      1], type =    f32, size =    0.001 MiB
[ 553/ 720] blk.32.attn_norm.weight              - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 554/ 720] blk.32.attn_output.weight            - [  2048,   2560,      1,      1], type =    f16, converting to q8_0 .. size =    10.00 MiB ->     5.31 MiB
[ 555/ 720] blk.32.attn_q.weight                 - [  2560,   2048,      1,      1], type =    f16, converting to q8_0 .. size =    10.00 MiB ->     5.31 MiB
[ 556/ 720] blk.32.attn_q_norm.weight            - [   256,      1,      1,      1], type =    f32, size =    0.001 MiB
[ 557/ 720] blk.32.attn_v.weight                 - [  2560,    512,      1,      1], type =    f16, converting to q8_0 .. size =     2.50 MiB ->     1.33 MiB
[ 558/ 720] blk.32.ffn_down.weight               - [ 10240,   2560,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 559/ 720] blk.32.ffn_gate.weight               - [  2560,  10240,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 560/ 720] blk.32.ffn_norm.weight               - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 561/ 720] blk.32.ffn_up.weight                 - [  2560,  10240,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 562/ 720] blk.32.inp_gate.weight               - [  2560,    256,      1,      1], type =    f16, converting to q8_0 .. size =     1.25 MiB ->     0.66 MiB
[ 563/ 720] blk.32.layer_output_scale.weight     - [     1,      1,      1,      1], type =    f32, size =    0.000 MiB
[ 564/ 720] blk.32.post_attention_norm.weight    - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 565/ 720] blk.32.post_ffw_norm.weight          - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 566/ 720] blk.32.post_norm.weight              - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 567/ 720] blk.32.proj.weight                   - [   256,   2560,      1,      1], type =    f16, converting to q8_0 .. size =     1.25 MiB ->     0.66 MiB
[ 568/ 720] blk.33.attn_k.weight                 - [  2560,    512,      1,      1], type =    f16, converting to q8_0 .. size =     2.50 MiB ->     1.33 MiB
[ 569/ 720] blk.33.attn_k_norm.weight            - [   256,      1,      1,      1], type =    f32, size =    0.001 MiB
[ 570/ 720] blk.33.attn_norm.weight              - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 571/ 720] blk.33.attn_output.weight            - [  2048,   2560,      1,      1], type =    f16, converting to q8_0 .. size =    10.00 MiB ->     5.31 MiB
[ 572/ 720] blk.33.attn_q.weight                 - [  2560,   2048,      1,      1], type =    f16, converting to q8_0 .. size =    10.00 MiB ->     5.31 MiB
[ 573/ 720] blk.33.attn_q_norm.weight            - [   256,      1,      1,      1], type =    f32, size =    0.001 MiB
[ 574/ 720] blk.33.attn_v.weight                 - [  2560,    512,      1,      1], type =    f16, converting to q8_0 .. size =     2.50 MiB ->     1.33 MiB
[ 575/ 720] blk.33.ffn_down.weight               - [ 10240,   2560,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 576/ 720] blk.33.ffn_gate.weight               - [  2560,  10240,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 577/ 720] blk.33.ffn_norm.weight               - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 578/ 720] blk.33.ffn_up.weight                 - [  2560,  10240,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 579/ 720] blk.33.inp_gate.weight               - [  2560,    256,      1,      1], type =    f16, converting to q8_0 .. size =     1.25 MiB ->     0.66 MiB
[ 580/ 720] blk.33.layer_output_scale.weight     - [     1,      1,      1,      1], type =    f32, size =    0.000 MiB
[ 581/ 720] blk.33.post_attention_norm.weight    - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 582/ 720] blk.33.post_ffw_norm.weight          - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 583/ 720] blk.33.post_norm.weight              - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 584/ 720] blk.33.proj.weight                   - [   256,   2560,      1,      1], type =    f16, converting to q8_0 .. size =     1.25 MiB ->     0.66 MiB
[ 585/ 720] blk.34.attn_k.weight                 - [  2560,    512,      1,      1], type =    f16, converting to q8_0 .. size =     2.50 MiB ->     1.33 MiB
[ 586/ 720] blk.34.attn_k_norm.weight            - [   256,      1,      1,      1], type =    f32, size =    0.001 MiB
[ 587/ 720] blk.34.attn_norm.weight              - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 588/ 720] blk.34.attn_output.weight            - [  2048,   2560,      1,      1], type =    f16, converting to q8_0 .. size =    10.00 MiB ->     5.31 MiB
[ 589/ 720] blk.34.attn_q.weight                 - [  2560,   2048,      1,      1], type =    f16, converting to q8_0 .. size =    10.00 MiB ->     5.31 MiB
[ 590/ 720] blk.34.attn_q_norm.weight            - [   256,      1,      1,      1], type =    f32, size =    0.001 MiB
[ 591/ 720] blk.34.attn_v.weight                 - [  2560,    512,      1,      1], type =    f16, converting to q8_0 .. size =     2.50 MiB ->     1.33 MiB
[ 592/ 720] blk.34.ffn_down.weight               - [ 10240,   2560,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 593/ 720] blk.34.ffn_gate.weight               - [  2560,  10240,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 594/ 720] blk.34.ffn_norm.weight               - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 595/ 720] blk.34.ffn_up.weight                 - [  2560,  10240,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 596/ 720] blk.34.inp_gate.weight               - [  2560,    256,      1,      1], type =    f16, converting to q8_0 .. size =     1.25 MiB ->     0.66 MiB
[ 597/ 720] blk.34.layer_output_scale.weight     - [     1,      1,      1,      1], type =    f32, size =    0.000 MiB
[ 598/ 720] blk.34.post_attention_norm.weight    - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 599/ 720] blk.34.post_ffw_norm.weight          - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 600/ 720] blk.34.post_norm.weight              - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 601/ 720] blk.34.proj.weight                   - [   256,   2560,      1,      1], type =    f16, converting to q8_0 .. size =     1.25 MiB ->     0.66 MiB
[ 602/ 720] blk.35.attn_k.weight                 - [  2560,   1024,      1,      1], type =    f16, converting to q8_0 .. size =     5.00 MiB ->     2.66 MiB
[ 603/ 720] blk.35.attn_k_norm.weight            - [   512,      1,      1,      1], type =    f32, size =    0.002 MiB
[ 604/ 720] blk.35.attn_norm.weight              - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 605/ 720] blk.35.attn_output.weight            - [  4096,   2560,      1,      1], type =    f16, converting to q8_0 .. size =    20.00 MiB ->    10.62 MiB
[ 606/ 720] blk.35.attn_q.weight                 - [  2560,   4096,      1,      1], type =    f16, converting to q8_0 .. size =    20.00 MiB ->    10.62 MiB
[ 607/ 720] blk.35.attn_q_norm.weight            - [   512,      1,      1,      1], type =    f32, size =    0.002 MiB
[ 608/ 720] blk.35.attn_v.weight                 - [  2560,   1024,      1,      1], type =    f16, converting to q8_0 .. size =     5.00 MiB ->     2.66 MiB
[ 609/ 720] blk.35.ffn_down.weight               - [ 10240,   2560,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 610/ 720] blk.35.ffn_gate.weight               - [  2560,  10240,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 611/ 720] blk.35.ffn_norm.weight               - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 612/ 720] blk.35.ffn_up.weight                 - [  2560,  10240,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 613/ 720] blk.35.inp_gate.weight               - [  2560,    256,      1,      1], type =    f16, converting to q8_0 .. size =     1.25 MiB ->     0.66 MiB
[ 614/ 720] blk.35.layer_output_scale.weight     - [     1,      1,      1,      1], type =    f32, size =    0.000 MiB
[ 615/ 720] blk.35.post_attention_norm.weight    - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 616/ 720] blk.35.post_ffw_norm.weight          - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 617/ 720] blk.35.post_norm.weight              - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 618/ 720] blk.35.proj.weight                   - [   256,   2560,      1,      1], type =    f16, converting to q8_0 .. size =     1.25 MiB ->     0.66 MiB
[ 619/ 720] blk.36.attn_k.weight                 - [  2560,    512,      1,      1], type =    f16, converting to q8_0 .. size =     2.50 MiB ->     1.33 MiB
[ 620/ 720] blk.36.attn_k_norm.weight            - [   256,      1,      1,      1], type =    f32, size =    0.001 MiB
[ 621/ 720] blk.36.attn_norm.weight              - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 622/ 720] blk.36.attn_output.weight            - [  2048,   2560,      1,      1], type =    f16, converting to q8_0 .. size =    10.00 MiB ->     5.31 MiB
[ 623/ 720] blk.36.attn_q.weight                 - [  2560,   2048,      1,      1], type =    f16, converting to q8_0 .. size =    10.00 MiB ->     5.31 MiB
[ 624/ 720] blk.36.attn_q_norm.weight            - [   256,      1,      1,      1], type =    f32, size =    0.001 MiB
[ 625/ 720] blk.36.attn_v.weight                 - [  2560,    512,      1,      1], type =    f16, converting to q8_0 .. size =     2.50 MiB ->     1.33 MiB
[ 626/ 720] blk.36.ffn_down.weight               - [ 10240,   2560,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 627/ 720] blk.36.ffn_gate.weight               - [  2560,  10240,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 628/ 720] blk.36.ffn_norm.weight               - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 629/ 720] blk.36.ffn_up.weight                 - [  2560,  10240,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 630/ 720] blk.36.inp_gate.weight               - [  2560,    256,      1,      1], type =    f16, converting to q8_0 .. size =     1.25 MiB ->     0.66 MiB
[ 631/ 720] blk.36.layer_output_scale.weight     - [     1,      1,      1,      1], type =    f32, size =    0.000 MiB
[ 632/ 720] blk.36.post_attention_norm.weight    - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 633/ 720] blk.36.post_ffw_norm.weight          - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 634/ 720] blk.36.post_norm.weight              - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 635/ 720] blk.36.proj.weight                   - [   256,   2560,      1,      1], type =    f16, converting to q8_0 .. size =     1.25 MiB ->     0.66 MiB
[ 636/ 720] blk.37.attn_k.weight                 - [  2560,    512,      1,      1], type =    f16, converting to q8_0 .. size =     2.50 MiB ->     1.33 MiB
[ 637/ 720] blk.37.attn_k_norm.weight            - [   256,      1,      1,      1], type =    f32, size =    0.001 MiB
[ 638/ 720] blk.37.attn_norm.weight              - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 639/ 720] blk.37.attn_output.weight            - [  2048,   2560,      1,      1], type =    f16, converting to q8_0 .. size =    10.00 MiB ->     5.31 MiB
[ 640/ 720] blk.37.attn_q.weight                 - [  2560,   2048,      1,      1], type =    f16, converting to q8_0 .. size =    10.00 MiB ->     5.31 MiB
[ 641/ 720] blk.37.attn_q_norm.weight            - [   256,      1,      1,      1], type =    f32, size =    0.001 MiB
[ 642/ 720] blk.37.attn_v.weight                 - [  2560,    512,      1,      1], type =    f16, converting to q8_0 .. size =     2.50 MiB ->     1.33 MiB
[ 643/ 720] blk.37.ffn_down.weight               - [ 10240,   2560,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 644/ 720] blk.37.ffn_gate.weight               - [  2560,  10240,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 645/ 720] blk.37.ffn_norm.weight               - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 646/ 720] blk.37.ffn_up.weight                 - [  2560,  10240,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 647/ 720] blk.37.inp_gate.weight               - [  2560,    256,      1,      1], type =    f16, converting to q8_0 .. size =     1.25 MiB ->     0.66 MiB
[ 648/ 720] blk.37.layer_output_scale.weight     - [     1,      1,      1,      1], type =    f32, size =    0.000 MiB
[ 649/ 720] blk.37.post_attention_norm.weight    - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 650/ 720] blk.37.post_ffw_norm.weight          - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 651/ 720] blk.37.post_norm.weight              - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 652/ 720] blk.37.proj.weight                   - [   256,   2560,      1,      1], type =    f16, converting to q8_0 .. size =     1.25 MiB ->     0.66 MiB
[ 653/ 720] blk.38.attn_k.weight                 - [  2560,    512,      1,      1], type =    f16, converting to q8_0 .. size =     2.50 MiB ->     1.33 MiB
[ 654/ 720] blk.38.attn_k_norm.weight            - [   256,      1,      1,      1], type =    f32, size =    0.001 MiB
[ 655/ 720] blk.38.attn_norm.weight              - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 656/ 720] blk.38.attn_output.weight            - [  2048,   2560,      1,      1], type =    f16, converting to q8_0 .. size =    10.00 MiB ->     5.31 MiB
[ 657/ 720] blk.38.attn_q.weight                 - [  2560,   2048,      1,      1], type =    f16, converting to q8_0 .. size =    10.00 MiB ->     5.31 MiB
[ 658/ 720] blk.38.attn_q_norm.weight            - [   256,      1,      1,      1], type =    f32, size =    0.001 MiB
[ 659/ 720] blk.38.attn_v.weight                 - [  2560,    512,      1,      1], type =    f16, converting to q8_0 .. size =     2.50 MiB ->     1.33 MiB
[ 660/ 720] blk.38.ffn_down.weight               - [ 10240,   2560,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 661/ 720] blk.38.ffn_gate.weight               - [  2560,  10240,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 662/ 720] blk.38.ffn_norm.weight               - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 663/ 720] blk.38.ffn_up.weight                 - [  2560,  10240,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 664/ 720] blk.38.inp_gate.weight               - [  2560,    256,      1,      1], type =    f16, converting to q8_0 .. size =     1.25 MiB ->     0.66 MiB
[ 665/ 720] blk.38.layer_output_scale.weight     - [     1,      1,      1,      1], type =    f32, size =    0.000 MiB
[ 666/ 720] blk.38.post_attention_norm.weight    - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 667/ 720] blk.38.post_ffw_norm.weight          - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 668/ 720] blk.38.post_norm.weight              - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 669/ 720] blk.38.proj.weight                   - [   256,   2560,      1,      1], type =    f16, converting to q8_0 .. size =     1.25 MiB ->     0.66 MiB
[ 670/ 720] blk.39.attn_k.weight                 - [  2560,    512,      1,      1], type =    f16, converting to q8_0 .. size =     2.50 MiB ->     1.33 MiB
[ 671/ 720] blk.39.attn_k_norm.weight            - [   256,      1,      1,      1], type =    f32, size =    0.001 MiB
[ 672/ 720] blk.39.attn_norm.weight              - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 673/ 720] blk.39.attn_output.weight            - [  2048,   2560,      1,      1], type =    f16, converting to q8_0 .. size =    10.00 MiB ->     5.31 MiB
[ 674/ 720] blk.39.attn_q.weight                 - [  2560,   2048,      1,      1], type =    f16, converting to q8_0 .. size =    10.00 MiB ->     5.31 MiB
[ 675/ 720] blk.39.attn_q_norm.weight            - [   256,      1,      1,      1], type =    f32, size =    0.001 MiB
[ 676/ 720] blk.39.attn_v.weight                 - [  2560,    512,      1,      1], type =    f16, converting to q8_0 .. size =     2.50 MiB ->     1.33 MiB
[ 677/ 720] blk.39.ffn_down.weight               - [ 10240,   2560,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 678/ 720] blk.39.ffn_gate.weight               - [  2560,  10240,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 679/ 720] blk.39.ffn_norm.weight               - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 680/ 720] blk.39.ffn_up.weight                 - [  2560,  10240,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 681/ 720] blk.39.inp_gate.weight               - [  2560,    256,      1,      1], type =    f16, converting to q8_0 .. size =     1.25 MiB ->     0.66 MiB
[ 682/ 720] blk.39.layer_output_scale.weight     - [     1,      1,      1,      1], type =    f32, size =    0.000 MiB
[ 683/ 720] blk.39.post_attention_norm.weight    - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 684/ 720] blk.39.post_ffw_norm.weight          - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 685/ 720] blk.39.post_norm.weight              - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 686/ 720] blk.39.proj.weight                   - [   256,   2560,      1,      1], type =    f16, converting to q8_0 .. size =     1.25 MiB ->     0.66 MiB
[ 687/ 720] blk.40.attn_k.weight                 - [  2560,    512,      1,      1], type =    f16, converting to q8_0 .. size =     2.50 MiB ->     1.33 MiB
[ 688/ 720] blk.40.attn_k_norm.weight            - [   256,      1,      1,      1], type =    f32, size =    0.001 MiB
[ 689/ 720] blk.40.attn_norm.weight              - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 690/ 720] blk.40.attn_output.weight            - [  2048,   2560,      1,      1], type =    f16, converting to q8_0 .. size =    10.00 MiB ->     5.31 MiB
[ 691/ 720] blk.40.attn_q.weight                 - [  2560,   2048,      1,      1], type =    f16, converting to q8_0 .. size =    10.00 MiB ->     5.31 MiB
[ 692/ 720] blk.40.attn_q_norm.weight            - [   256,      1,      1,      1], type =    f32, size =    0.001 MiB
[ 693/ 720] blk.40.attn_v.weight                 - [  2560,    512,      1,      1], type =    f16, converting to q8_0 .. size =     2.50 MiB ->     1.33 MiB
[ 694/ 720] blk.40.ffn_down.weight               - [ 10240,   2560,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 695/ 720] blk.40.ffn_gate.weight               - [  2560,  10240,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 696/ 720] blk.40.ffn_norm.weight               - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 697/ 720] blk.40.ffn_up.weight                 - [  2560,  10240,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 698/ 720] blk.40.inp_gate.weight               - [  2560,    256,      1,      1], type =    f16, converting to q8_0 .. size =     1.25 MiB ->     0.66 MiB
[ 699/ 720] blk.40.layer_output_scale.weight     - [     1,      1,      1,      1], type =    f32, size =    0.000 MiB
[ 700/ 720] blk.40.post_attention_norm.weight    - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 701/ 720] blk.40.post_ffw_norm.weight          - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 702/ 720] blk.40.post_norm.weight              - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 703/ 720] blk.40.proj.weight                   - [   256,   2560,      1,      1], type =    f16, converting to q8_0 .. size =     1.25 MiB ->     0.66 MiB
[ 704/ 720] blk.41.attn_k.weight                 - [  2560,   1024,      1,      1], type =    f16, converting to q8_0 .. size =     5.00 MiB ->     2.66 MiB
[ 705/ 720] blk.41.attn_k_norm.weight            - [   512,      1,      1,      1], type =    f32, size =    0.002 MiB
[ 706/ 720] blk.41.attn_norm.weight              - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 707/ 720] blk.41.attn_output.weight            - [  4096,   2560,      1,      1], type =    f16, converting to q8_0 .. size =    20.00 MiB ->    10.62 MiB
[ 708/ 720] blk.41.attn_q.weight                 - [  2560,   4096,      1,      1], type =    f16, converting to q8_0 .. size =    20.00 MiB ->    10.62 MiB
[ 709/ 720] blk.41.attn_q_norm.weight            - [   512,      1,      1,      1], type =    f32, size =    0.002 MiB
[ 710/ 720] blk.41.attn_v.weight                 - [  2560,   1024,      1,      1], type =    f16, converting to q8_0 .. size =     5.00 MiB ->     2.66 MiB
[ 711/ 720] blk.41.ffn_down.weight               - [ 10240,   2560,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 712/ 720] blk.41.ffn_gate.weight               - [  2560,  10240,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 713/ 720] blk.41.ffn_norm.weight               - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 714/ 720] blk.41.ffn_up.weight                 - [  2560,  10240,      1,      1], type =    f16, converting to q8_0 .. size =    50.00 MiB ->    26.56 MiB
[ 715/ 720] blk.41.inp_gate.weight               - [  2560,    256,      1,      1], type =    f16, converting to q8_0 .. size =     1.25 MiB ->     0.66 MiB
[ 716/ 720] blk.41.layer_output_scale.weight     - [     1,      1,      1,      1], type =    f32, size =    0.000 MiB
[ 717/ 720] blk.41.post_attention_norm.weight    - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 718/ 720] blk.41.post_ffw_norm.weight          - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 719/ 720] blk.41.post_norm.weight              - [  2560,      1,      1,      1], type =    f32, size =    0.010 MiB
[ 720/ 720] blk.41.proj.weight                   - [   256,   2560,      1,      1], type =    f16, converting to q8_0 .. size =     1.25 MiB ->     0.66 MiB

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Posted by 구차니

언제나 그렇듯(!) 1080은 돌리기 힘든 시대구만.. 저번에 찾아둔 걸로 버전 맞추고

pip3 uninstall -y torch torchvision torchaudio xformers
pip3 install torch==2.6.0 torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
pip3 install xformers==0.0.29.post2

 

-mvtec-ad 는 dataset 다운로드 받아 압축푼 최상위 디렉토리를 지정해주면 된다.

~/src/STFPM/mvtec-ad$ tree -d -L 2
.
├── bottle
│   ├── ground_truth
│   ├── test
│   └── train
├── cable
│   ├── ground_truth
│   ├── test
│   └── train
├── capsule
│   ├── ground_truth
│   ├── test
│   └── train
├── carpet
│   ├── ground_truth
│   ├── test
│   └── train
├── grid
│   ├── ground_truth
│   ├── test
│   └── train
├── hazelnut
│   ├── ground_truth
│   ├── test
│   └── train
├── leather
│   ├── ground_truth
│   ├── test
│   └── train
├── metal_nut
│   ├── ground_truth
│   ├── test
│   └── train
├── pill
│   ├── ground_truth
│   ├── test
│   └── train
├── screw
│   ├── ground_truth
│   ├── test
│   └── train
├── tile
│   ├── ground_truth
│   ├── test
│   └── train
├── toothbrush
│   ├── ground_truth
│   ├── test
│   └── train
├── transistor
│   ├── ground_truth
│   ├── test
│   └── train
├── wood
│   ├── ground_truth
│   ├── test
│   └── train
└── zipper
    ├── ground_truth
    ├── test
    └── train

 

$ python3 main.py train --mvtec-ad  mvtec-ad --category carpet --epochs 200
...
[198/200] loss: 0.145916
[198/200] loss: 0.147569
[198/200] loss: 0.147157
[198/200] loss: 0.146806
[198/200] loss: 0.147341
[198/200] loss: 0.145462
[198/200] loss: 0.144424
Valid Loss: 0.0001142
[199/200] loss: 0.146722
[199/200] loss: 0.146246
[199/200] loss: 0.145853
[199/200] loss: 0.145966
[199/200] loss: 0.145793
[199/200] loss: 0.145863
[199/200] loss: 0.154857
Valid Loss: 0.0001460

real 9m35.951s
user 31m24.301s
sys 1m16.579s

 

loss 만 보고 학습을 하게 했네. 

그래도 200 epoch 인데 내부는 어떤지 모르겠지만 10분 컷이면 양호하네

 

cpu 로 1개 코어만 사용해서 하는데 제법 걸린다. 이미지로 판별하고 출력할 수 없나?

$ time python3 main.py test --mvtec-ad  mvtec-ad --category carpet --checkpoint snapshots/carpet/best.pth.tar
load snapshots/carpet/best.pth.tar
Catergory: carpet Pixel-AUC: 0.990288 Image-AUC: 0.955859 PRO: 0.959637

real 5m39.944s
user 5m46.592s
sys 0m1.064s

 

tar 라면서 왜 zip 이래?!?

~/src/STFPM/snapshots/carpet$ file *
best.pth.tar: Zip archive data, at least v0.0 to extract, compression method=store

~/src/STFPM/snapshots/carpet$ ll -h
total 43M
drwxrwxr-x 2 falinux falinux 4.0K  6월  1 11:57 ./
drwxrwxr-x 3 falinux falinux 4.0K  6월  1 11:14 ../
-rw-rw-r-- 1 falinux falinux  43M  6월  1 11:52 best.pth.tar

 

[링크 : https://github.com/gdwang08/STFPM]

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Posted by 구차니

생각해보니 기존에 쓰던 버전도 몰랐네 -_-?

 

그래서 리눅스용 tar 파일 받고, 실행하는데 머.. 특별한건 없어 보인다.

 

로그인 하고

 

동의하고

 

뜬다.

 

소심하게 좌측상단 메뉴 옆에 Antigravity 누르면 버전 나오고 땡

 

테마 고르고

 

멀 할지 고르고(아무것도 선택안해도 넘어감)

 

짜잔~

이전에 작업했던 것들이 왼쪽에 쭈르륵 뜬다. 이건 좀 좋다고 해야하나 말아야 하나?

 

음.. 저번에 다 써먹었는데 이번에는 좀 쓸만한 양을 주려나?

 

근데, 우분투에서 패키지로 깐게 아니라

설치하는 경로에서 실행해줘야 해서 조금 귀찮다. 어떻게 데스크탑 아이콘 설정 바꾸지?

 

 

Exec 뒤에 경로를 적절히 조정해주면 된다.

$ cd ~/.local/share/applications
$ vi antigravity-ide.desktop
$ cat antigravity-ide.desktop
[Desktop Entry]
Name=Antigravity IDE
Comment=Antigravity IDE v2.0 - Experience liftoff
GenericName=IDE
Exec="/home/minimonk/Antigravity-x64/antigravity" %F
Icon=antigravity
Type=Application
Terminal=false
StartupNotify=true
StartupWMClass=Antigravity
Categories=Development;IDE;TextEditor;
MimeType=application/x-antigravity-workspace;
$ chmod +x antigravity-ide.desktop 
$ update-desktop-database ~/.local/share/applications/

[링크 : https://discuss.ai.google.dev/t/ubuntu-24-04-upgrading-to-antigravity-2-0-and-antigravity-ide/146516/4]

Posted by 구차니
프로그램 사용/Blender2026. 5. 27. 23:53

먼가 발견하긴 했는데, 이미 서비스 종료 -_ㅠ

 

Notice: The model used in the paper, Claude Sonnet 3.7, has been retired. As a result, we have discontinued the LL3M server.

[링크 : https://github.com/threedle/ll3m]

[링크 : https://threedle.github.io/ll3m/]

 

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Posted by 구차니

 

-----

metric 함수. 얘도 함수였구나.

아무튼 metric은 격하게 오락가락 하고

[링크 : https://modulabs.co.kr/blog/loss-versus-accuracy]

 

loss 가 점진적 감소 -> 잘 학습

loss 급격히 감소 후 유지 -> 정상

loss 일정 시점 이후 증가 -> over fitting

[링크 : https://m.blog.naver.com/datapreprep/223748771141]

 

근데.. underfitting은 먼지 감이 안오네.

When to change the number of epochs (training cycles)

Selecting the appropriate number of epochs is a balance between underfitting and overfitting.

Underfitting: One of the most straightforward indicators of underfitting is if the model performs poorly on the training data. This can be observed in Edge Impulse Studio through metrics such as accuracy, or loss, depending on the type of problem (classification or regression). If these metrics indicate poor performance, it suggests that the model has not learned the patterns of the data well. In that case, increasing the number of epochs can improve your model performance. Please note that other solutions exist such as increasing your neural network architecture complexity, changing the preprocessing technique or reducing regularization.

Overfitting: Detecting overfitting involves recognizing when the model has learned too much from the training data, including its noise and outliers, to the detriment of its performance on new, unseen data. Overfitting is characterized by the model performing exceptionally well on the training data but poorly on the validation or test data. Evaluating overfitting can be achieved by comparing the performance of the model between the training set and the validation set during training. When the performance on the validation set starts to degrade, it might indicate that the model is beginning to overfit the training data. In that case, decreasing the number of epochs can improve your model performance. As with underfitting, other solutions exist to reduce overfitting such as increasing the number of training data, adding regularization techniques to add penalties on large weights, adding dropout layers, simplifying the model architecture and even using early stopping.



[링크 : https://docs.edgeimpulse.com/knowledge/concepts/machine-learning/neural-networks/epochs]

 

overfitting 중인 loss 값 이라는데, 

 

학습셋이 너무 적을 경우 overfitting이 빠르게 발생하는 것으로 보인다.

그나저나 저 130 miliion parameter는 ssd300 에서 어떻게 산출된걸까?

What you are experiencing is called overfitting and it happens because of your very small dataset.
All the model cares about is performance on the training dataset, so given the opportunity, it will simply attempt to memorize it. This is what happens in you case, you feed a model which contains over 130 Million parameters less than 319 images.
So regarding your questions:
  1. The loss function shows a clear case of overfitting.
  2. On general, it is okay to use a trained model, especially when you only have a small dataset, but in your case, the dataset is too small for any deep-learning model. When I say small dataset, I mean 10k images, not several hundreds.
  3. You should not train for longer time, once the validation loss stops improving, it is a clear sign to stop. There is even a training technique named "early stopping" which is designed to stop training once the validation loss stops to drop.
You have to understand that currently, your dataset of 300 images, is irrelevant to the world of deep-learning. So if you still want to use it for object detection, you need to revert to more classic computer-vision techniques like using HOG or SIFT features, or even manually engineering the features for your special case.
 

[링크 : https://datascience.stackexchange.com/questions/46456/issues-with-training-ssd-on-own-dataset]

 

The SSD300 has 35.6 million parameters, while the SSDlite320 has about a tenth of them, i.e., 3.4 million.

[링크 : https://www.mdpi.com/2673-6470/3/3/12]

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그래서.. openclaw 에서 대화창 인증하고 나서 바로 메뉴가 추가되었던 건가..

그리고 set_my_commands() 로 등록되어도 채팅 내용으로 받아와지진 않는다.

다른 무슨 처리가 별도로 필요한 듯.

 

Bot settings

set_my_commands() Used for setting the list of commands
delete_my_commands() Used for deleting the list of commands
get_my_commands() Used for obtaining the list of commands
get_my_default_administrator_rights() Used for obtaining the default administrator rights for the bot
set_my_default_administrator_rights() Used for setting the default administrator rights for the bot
get_chat_menu_button() Used for obtaining the menu button of a private chat or the default menu button
set_chat_menu_button() Used for setting the menu button of a private chat or the default menu button
set_my_description() Used for setting the description of the bot
get_my_description() Used for obtaining the description of the bot
set_my_short_description() Used for setting the short description of the bot
get_my_short_description() Used for obtaining the short description of the bot
set_my_name() Used for setting the name of the bot
get_my_name() Used for obtaining the name of the bot
set_my_profile_photo() Used for setting the profile photo of the bot
remove_my_profile_photo() Used for removing the profile photo of the bot

[링크 : https://docs.python-telegram-bot.org/en/stable/telegram.bot.html]

 

봇 토큰으로 채팅내 메뉴 목록 보기

import asyncio
from telegram import Bot

async def check_bot_menu():
    # Replace with your actual Bot Token from @BotFather
    bot_token = "YOUR_BOT_TOKEN_HERE"
    bot = Bot(token=bot_token)
    
    print("Fetching active menu commands...")
    # Retrieves a list of BotCommand objects
    commands = await bot.get_my_commands()
    
    if not commands:
        print("No menu commands found. The menu is empty.")
    else:
        print(f"Found {len(commands)} command(s):")
        for cmd in commands:
            print(f"/{cmd.command} - {cmd.description}")

if __name__ == "__main__":
    asyncio.run(check_bot_menu())

 

봇 토큰으로 채팅내 메뉴 비우기

import asyncio
from telegram import Bot

async def remove_bot_menu():
    # Replace with your actual Bot Token from @BotFather
    bot_token = "YOUR_BOT_TOKEN_HERE"
    bot = Bot(token=bot_token)
    
    print("Removing all menu commands...")
    # Passing no arguments clears the commands globally
    await bot.delete_my_commands()
    print("Success! All menu commands have been removed.")

# Run the async function
if __name__ == "__main__":
    asyncio.run(remove_bot_menu())

 

봇 토큰과 채팅id를 이용해서 즉각적으로 메뉴 변경하기.

set_my_commands 로는 대화창을 나가도 바뀌지 않아서, 특정 채팅 아이디 넣어서야 바뀌게 되었음

import asyncio
from telegram import Bot, BotCommand, BotCommandScopeChat

async def force_update_user_menu():
    # 1. 봇 토큰과 대상 유저의 Chat ID를 입력하세요.
    BOT_TOKEN = "YOUR_BOT_TOKEN_HERE"
    USER_CHAT_ID = 123456789  # 숫자로 된 유저의 chat_id 입력
    
    bot = Bot(token=BOT_TOKEN)
    
    # 2. 변경하고 싶은 새로운 메뉴 목록을 정의합니다.
    new_commands = [
        BotCommand(command="home", description="🏠 홈 화면으로"),
        BotCommand(command="mypage", description="👤 내 정보 보기"),
        BotCommand(command="support", description="❓ 고객 센터")
    ]
    
    print(f"User({USER_CHAT_ID})의 메뉴를 즉시 변경합니다...")
    
    # 3. scope를 'BotCommandScopeChat'으로 지정하여 특정 chat_id에 즉시 강제 적용
    await bot.set_my_commands(
        commands=new_commands,
        scope=BotCommandScopeChat(chat_id=USER_CHAT_ID)
    )
    
    print("성공! 해당 유저의 텔레그램 앱 화면에서 메뉴가 즉시 업데이트되었습니다.")

if __name__ == "__main__":
    asyncio.run(force_update_user_menu())

 

요건 아직 테스트 안해봄

from telegram import Update, BotCommand, BotCommandScopeChat
from telegram.ext import Application, CommandHandler, ContextTypes

# 봇 토큰 설정
BOT_TOKEN = "YOUR_BOT_TOKEN_HERE"

# 1. /start 명령어가 들어왔을 때 실행될 함수 (여기서 메뉴를 즉시 변경)
async def start_command(update: Update, context: ContextTypes.DEFAULT_TYPE):
    chat_id = update.effective_chat.id
    
    # 해당 유저의 화면에 보일 새로운 메뉴 정의
    new_menu = [
        BotCommand(command="home", description="🏠 홈 화면으로"),
        BotCommand(command="mypage", description="👤 내 정보 보기")
    ]
    
    # 유저 화면의 메뉴 버튼 즉시 업데이트
    await context.bot.set_my_commands(
        commands=new_menu,
        scope=BotCommandScopeChat(chat_id=chat_id)
    )
    
    await update.message.reply_text(
        "반갑습니다! 메뉴 버튼이 업데이트되었습니다.\n"
        "좌측 하단의 [Menu] 버튼을 누르거나 명령어를 입력해보세요!"
    )

# 2. 메뉴 버튼의 /home 처리를 위한 핸들러 함수
async def home_command(update: Update, context: ContextTypes.DEFAULT_TYPE):
    await update.message.reply_text("🏠 홈 화면으로 이동했습니다.")

# 3. 메뉴 버튼의 /mypage 처리를 위한 핸들러 함수
async def mypage_command(update: Update, context: ContextTypes.DEFAULT_TYPE):
    user = update.effective_user
    await update.message.reply_text(f"👤 [{user.first_name}] 님의 마이페이지입니다.")


def main():
    # 애플리케이션 빌드
    application = Application.builder().token(BOT_TOKEN).build()

    # ⭐ [핵심] 메뉴에 추가한 명령어들과 핸들러 함수를 1:1로 매핑해줍니다.
    application.add_handler(CommandHandler("start", start_command))
    application.add_handler(CommandHandler("home", home_command))       # /home 처리
    application.add_handler(CommandHandler("mypage", mypage_command))   # /mypage 처리

    # 봇 시작 (폴링 방식)
    print("봇이 시작되었습니다. 대화를 시작하세요...")
    application.run_polling()

if __name__ == "__main__":
    main()
Posted by 구차니

HF_HOME 환경 변수를 바꿔주면 된다고

리눅스에서는 기본값 ~/.cache/huggingface

[링크 : https://developer0hye.tistory.com/775]

Posted by 구차니

실행하라면 이렇게 하라고 하는데

vllm serve google/gemma-4-E4B-it \
  --max-model-len <n_of_tokens> # up to 131072

[링크 : https://docs.vllm.ai/projects/recipes/en/latest/Google/Gemma4.html]

 

아따.. 드럽게 크네. 그나저나 허깅페이스에서 바로 받으려나?

그리고 gguf가 양자화 되서 작은거였나. 기존에 내가 쓰던데 Q4_K_M 이라 4.7기가 정도 되었는데

model.safetensors는 16기가나 된다. 와우

[링크 : https://huggingface.co/google/gemma-4-E4B]

 

vllm : 1080 ti 라니 불량식품이잖아! 퉤!

$ vllm serve google/gemma-4-E4B-it
(APIServer pid=52696) INFO 05-24 22:20:19 [utils.py:306]
(APIServer pid=52696) INFO 05-24 22:20:19 [utils.py:306]        █     █     █▄   ▄█
(APIServer pid=52696) INFO 05-24 22:20:19 [utils.py:306]  ▄▄ ▄█ █     █     █ ▀▄▀ █  version 0.21.0
(APIServer pid=52696) INFO 05-24 22:20:19 [utils.py:306]   █▄█▀ █     █     █     █  model   google/gemma-4-E4B-it
(APIServer pid=52696) INFO 05-24 22:20:19 [utils.py:306]    ▀▀  ▀▀▀▀▀ ▀▀▀▀▀ ▀     ▀
(APIServer pid=52696) INFO 05-24 22:20:19 [utils.py:306]
(APIServer pid=52696) INFO 05-24 22:20:19 [utils.py:240] non-default args: {'model_tag': 'google/gemma-4-E4B-it', 'model': 'google/gemma-4-E4B-it'}
(APIServer pid=52696) Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.
config.json: 5.14kB [00:00, 17.7MB/s]
processor_config.json: 1.69kB [00:00, 1.70MB/s]
(APIServer pid=52696) INFO 05-24 22:20:29 [model.py:568] Resolved architecture: Gemma4ForConditionalGeneration
(APIServer pid=52696) WARNING 05-24 22:20:29 [model.py:1982] Your device 'NVIDIA GeForce GTX 1080 Ti' (with compute capability 6.1) doesn't support torch.bfloat16. Falling back to torch.float16 for compatibility.
(APIServer pid=52696) WARNING 05-24 22:20:29 [model.py:2035] Casting torch.bfloat16 to torch.float16.
(APIServer pid=52696) INFO 05-24 22:20:29 [model.py:1697] Using max model len 131072
(APIServer pid=52696) INFO 05-24 22:20:29 [config.py:101] Gemma4 model has heterogeneous head dimensions (head_dim=256, global_head_dim=512). Forcing TRITON_ATTN backend to prevent mixed-backend numerical divergence.
(APIServer pid=52696) INFO 05-24 22:20:29 [vllm.py:886] Asynchronous scheduling is enabled.
(APIServer pid=52696) INFO 05-24 22:20:29 [kernel.py:212] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native'])
tokenizer_config.json: 2.10kB [00:00, 2.05MB/s]
tokenizer.json: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 32.2M/32.2M [00:02<00:00, 14.8MB/s]
chat_template.jinja: 17.3kB [00:00, 12.6MB/s]
generation_config.json: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 208/208 [00:00<00:00, 1.68MB/s]
(EngineCore pid=52767) INFO 05-24 22:21:24 [core.py:109] Initializing a V1 LLM engine (v0.21.0) with config: model='google/gemma-4-E4B-it', speculative_config=None, tokenizer='google/gemma-4-E4B-it', skip_t
okenizer_init=False, tokenizer_mode=auto, revision=None, tokenizer_revision=None, trust_remote_code=False, dtype=torch.float16, max_seq_len=131072, download_dir=None, load_format=auto, tensor_parallel_size=
1, pipeline_parallel_size=1, data_parallel_size=1, decode_context_parallel_size=1, dcp_comm_backend=ag_rs, disable_custom_all_reduce=False, quantization=None, quantization_config=None, enforce_eager=False,
enable_return_routed_experts=False, kv_cache_dtype=auto, device_config=cuda, structured_outputs_config=StructuredOutputsConfig(backend='auto', disable_any_whitespace=False, disable_additional_properties=Fal
se, reasoning_parser='', reasoning_parser_plugin='', enable_in_reasoning=False), observability_config=ObservabilityConfig(show_hidden_metrics_for_version=None, otlp_traces_endpoint=None, collect_detailed_tr
aces=None, kv_cache_metrics=False, kv_cache_metrics_sample=0.01, cudagraph_metrics=False, enable_layerwise_nvtx_tracing=False, enable_mfu_metrics=False, enable_mm_processor_stats=False, enable_logging_itera
tion_details=False), seed=0, served_model_name=google/gemma-4-E4B-it, enable_prefix_caching=True, enable_chunked_prefill=True, pooler_config=None, compilation_config={'mode': <CompilationMode.VLLM_COMPILE:
3>, 'debug_dump_path': None, 'cache_dir': '', 'compile_cache_save_format': 'binary', 'backend': 'inductor', 'custom_ops': ['none'], 'ir_enable_torch_wrap': True, 'splitting_ops': ['vllm::unified_attention_w
ith_output', 'vllm::unified_mla_attention_with_output', 'vllm::mamba_mixer2', 'vllm::mamba_mixer', 'vllm::short_conv', 'vllm::linear_attention', 'vllm::plamo2_mamba_mixer', 'vllm::gdn_attention_core', 'vllm
::gdn_attention_core_xpu', 'vllm::olmo_hybrid_gdn_full_forward', 'vllm::kda_attention', 'vllm::sparse_attn_indexer', 'vllm::rocm_aiter_sparse_attn_indexer', 'vllm::deepseek_v4_attention', 'vllm::unified_kv_
cache_update', 'vllm::unified_mla_kv_cache_update'], 'compile_mm_encoder': False, 'cudagraph_mm_encoder': False, 'encoder_cudagraph_token_budgets': [], 'encoder_cudagraph_max_vision_items_per_batch': 0, 'en
coder_cudagraph_max_frames_per_batch': None, 'compile_sizes': [], 'compile_ranges_endpoints': [2048], 'inductor_compile_config': {'enable_auto_functionalized_v2': False, 'size_asserts': False, 'alignment_as
serts': False, 'scalar_asserts': False, 'combo_kernels': True, 'benchmark_combo_kernel': True}, 'inductor_passes': {}, 'cudagraph_mode': <CUDAGraphMode.FULL_AND_PIECEWISE: (2, 1)>, 'cudagraph_num_of_warmups
': 1, 'cudagraph_capture_sizes': [1, 2, 4, 8, 16, 24, 32, 40, 48, 56, 64, 72, 80, 88, 96, 104, 112, 120, 128, 136, 144, 152, 160, 168, 176, 184, 192, 200, 208, 216, 224, 232, 240, 248, 256, 272, 288, 304, 3
20, 336, 352, 368, 384, 400, 416, 432, 448, 464, 480, 496, 512], 'cudagraph_copy_inputs': False, 'cudagraph_specialize_lora': True, 'use_inductor_graph_partition': False, 'pass_config': {'fuse_norm_quant':
False, 'fuse_act_quant': False, 'fuse_attn_quant': False, 'enable_sp': False, 'fuse_gemm_comms': False, 'fuse_allreduce_rms': False, 'fuse_act_padding': False}, 'max_cudagraph_capture_size': 512, 'dynamic_s
hapes_config': {'type': <DynamicShapesType.BACKED: 'backed'>, 'evaluate_guards': False, 'assume_32_bit_indexing': False}, 'local_cache_dir': None, 'fast_moe_cold_start': False, 'static_all_moe_layers': []},
 kernel_config=KernelConfig(ir_op_priority=IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']), enable_flashinfer_autotune=False, moe_backend='auto')
(EngineCore pid=52767) /home/minimonk/.local/lib/python3.10/site-packages/torch/cuda/__init__.py:371: UserWarning: Found GPU0 NVIDIA GeForce GTX 1080 Ti which is of compute capability (CC) 6.1.
(EngineCore pid=52767) The following list shows the CCs this version of PyTorch was built for and the hardware CCs it supports:
(EngineCore pid=52767) - 7.5 which supports hardware CC >=7.5,<8.0
(EngineCore pid=52767) - 8.0 which supports hardware CC >=8.0,<9.0 except {8.7}
(EngineCore pid=52767) - 8.6 which supports hardware CC >=8.6,<9.0 except {8.7}
(EngineCore pid=52767) - 9.0 which supports hardware CC >=9.0,<10.0
(EngineCore pid=52767) - 8.6 which supports hardware CC >=8.6,<9.0 except {8.7}                                                                                                                      [108/299]
(EngineCore pid=52767) - 9.0 which supports hardware CC >=9.0,<10.0
(EngineCore pid=52767) - 10.0 which supports hardware CC >=10.0,<11.0 except {10.1}
(EngineCore pid=52767) - 12.0 which supports hardware CC >=12.0,<13.0
(EngineCore pid=52767) Please follow the instructions at https://pytorch.org/get-started/locally/ to install a PyTorch release that supports one of these CUDA versions: 12.6
(EngineCore pid=52767)   _warn_unsupported_code(d, device_cc, code_ccs)
(EngineCore pid=52767) /home/minimonk/.local/lib/python3.10/site-packages/torch/cuda/__init__.py:371: UserWarning: Found GPU1 NVIDIA GeForce GTX 1080 Ti which is of compute capability (CC) 6.1.
(EngineCore pid=52767) The following list shows the CCs this version of PyTorch was built for and the hardware CCs it supports:
(EngineCore pid=52767) - 7.5 which supports hardware CC >=7.5,<8.0                                                                                                                                            (EngineCore pid=52767) - 8.0 which supports hardware CC >=8.0,<9.0 except {8.7}
(EngineCore pid=52767) - 8.6 which supports hardware CC >=8.6,<9.0 except {8.7}
(EngineCore pid=52767) - 9.0 which supports hardware CC >=9.0,<10.0
(EngineCore pid=52767) - 10.0 which supports hardware CC >=10.0,<11.0 except {10.1}
(EngineCore pid=52767) - 12.0 which supports hardware CC >=12.0,<13.0
(EngineCore pid=52767) Please follow the instructions at https://pytorch.org/get-started/locally/ to install a PyTorch release that supports one of these CUDA versions: 12.6
(EngineCore pid=52767)   _warn_unsupported_code(d, device_cc, code_ccs)
(EngineCore pid=52767) /home/minimonk/.local/lib/python3.10/site-packages/torch/cuda/__init__.py:489: UserWarning:
(EngineCore pid=52767) NVIDIA GeForce GTX 1080 Ti with CUDA capability sm_61 is not compatible with the current PyTorch installation.
(EngineCore pid=52767) The current PyTorch install supports CUDA capabilities sm_75 sm_80 sm_86 sm_90 sm_100 sm_120.
(EngineCore pid=52767) If you want to use the NVIDIA GeForce GTX 1080 Ti GPU with PyTorch, please check the instructions at https://pytorch.org/get-started/locally/
(EngineCore pid=52767)
(EngineCore pid=52767)   queued_call()
(EngineCore pid=52767) INFO 05-24 22:21:30 [parallel_state.py:1410] world_size=1 rank=0 local_rank=0 distributed_init_method=tcp://192.168.40.238:47913 backend=nccl
(EngineCore pid=52767) INFO 05-24 22:21:30 [parallel_state.py:1723] rank 0 in world size 1 is assigned as DP rank 0, PP rank 0, PCP rank 0, TP rank 0, EP rank N/A, EPLB rank N/A
(EngineCore pid=52767) WARNING 05-24 22:21:31 [topk_topp_sampler.py:61] FlashInfer top-p/top-k sampling not supported on compute capability 6.1; falling back to PyTorch-native sampler. Set VLLM_USE_FLASHINF
ER_SAMPLER=0 to silence.
(EngineCore pid=52767) ERROR 05-24 22:21:32 [core.py:1140] EngineCore failed to start.
(EngineCore pid=52767) ERROR 05-24 22:21:32 [core.py:1140] Traceback (most recent call last):
(EngineCore pid=52767) ERROR 05-24 22:21:32 [core.py:1140]   File "/home/minimonk/.local/lib/python3.10/site-packages/vllm/v1/engine/core.py", line 1114, in run_engine_core
(EngineCore pid=52767) ERROR 05-24 22:21:32 [core.py:1140]     engine_core = EngineCoreProc(*args, engine_index=dp_rank, **kwargs)
(EngineCore pid=52767) ERROR 05-24 22:21:32 [core.py:1140]   File "/home/minimonk/.local/lib/python3.10/site-packages/vllm/tracing/otel.py", line 178, in sync_wrapper
(EngineCore pid=52767) ERROR 05-24 22:21:32 [core.py:1140]     return func(*args, **kwargs)
(EngineCore pid=52767) ERROR 05-24 22:21:32 [core.py:1140]   File "/home/minimonk/.local/lib/python3.10/site-packages/vllm/v1/engine/core.py", line 880, in __init__
(EngineCore pid=52767) ERROR 05-24 22:21:32 [core.py:1140]     super().__init__(
(EngineCore pid=52767) ERROR 05-24 22:21:32 [core.py:1140]   File "/home/minimonk/.local/lib/python3.10/site-packages/vllm/v1/engine/core.py", line 118, in __init__
(EngineCore pid=52767) ERROR 05-24 22:21:32 [core.py:1140]     self.model_executor = executor_class(vllm_config)
(EngineCore pid=52767) ERROR 05-24 22:21:32 [core.py:1140]   File "/home/minimonk/.local/lib/python3.10/site-packages/vllm/tracing/otel.py", line 178, in sync_wrapper
(EngineCore pid=52767) ERROR 05-24 22:21:32 [core.py:1140]     return func(*args, **kwargs)
(EngineCore pid=52767) ERROR 05-24 22:21:32 [core.py:1140]   File "/home/minimonk/.local/lib/python3.10/site-packages/vllm/v1/executor/abstract.py", line 109, in __init__
(EngineCore pid=52767) ERROR 05-24 22:21:32 [core.py:1140]     self._init_executor()
(EngineCore pid=52767) ERROR 05-24 22:21:32 [core.py:1140]   File "/home/minimonk/.local/lib/python3.10/site-packages/vllm/v1/executor/uniproc_executor.py", line 60, in _init_executor
(EngineCore pid=52767) ERROR 05-24 22:21:32 [core.py:1140]     self.driver_worker.init_device()
(EngineCore pid=52767) ERROR 05-24 22:21:32 [core.py:1140]   File "/home/minimonk/.local/lib/python3.10/site-packages/vllm/v1/worker/worker_base.py", line 317, in init_device
(EngineCore pid=52767) ERROR 05-24 22:21:32 [core.py:1140]     self.worker.init_device()  # type: ignore
(EngineCore pid=52767) ERROR 05-24 22:21:32 [core.py:1140]   File "/home/minimonk/.local/lib/python3.10/site-packages/vllm/tracing/otel.py", line 178, in sync_wrapper
(EngineCore pid=52767) ERROR 05-24 22:21:32 [core.py:1140]     return func(*args, **kwargs)
(EngineCore pid=52767) ERROR 05-24 22:21:32 [core.py:1140]   File "/home/minimonk/.local/lib/python3.10/site-packages/vllm/v1/worker/gpu_worker.py", line 330, in init_device
(EngineCore pid=52767) ERROR 05-24 22:21:32 [core.py:1140]     self.model_runner = GPUModelRunnerV1(self.vllm_config, self.device)
(EngineCore pid=52767) ERROR 05-24 22:21:32 [core.py:1140]   File "/home/minimonk/.local/lib/python3.10/site-packages/vllm/v1/worker/gpu_model_runner.py", line 629, in __init__
(EngineCore pid=52767) ERROR 05-24 22:21:32 [core.py:1140]     self.input_batch = InputBatch(
(EngineCore pid=52767) ERROR 05-24 22:21:32 [core.py:1140]   File "/home/minimonk/.local/lib/python3.10/site-packages/vllm/v1/worker/gpu_input_batch.py", line 171, in __init__
(EngineCore pid=52767) ERROR 05-24 22:21:32 [core.py:1140]     self.block_table = MultiGroupBlockTable(
(EngineCore pid=52767) ERROR 05-24 22:21:32 [core.py:1140]   File "/home/minimonk/.local/lib/python3.10/site-packages/vllm/v1/worker/block_table.py", line 267, in __init__
(EngineCore pid=52767) ERROR 05-24 22:21:32 [core.py:1140]     self.block_tables = [
(EngineCore pid=52767) ERROR 05-24 22:21:32 [core.py:1140]   File "/home/minimonk/.local/lib/python3.10/site-packages/vllm/v1/worker/block_table.py", line 268, in <listcomp>
(EngineCore pid=52767) ERROR 05-24 22:21:32 [core.py:1140]     BlockTable(
(EngineCore pid=52767) ERROR 05-24 22:21:32 [core.py:1140]   File "/home/minimonk/.local/lib/python3.10/site-packages/vllm/v1/worker/block_table.py", line 268, in <listcomp>                          [54/299]
(EngineCore pid=52767) ERROR 05-24 22:21:32 [core.py:1140]     BlockTable(
(EngineCore pid=52767) ERROR 05-24 22:21:32 [core.py:1140]   File "/home/minimonk/.local/lib/python3.10/site-packages/vllm/v1/worker/block_table.py", line 70, in __init__
(EngineCore pid=52767) ERROR 05-24 22:21:32 [core.py:1140]     self.block_table = self._make_buffer(
(EngineCore pid=52767) ERROR 05-24 22:21:32 [core.py:1140]   File "/home/minimonk/.local/lib/python3.10/site-packages/vllm/v1/worker/block_table.py", line 218, in _make_buffer
(EngineCore pid=52767) ERROR 05-24 22:21:32 [core.py:1140]     return CpuGpuBuffer(
(EngineCore pid=52767) ERROR 05-24 22:21:32 [core.py:1140]   File "/home/minimonk/.local/lib/python3.10/site-packages/vllm/v1/utils.py", line 120, in __init__
(EngineCore pid=52767) ERROR 05-24 22:21:32 [core.py:1140]     self.gpu = torch.zeros_like(self.cpu, device=device)
(EngineCore pid=52767) ERROR 05-24 22:21:32 [core.py:1140] torch.AcceleratorError: CUDA error: no kernel image is available for execution on the device                                                       
(EngineCore pid=52767) ERROR 05-24 22:21:32 [core.py:1140] Search for `cudaErrorNoKernelImageForDevice' in https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__TYPES.html for more information.
(EngineCore pid=52767) ERROR 05-24 22:21:32 [core.py:1140] CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.
(EngineCore pid=52767) ERROR 05-24 22:21:32 [core.py:1140] For debugging consider passing CUDA_LAUNCH_BLOCKING=1
(EngineCore pid=52767) ERROR 05-24 22:21:32 [core.py:1140] Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.
(EngineCore pid=52767) ERROR 05-24 22:21:32 [core.py:1140]
(EngineCore pid=52767) Process EngineCore:
(EngineCore pid=52767) Traceback (most recent call last):
(EngineCore pid=52767)   File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
(EngineCore pid=52767)     self.run()
(EngineCore pid=52767)   File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
(EngineCore pid=52767)     self._target(*self._args, **self._kwargs)
(EngineCore pid=52767)   File "/home/minimonk/.local/lib/python3.10/site-packages/vllm/v1/engine/core.py", line 1144, in run_engine_core
(EngineCore pid=52767)     raise e
(EngineCore pid=52767)   File "/home/minimonk/.local/lib/python3.10/site-packages/vllm/v1/engine/core.py", line 1114, in run_engine_core
(EngineCore pid=52767)     engine_core = EngineCoreProc(*args, engine_index=dp_rank, **kwargs)
(EngineCore pid=52767)   File "/home/minimonk/.local/lib/python3.10/site-packages/vllm/tracing/otel.py", line 178, in sync_wrapper
(EngineCore pid=52767)     return func(*args, **kwargs)
(EngineCore pid=52767)   File "/home/minimonk/.local/lib/python3.10/site-packages/vllm/v1/engine/core.py", line 880, in __init__
(EngineCore pid=52767)     super().__init__(
(EngineCore pid=52767)   File "/home/minimonk/.local/lib/python3.10/site-packages/vllm/v1/engine/core.py", line 118, in __init__
(EngineCore pid=52767)     self.model_executor = executor_class(vllm_config)
(EngineCore pid=52767)   File "/home/minimonk/.local/lib/python3.10/site-packages/vllm/tracing/otel.py", line 178, in sync_wrapper
(EngineCore pid=52767)     return func(*args, **kwargs)
(EngineCore pid=52767)   File "/home/minimonk/.local/lib/python3.10/site-packages/vllm/v1/executor/abstract.py", line 109, in __init__
(EngineCore pid=52767)     self._init_executor()
(EngineCore pid=52767)   File "/home/minimonk/.local/lib/python3.10/site-packages/vllm/v1/executor/uniproc_executor.py", line 60, in _init_executor
(EngineCore pid=52767)     self.driver_worker.init_device()
(EngineCore pid=52767)   File "/home/minimonk/.local/lib/python3.10/site-packages/vllm/v1/worker/worker_base.py", line 317, in init_device
(EngineCore pid=52767)     self.worker.init_device()  # type: ignore
(EngineCore pid=52767)   File "/home/minimonk/.local/lib/python3.10/site-packages/vllm/tracing/otel.py", line 178, in sync_wrapper
(EngineCore pid=52767)     return func(*args, **kwargs)
(EngineCore pid=52767)   File "/home/minimonk/.local/lib/python3.10/site-packages/vllm/v1/worker/gpu_worker.py", line 330, in init_device
(EngineCore pid=52767)     self.model_runner = GPUModelRunnerV1(self.vllm_config, self.device)
(EngineCore pid=52767)   File "/home/minimonk/.local/lib/python3.10/site-packages/vllm/v1/worker/gpu_model_runner.py", line 629, in __init__
(EngineCore pid=52767)     self.input_batch = InputBatch(
(EngineCore pid=52767)   File "/home/minimonk/.local/lib/python3.10/site-packages/vllm/v1/worker/gpu_input_batch.py", line 171, in __init__
(EngineCore pid=52767)     self.block_table = MultiGroupBlockTable(
(EngineCore pid=52767)   File "/home/minimonk/.local/lib/python3.10/site-packages/vllm/v1/worker/block_table.py", line 267, in __init__
(EngineCore pid=52767)     self.block_tables = [
(EngineCore pid=52767)   File "/home/minimonk/.local/lib/python3.10/site-packages/vllm/v1/worker/block_table.py", line 268, in <listcomp>
(EngineCore pid=52767)     BlockTable(
(EngineCore pid=52767)   File "/home/minimonk/.local/lib/python3.10/site-packages/vllm/v1/worker/block_table.py", line 70, in __init__
(EngineCore pid=52767)     self.block_table = self._make_buffer(
(EngineCore pid=52767)   File "/home/minimonk/.local/lib/python3.10/site-packages/vllm/v1/worker/block_table.py", line 218, in _make_buffer
(EngineCore pid=52767)     return CpuGpuBuffer(
(EngineCore pid=52767)   File "/home/minimonk/.local/lib/python3.10/site-packages/vllm/v1/utils.py", line 120, in __init__
(EngineCore pid=52767)     self.gpu = torch.zeros_like(self.cpu, device=device)
(EngineCore pid=52767) torch.AcceleratorError: CUDA error: no kernel image is available for execution on the device
(EngineCore pid=52767) Search for `cudaErrorNoKernelImageForDevice' in https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__TYPES.html for more information.
(EngineCore pid=52767) CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.
(EngineCore pid=52767) For debugging consider passing CUDA_LAUNCH_BLOCKING=1
(EngineCore pid=52767) Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.
(EngineCore pid=52767)
[rank0]:[W524 22:21:32.199264489 ProcessGroupNCCL.cpp:1575] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.
org/docs/stable/distributed.html#shutdown (function operator())
(APIServer pid=52696) Traceback (most recent call last):
(APIServer pid=52696)   File "/home/minimonk/.local/bin/vllm", line 8, in <module>
(APIServer pid=52696)     sys.exit(main())
(APIServer pid=52696)   File "/home/minimonk/.local/lib/python3.10/site-packages/vllm/entrypoints/cli/main.py", line 92, in main
(APIServer pid=52696)     args.dispatch_function(args)
(APIServer pid=52696)   File "/home/minimonk/.local/lib/python3.10/site-packages/vllm/entrypoints/cli/serve.py", line 122, in cmd
(APIServer pid=52696)     uvloop.run(run_server(args))
(APIServer pid=52696)   File "/home/minimonk/.local/lib/python3.10/site-packages/uvloop/__init__.py", line 69, in run
(APIServer pid=52696)     return loop.run_until_complete(wrapper())
(APIServer pid=52696)   File "uvloop/loop.pyx", line 1518, in uvloop.loop.Loop.run_until_complete
(APIServer pid=52696)   File "/home/minimonk/.local/lib/python3.10/site-packages/uvloop/__init__.py", line 48, in wrapper
(APIServer pid=52696)     return await main
(APIServer pid=52696)   File "/home/minimonk/.local/lib/python3.10/site-packages/vllm/entrypoints/openai/api_server.py", line 693, in run_server
(APIServer pid=52696)     await run_server_worker(listen_address, sock, args, **uvicorn_kwargs)
(APIServer pid=52696)   File "/home/minimonk/.local/lib/python3.10/site-packages/vllm/entrypoints/openai/api_server.py", line 707, in run_server_worker
(APIServer pid=52696)     async with build_async_engine_client(
(APIServer pid=52696)   File "/usr/lib/python3.10/contextlib.py", line 199, in __aenter__
(APIServer pid=52696)     return await anext(self.gen)
(APIServer pid=52696)   File "/home/minimonk/.local/lib/python3.10/site-packages/vllm/entrypoints/openai/api_server.py", line 100, in build_async_engine_client
(APIServer pid=52696)     async with build_async_engine_client_from_engine_args(
(APIServer pid=52696)   File "/usr/lib/python3.10/contextlib.py", line 199, in __aenter__
(APIServer pid=52696)     return await anext(self.gen)
(APIServer pid=52696)   File "/home/minimonk/.local/lib/python3.10/site-packages/vllm/entrypoints/openai/api_server.py", line 136, in build_async_engine_client_from_engine_args
(APIServer pid=52696)     async_llm = AsyncLLM.from_vllm_config(
(APIServer pid=52696)   File "/home/minimonk/.local/lib/python3.10/site-packages/vllm/v1/engine/async_llm.py", line 217, in from_vllm_config
(APIServer pid=52696)     return cls(
(APIServer pid=52696)   File "/home/minimonk/.local/lib/python3.10/site-packages/vllm/v1/engine/async_llm.py", line 146, in __init__
(APIServer pid=52696)     self.engine_core = EngineCoreClient.make_async_mp_client(
(APIServer pid=52696)   File "/home/minimonk/.local/lib/python3.10/site-packages/vllm/tracing/otel.py", line 178, in sync_wrapper
(APIServer pid=52696)     return func(*args, **kwargs)
(APIServer pid=52696)   File "/home/minimonk/.local/lib/python3.10/site-packages/vllm/v1/engine/core_client.py", line 130, in make_async_mp_client
(APIServer pid=52696)     return AsyncMPClient(*client_args)
(APIServer pid=52696)   File "/home/minimonk/.local/lib/python3.10/site-packages/vllm/tracing/otel.py", line 178, in sync_wrapper
(APIServer pid=52696)     return func(*args, **kwargs)
(APIServer pid=52696)   File "/home/minimonk/.local/lib/python3.10/site-packages/vllm/v1/engine/core_client.py", line 900, in __init__
(APIServer pid=52696)     super().__init__(
(APIServer pid=52696)   File "/home/minimonk/.local/lib/python3.10/site-packages/vllm/v1/engine/core_client.py", line 535, in __init__
(APIServer pid=52696)     with launch_core_engines(
(APIServer pid=52696)   File "/usr/lib/python3.10/contextlib.py", line 142, in __exit__
(APIServer pid=52696)     next(self.gen)
(APIServer pid=52696)   File "/home/minimonk/.local/lib/python3.10/site-packages/vllm/v1/engine/utils.py", line 1128, in launch_core_engines
(APIServer pid=52696)     wait_for_engine_startup(
(APIServer pid=52696)   File "/home/minimonk/.local/lib/python3.10/site-packages/vllm/v1/engine/utils.py", line 1187, in wait_for_engine_startup
(APIServer pid=52696)     raise RuntimeError(
(APIServer pid=52696) RuntimeError: Engine core initialization failed. See root cause above. Failed core proc(s): {}

 

파스칼은 아예 지원 하드웨어에서 빼버린건가?

[링크 : https://docs.vllm.ai/en/latest/features/quantization/]

 

fork로 이런것도 존재하는데, 아래 pascal-pkgs-ci로 대체 된다고

[링크 : https://github.com/cduk/vllm-pascal]

 

도커로 시도해야하나..

[링크 : https://github.com/sasha0552/pascal-pkgs-ci]

    [링크 : https://github.com/vllm-project/vllm/issues/19542]

 

위의 도커를 불러오게 하면되려나? 볼륨은 로컬 캐싱에서 HF_HOME 으로 변경해주면 좋을듯

docker run -itd --name gemma4 \
    --ipc=host \
    --network host \
    --shm-size 16G \
    --gpus all \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    vllm/vllm-openai:latest \
        --model google/gemma-4-31B-it \
        --tensor-parallel-size 2 \
        --max-model-len 32768 \
        --gpu-memory-utilization 0.90 \
        --host 0.0.0.0 \
        --port 8000

[링크 : https://docs.vllm.ai/projects/recipes/en/latest/Google/Gemma4.html#docker-deployment]

 

아래와 같이 도커에서 그래픽 카드를 인식하지 못하면

docker: Error response from daemon: could not select device driver "" with capabilities: [[gpu]]

 

nvidia-container-toolkit을 설치하고 도커를 재기동하면 된단다

$ distribution=$(. /etc/os-release;echo $ID$VERSION_ID)    && curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add -    && curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list
$ sudo apt-get update && sudo apt-get install -y nvidia-container-toolkit
$ sudo systemctl restart docker

[링크 : https://bluecolorsky.tistory.com/110]

 

docker run -itd --name gemma4 \
    --ipc=host \
    --network host \
    --gpus all \
    -v /mnt/huggingface:/root/.cache/huggingface \
    ghcr.io/sasha0552/vllm\
        --model google/gemma-4-e4b-it \
        --tensor-parallel-size 2 \
        --max-model-len 131072\
        --gpu-memory-utilization 0.90 \
        --host 0.0.0.0 \
        --port 8000

[링크 : https://github.com/sasha0552/pascal-pkgs-ci/pkgs/container/vllm]

 

에라모르겠다 ㅋㅋ

$ docker ps -a
CONTAINER ID   IMAGE                    COMMAND                  CREATED          STATUS                      PORTS     NAMES
fdd1ba582924   ghcr.io/sasha0552/vllm   "python3 -m vllm.ent…"   42 seconds ago   Exited (1) 26 seconds ago             gemma4

[링크 : https://data-newbie.tistory.com/m/1012]

[링크 : https://coding-review.tistory.com/m/608]

 

+

2026.05.25

docker run -itd \
  --name gemma4 \
  --ipc=host \
  --network host \
  --gpus all \
  -v /mnt/huggingface:/root/.cache/huggingface \
  ghcr.io/sasha0552/vllm

 

문제없이 되는거 같으면서도

왜 qwen3-0.6B가 언급이 되지?

INFO 05-25 05:00:09 [__init__.py:241] Automatically detected platform cuda.
(APIServer pid=1) INFO 05-25 05:00:11 [api_server.py:1873] vLLM API server version 999.999.999
(APIServer pid=1) INFO 05-25 05:00:11 [utils.py:326] non-default args: {}
(APIServer pid=1) INFO 05-25 05:00:19 [__init__.py:742] Resolved architecture: Qwen3ForCausalLM
(APIServer pid=1) WARNING 05-25 05:00:19 [__init__.py:2828] Your device 'NVIDIA GeForce GTX 1080 Ti' (with compute capability 6.1) doesn't support torch.bfloat16. Falling back to torch.float16 for compatibility.
(APIServer pid=1) WARNING 05-25 05:00:19 [__init__.py:2879] Casting torch.bfloat16 to torch.float16.
(APIServer pid=1) INFO 05-25 05:00:19 [__init__.py:1774] Using max model len 40960
(APIServer pid=1) WARNING 05-25 05:00:19 [arg_utils.py:1806] Compute Capability < 8.0 is not supported by the V1 Engine. Falling back to V0.
(APIServer pid=1) WARNING 05-25 05:00:19 [arg_utils.py:1580] Chunked prefill is enabled by default for models with max_model_len > 32K. Chunked prefill might not work with some features or models. If you encounter any issues, please disable by launching with --enable-chunked-prefill=False.
(APIServer pid=1) INFO 05-25 05:00:20 [scheduler.py:222] Chunked prefill is enabled with max_num_batched_tokens=2048.
(APIServer pid=1) INFO 05-25 05:00:20 [api_server.py:295] Started engine process with PID 36
INFO 05-25 05:00:24 [__init__.py:241] Automatically detected platform cuda.
INFO 05-25 05:00:25 [llm_engine.py:222] Initializing a V0 LLM engine (v999.999.999) with config: model='Qwen/Qwen3-0.6B', speculative_config=None, tokenizer='Qwen/Qwen3-0.6B', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, override_neuron_config={}, tokenizer_revision=None, trust_remote_code=False, dtype=torch.float16, max_seq_len=40960, download_dir=None, load_format=auto, tensor_parallel_size=1, pipeline_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=False, kv_cache_dtype=auto, device_config=cuda, decoding_config=DecodingConfig(backend='auto', disable_fallback=False, disable_any_whitespace=False, disable_additional_properties=False, reasoning_backend=''), observability_config=ObservabilityConfig(show_hidden_metrics_for_version=None, otlp_traces_endpoint=None, collect_detailed_traces=None), seed=0, served_model_name=Qwen/Qwen3-0.6B, enable_prefix_caching=None, chunked_prefill_enabled=True, use_async_output_proc=True, pooler_config=None, compilation_config={"level":0,"debug_dump_path":"","cache_dir":"","backend":"","custom_ops":[],"splitting_ops":null,"use_inductor":true,"compile_sizes":[],"inductor_compile_config":{"enable_auto_functionalized_v2":false},"inductor_passes":{},"cudagraph_mode":0,"use_cudagraph":true,"cudagraph_num_of_warmups":0,"cudagraph_capture_sizes":[256,248,240,232,224,216,208,200,192,184,176,168,160,152,144,136,128,120,112,104,96,88,80,72,64,56,48,40,32,24,16,8,4,2,1],"cudagraph_copy_inputs":false,"full_cuda_graph":false,"pass_config":{"enable_fusion":false,"enable_noop":false},"max_capture_size":256,"local_cache_dir":null}, use_cached_outputs=True,
INFO 05-25 05:00:28 [cuda.py:374] Cannot use FlashAttention-2 backend for Volta and Turing GPUs.
INFO 05-25 05:00:28 [cuda.py:419] Using XFormers backend.
INFO 05-25 05:00:28 [parallel_state.py:1134] rank 0 in world size 1 is assigned as DP rank 0, PP rank 0, TP rank 0, EP rank 0
INFO 05-25 05:00:28 [model_runner.py:1080] Starting to load model Qwen/Qwen3-0.6B...
INFO 05-25 05:00:29 [weight_utils.py:296] Using model weights format ['*.safetensors']
INFO 05-25 05:00:29 [weight_utils.py:349] No model.safetensors.index.json found in remote.
Loading safetensors checkpoint shards:   0% Completed | 0/1 [00:00<?, ?it/s]
Loading safetensors checkpoint shards: 100% Completed | 1/1 [00:00<00:00,  3.47it/s]
Loading safetensors checkpoint shards: 100% Completed | 1/1 [00:00<00:00,  3.47it/s]

INFO 05-25 05:00:29 [default_loader.py:267] Loading weights took 0.32 seconds
INFO 05-25 05:00:30 [model_runner.py:1112] Model loading took 1.1201 GiB and 1.275699 seconds
INFO 05-25 05:00:31 [worker.py:296] Memory profiling takes 1.07 seconds
INFO 05-25 05:00:31 [worker.py:296] the current vLLM instance can use total_gpu_memory (10.90GiB) x gpu_memory_utilization (0.90) = 9.81GiB
INFO 05-25 05:00:31 [worker.py:296] model weights take 1.12GiB; non_torch_memory takes 0.04GiB; PyTorch activation peak memory takes 1.39GiB; the rest of the memory reserved for KV Cache is 7.26GiB.
INFO 05-25 05:00:31 [executor_base.py:114] # cuda blocks: 4247, # CPU blocks: 2340
INFO 05-25 05:00:31 [executor_base.py:119] Maximum concurrency for 40960 tokens per request: 1.66x
INFO 05-25 05:00:34 [model_runner.py:1383] Capturing cudagraphs for decoding. This may lead to unexpected consequences if the model is not static. To run the model in eager mode, set 'enforce_eager=True' or use '--enforce-eager' in the CLI. If out-of-memory error occurs during cudagraph capture, consider decreasing `gpu_memory_utilization` or switching to eager mode. You can also reduce the `max_num_seqs` as needed to decrease memory usage.
Capturing CUDA graph shapes: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 35/35 [00:12<00:00,  2.85it/s]
INFO 05-25 05:00:46 [model_runner.py:1535] Graph capturing finished in 12 secs, took 0.19 GiB
INFO 05-25 05:00:46 [llm_engine.py:417] init engine (profile, create kv cache, warmup model) took 16.41 seconds
(APIServer pid=1) INFO 05-25 05:00:46 [api_server.py:1679] Supported_tasks: ['generate']
(APIServer pid=1) WARNING 05-25 05:00:46 [__init__.py:1658] Default sampling parameters have been overridden by the model's Hugging Face generation config recommended from the model creator. If this is not intended, please relaunch vLLM instance with `--generation-config vllm`.
(APIServer pid=1) INFO 05-25 05:00:46 [serving_responses.py:124] Using default chat sampling params from model: {'temperature': 0.6, 'top_k': 20, 'top_p': 0.95}
(APIServer pid=1) INFO 05-25 05:00:47 [serving_chat.py:135] Using default chat sampling params from model: {'temperature': 0.6, 'top_k': 20, 'top_p': 0.95}
(APIServer pid=1) INFO 05-25 05:00:47 [serving_completion.py:77] Using default completion sampling params from model: {'temperature': 0.6, 'top_k': 20, 'top_p': 0.95}
(APIServer pid=1) INFO 05-25 05:00:47 [api_server.py:1948] Starting vLLM API server 0 on http://0.0.0.0:8000
(APIServer pid=1) INFO 05-25 05:00:47 [launcher.py:36] Available routes are:
(APIServer pid=1) INFO 05-25 05:00:47 [launcher.py:44] Route: /openapi.json, Methods: HEAD, GET
(APIServer pid=1) INFO 05-25 05:00:47 [launcher.py:44] Route: /docs, Methods: HEAD, GET
(APIServer pid=1) INFO 05-25 05:00:47 [launcher.py:44] Route: /docs/oauth2-redirect, Methods: HEAD, GET
(APIServer pid=1) INFO 05-25 05:00:47 [launcher.py:44] Route: /redoc, Methods: HEAD, GET
(APIServer pid=1) INFO 05-25 05:00:47 [launcher.py:44] Route: /health, Methods: GET
(APIServer pid=1) INFO 05-25 05:00:47 [launcher.py:44] Route: /load, Methods: GET
(APIServer pid=1) INFO 05-25 05:00:47 [launcher.py:44] Route: /ping, Methods: POST
(APIServer pid=1) INFO 05-25 05:00:47 [launcher.py:44] Route: /ping, Methods: GET
(APIServer pid=1) INFO 05-25 05:00:47 [launcher.py:44] Route: /tokenize, Methods: POST
(APIServer pid=1) INFO 05-25 05:00:47 [launcher.py:44] Route: /detokenize, Methods: POST
(APIServer pid=1) INFO 05-25 05:00:47 [launcher.py:44] Route: /v1/models, Methods: GET
(APIServer pid=1) INFO 05-25 05:00:47 [launcher.py:44] Route: /version, Methods: GET
(APIServer pid=1) INFO 05-25 05:00:47 [launcher.py:44] Route: /v1/responses, Methods: POST
(APIServer pid=1) INFO 05-25 05:00:47 [launcher.py:44] Route: /v1/responses/{response_id}, Methods: GET
(APIServer pid=1) INFO 05-25 05:00:47 [launcher.py:44] Route: /v1/responses/{response_id}/cancel, Methods: POST
(APIServer pid=1) INFO 05-25 05:00:47 [launcher.py:44] Route: /v1/chat/completions, Methods: POST
(APIServer pid=1) INFO 05-25 05:00:47 [launcher.py:44] Route: /v1/completions, Methods: POST
(APIServer pid=1) INFO 05-25 05:00:47 [launcher.py:44] Route: /v1/embeddings, Methods: POST
(APIServer pid=1) INFO 05-25 05:00:47 [launcher.py:44] Route: /pooling, Methods: POST
(APIServer pid=1) INFO 05-25 05:00:47 [launcher.py:44] Route: /classify, Methods: POST
(APIServer pid=1) INFO 05-25 05:00:47 [launcher.py:44] Route: /score, Methods: POST
(APIServer pid=1) INFO 05-25 05:00:47 [launcher.py:44] Route: /v1/score, Methods: POST
(APIServer pid=1) INFO 05-25 05:00:47 [launcher.py:44] Route: /v1/audio/transcriptions, Methods: POST
(APIServer pid=1) INFO 05-25 05:00:47 [launcher.py:44] Route: /v1/audio/translations, Methods: POST
(APIServer pid=1) INFO 05-25 05:00:47 [launcher.py:44] Route: /rerank, Methods: POST
(APIServer pid=1) INFO 05-25 05:00:47 [launcher.py:44] Route: /v1/rerank, Methods: POST
(APIServer pid=1) INFO 05-25 05:00:47 [launcher.py:44] Route: /v2/rerank, Methods: POST
(APIServer pid=1) INFO 05-25 05:00:47 [launcher.py:44] Route: /scale_elastic_ep, Methods: POST
(APIServer pid=1) INFO 05-25 05:00:47 [launcher.py:44] Route: /is_scaling_elastic_ep, Methods: POST
(APIServer pid=1) INFO 05-25 05:00:47 [launcher.py:44] Route: /invocations, Methods: POST
(APIServer pid=1) INFO 05-25 05:00:47 [launcher.py:44] Route: /metrics, Methods: GET
(APIServer pid=1) INFO:     Started server process [1]
(APIServer pid=1) INFO:     Waiting for application startup.
(APIServer pid=1) INFO:     Application startup complete.

 

파스칼 P40 용으로 시도하는데 여전히 안된다. 아놔.. 포기!

[링크 : https://github.com/uaysk/vllm-pascal]

 

$ vllm serve google/gemma-4-E4B-it
(APIServer pid=65016) INFO 05-25 21:22:17 [utils.py:306]
(APIServer pid=65016) INFO 05-25 21:22:17 [utils.py:306]        █     █     █▄   ▄█
(APIServer pid=65016) INFO 05-25 21:22:17 [utils.py:306]  ▄▄ ▄█ █     █     █ ▀▄▀ █  version 0.21.0
(APIServer pid=65016) INFO 05-25 21:22:17 [utils.py:306]   █▄█▀ █     █     █     █  model   google/gemma-4-E4B-it
(APIServer pid=65016) INFO 05-25 21:22:17 [utils.py:306]    ▀▀  ▀▀▀▀▀ ▀▀▀▀▀ ▀     ▀
(APIServer pid=65016) INFO 05-25 21:22:17 [utils.py:306]
(APIServer pid=65016) INFO 05-25 21:22:17 [utils.py:240] non-default args: {'model_tag': 'google/gemma-4-E4B-it', 'model': 'google/gemma-4-E4B-it'}
(APIServer pid=65016) Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.
config.json: 5.14kB [00:00, 4.19MB/s]
processor_config.json: 1.69kB [00:00, 6.85MB/s]
(APIServer pid=65016) INFO 05-25 21:22:19 [model.py:568] Resolved architecture: Gemma4ForConditionalGeneration
(APIServer pid=65016) WARNING 05-25 21:22:19 [model.py:1982] Your device 'NVIDIA GeForce GTX 1080 Ti' (with compute capability 6.1) doesn't support torch.bfloat16. Falling back to torch.float16 for compatibility.
(APIServer pid=65016) WARNING 05-25 21:22:19 [model.py:2035] Casting torch.bfloat16 to torch.float16.
(APIServer pid=65016) INFO 05-25 21:22:19 [model.py:1697] Using max model len 131072
(APIServer pid=65016) INFO 05-25 21:22:19 [config.py:101] Gemma4 model has heterogeneous head dimensions (head_dim=256, global_head_dim=512). Forcing TRITON_ATTN backend to prevent mixed-backend numerical divergence.
(APIServer pid=65016) INFO 05-25 21:22:19 [vllm.py:886] Asynchronous scheduling is enabled.
(APIServer pid=65016) INFO 05-25 21:22:19 [kernel.py:212] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native'])
tokenizer_config.json: 2.10kB [00:00, 2.10MB/s]
tokenizer.json: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 32.2M/32.2M [00:02<00:00, 11.8MB/s]
chat_template.jinja: 17.3kB [00:00, 13.0MB/s]
generation_config.json: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 208/208 [00:00<00:00, 1.66MB/s]
(EngineCore pid=65068) INFO 05-25 21:23:14 [core.py:109] Initializing a V1 LLM engine (v0.21.0) with config: model='google/gemma-4-E4B-it', speculative_config=None, tokenizer='google/gemma-4-E4B-it', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, tokenizer_revision=None, trust_remote_code=False, dtype=torch.float16, max_seq_len=131072, download_dir=None, load_format=auto, tensor_parallel_size=1, pipeline_parallel_size=1, data_parallel_size=1, decode_context_parallel_size=1, dcp_comm_backend=ag_rs, disable_custom_all_reduce=False, quantization=None, quantization_config=None, enforce_eager=False, enable_return_routed_experts=False, kv_cache_dtype=auto, device_config=cuda, structured_outputs_config=StructuredOutputsConfig(backend='auto', disable_any_whitespace=False, disable_additional_properties=False, reasoning_parser='', reasoning_parser_plugin='', enable_in_reasoning=False), observability_config=ObservabilityConfig(show_hidden_metrics_for_version=None, otlp_traces_endpoint=None, collect_detailed_traces=None, kv_cache_metrics=False, kv_cache_metrics_sample=0.01, cudagraph_metrics=False, enable_layerwise_nvtx_tracing=False, enable_mfu_metrics=False, enable_mm_processor_stats=False, enable_logging_iteration_details=False), seed=0, served_model_name=google/gemma-4-E4B-it, enable_prefix_caching=True, enable_chunked_prefill=True, pooler_config=None, compilation_config={'mode': <CompilationMode.VLLM_COMPILE: 3>, 'debug_dump_path': None, 'cache_dir': '', 'compile_cache_save_format': 'binary', 'backend': 'inductor', 'custom_ops': ['none'], 'ir_enable_torch_wrap': True, 'splitting_ops': ['vllm::unified_attention_with_output', 'vllm::unified_mla_attention_with_output', 'vllm::mamba_mixer2', 'vllm::mamba_mixer', 'vllm::short_conv', 'vllm::linear_attention', 'vllm::plamo2_mamba_mixer', 'vllm::gdn_attention_core', 'vllm::gdn_attention_core_xpu', 'vllm::olmo_hybrid_gdn_full_forward', 'vllm::kda_attention', 'vllm::sparse_attn_indexer', 'vllm::rocm_aiter_sparse_attn_indexer', 'vllm::deepseek_v4_attention', 'vllm::unified_kv_cache_update', 'vllm::unified_mla_kv_cache_update'], 'compile_mm_encoder': False, 'cudagraph_mm_encoder': False, 'encoder_cudagraph_token_budgets': [], 'encoder_cudagraph_max_vision_items_per_batch': 0, 'encoder_cudagraph_max_frames_per_batch': None, 'compile_sizes': [], 'compile_ranges_endpoints': [2048], 'inductor_compile_config': {'enable_auto_functionalized_v2': False, 'size_asserts': False, 'alignment_asserts': False, 'scalar_asserts': False, 'combo_kernels': True, 'benchmark_combo_kernel': True}, 'inductor_passes': {}, 'cudagraph_mode': <CUDAGraphMode.FULL_AND_PIECEWISE: (2, 1)>, 'cudagraph_num_of_warmups': 1, 'cudagraph_capture_sizes': [1, 2, 4, 8, 16, 24, 32, 40, 48, 56, 64, 72, 80, 88, 96, 104, 112, 120, 128, 136, 144, 152, 160, 168, 176, 184, 192, 200, 208, 216, 224, 232, 240, 248, 256, 272, 288, 304, 320, 336, 352, 368, 384, 400, 416, 432, 448, 464, 480, 496, 512], 'cudagraph_copy_inputs': False, 'cudagraph_specialize_lora': True, 'use_inductor_graph_partition': False, 'pass_config': {'fuse_norm_quant': False, 'fuse_act_quant': False, 'fuse_attn_quant': False, 'enable_sp': False, 'fuse_gemm_comms': False, 'fuse_allreduce_rms': False, 'fuse_act_padding': False}, 'max_cudagraph_capture_size': 512, 'dynamic_shapes_config': {'type': <DynamicShapesType.BACKED: 'backed'>, 'evaluate_guards': False, 'assume_32_bit_indexing': False}, 'local_cache_dir': None, 'fast_moe_cold_start': False, 'static_all_moe_layers': []}, kernel_config=KernelConfig(ir_op_priority=IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']), enable_flashinfer_autotune=False, moe_backend='auto')
(EngineCore pid=65068) Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.
(EngineCore pid=65068) /home/minimonk/.local/lib/python3.10/site-packages/torch/cuda/__init__.py:371: UserWarning: Found GPU0 NVIDIA GeForce GTX 1080 Ti which is of compute capability (CC) 6.1.
(EngineCore pid=65068) The following list shows the CCs this version of PyTorch was built for and the hardware CCs it supports:
(EngineCore pid=65068) - 7.5 which supports hardware CC >=7.5,<8.0
(EngineCore pid=65068) - 8.0 which supports hardware CC >=8.0,<9.0 except {8.7}
(EngineCore pid=65068) - 8.6 which supports hardware CC >=8.6,<9.0 except {8.7}
(EngineCore pid=65068) - 9.0 which supports hardware CC >=9.0,<10.0
(EngineCore pid=65068) - 10.0 which supports hardware CC >=10.0,<11.0 except {10.1}
(EngineCore pid=65068) - 12.0 which supports hardware CC >=12.0,<13.0
(EngineCore pid=65068) Please follow the instructions at https://pytorch.org/get-started/locally/ to install a PyTorch release that supports one of these CUDA versions: 12.6
(EngineCore pid=65068)   _warn_unsupported_code(d, device_cc, code_ccs)
(EngineCore pid=65068) /home/minimonk/.local/lib/python3.10/site-packages/torch/cuda/__init__.py:371: UserWarning: Found GPU1 NVIDIA GeForce GTX 1080 Ti which is of compute capability (CC) 6.1.
(EngineCore pid=65068) The following list shows the CCs this version of PyTorch was built for and the hardware CCs it supports:
(EngineCore pid=65068) - 7.5 which supports hardware CC >=7.5,<8.0
(EngineCore pid=65068) - 8.0 which supports hardware CC >=8.0,<9.0 except {8.7}
(EngineCore pid=65068) - 8.6 which supports hardware CC >=8.6,<9.0 except {8.7}
(EngineCore pid=65068) - 9.0 which supports hardware CC >=9.0,<10.0
(EngineCore pid=65068) - 10.0 which supports hardware CC >=10.0,<11.0 except {10.1}
(EngineCore pid=65068) - 12.0 which supports hardware CC >=12.0,<13.0
(EngineCore pid=65068) Please follow the instructions at https://pytorch.org/get-started/locally/ to install a PyTorch release that supports one of these CUDA versions: 12.6
(EngineCore pid=65068)   _warn_unsupported_code(d, device_cc, code_ccs)
(EngineCore pid=65068) /home/minimonk/.local/lib/python3.10/site-packages/torch/cuda/__init__.py:489: UserWarning:
(EngineCore pid=65068) NVIDIA GeForce GTX 1080 Ti with CUDA capability sm_61 is not compatible with the current PyTorch installation.
(EngineCore pid=65068) The current PyTorch install supports CUDA capabilities sm_75 sm_80 sm_86 sm_90 sm_100 sm_120.
(EngineCore pid=65068) If you want to use the NVIDIA GeForce GTX 1080 Ti GPU with PyTorch, please check the instructions at https://pytorch.org/get-started/locally/
(EngineCore pid=65068)
(EngineCore pid=65068)   queued_call()
(EngineCore pid=65068) INFO 05-25 21:23:20 [parallel_state.py:1410] world_size=1 rank=0 local_rank=0 distributed_init_method=tcp://192.168.40.238:39589 backend=nccl
(EngineCore pid=65068) INFO 05-25 21:23:20 [parallel_state.py:1723] rank 0 in world size 1 is assigned as DP rank 0, PP rank 0, PCP rank 0, TP rank 0, EP rank N/A, EPLB rank N/A
(EngineCore pid=65068) WARNING 05-25 21:23:21 [topk_topp_sampler.py:61] FlashInfer top-p/top-k sampling not supported on compute capability 6.1; falling back to PyTorch-native sampler. Set VLLM_USE_FLASHINFER_SAMPLER=0 to silence.
(EngineCore pid=65068) ERROR 05-25 21:23:21 [core.py:1140] EngineCore failed to start.
(EngineCore pid=65068) ERROR 05-25 21:23:21 [core.py:1140] Traceback (most recent call last):
(EngineCore pid=65068) ERROR 05-25 21:23:21 [core.py:1140]   File "/home/minimonk/.local/lib/python3.10/site-packages/vllm/v1/engine/core.py", line 1114, in run_engine_core
(EngineCore pid=65068) ERROR 05-25 21:23:21 [core.py:1140]     engine_core = EngineCoreProc(*args, engine_index=dp_rank, **kwargs)
(EngineCore pid=65068) ERROR 05-25 21:23:21 [core.py:1140]   File "/home/minimonk/.local/lib/python3.10/site-packages/vllm/tracing/otel.py", line 178, in sync_wrapper
(EngineCore pid=65068) ERROR 05-25 21:23:21 [core.py:1140]     return func(*args, **kwargs)
(EngineCore pid=65068) ERROR 05-25 21:23:21 [core.py:1140]   File "/home/minimonk/.local/lib/python3.10/site-packages/vllm/v1/engine/core.py", line 880, in __init__
(EngineCore pid=65068) ERROR 05-25 21:23:21 [core.py:1140]     super().__init__(
(EngineCore pid=65068) ERROR 05-25 21:23:21 [core.py:1140]   File "/home/minimonk/.local/lib/python3.10/site-packages/vllm/v1/engine/core.py", line 118, in __init__
(EngineCore pid=65068) ERROR 05-25 21:23:21 [core.py:1140]     self.model_executor = executor_class(vllm_config)
(EngineCore pid=65068) ERROR 05-25 21:23:21 [core.py:1140]   File "/home/minimonk/.local/lib/python3.10/site-packages/vllm/tracing/otel.py", line 178, in sync_wrapper
(EngineCore pid=65068) ERROR 05-25 21:23:21 [core.py:1140]     return func(*args, **kwargs)
(EngineCore pid=65068) ERROR 05-25 21:23:21 [core.py:1140]   File "/home/minimonk/.local/lib/python3.10/site-packages/vllm/v1/executor/abstract.py", line 109, in __init__
(EngineCore pid=65068) ERROR 05-25 21:23:21 [core.py:1140]     self._init_executor()
(EngineCore pid=65068) ERROR 05-25 21:23:21 [core.py:1140]   File "/home/minimonk/.local/lib/python3.10/site-packages/vllm/v1/executor/uniproc_executor.py", line 60, in _init_executor
(EngineCore pid=65068) ERROR 05-25 21:23:21 [core.py:1140]     self.driver_worker.init_device()
(EngineCore pid=65068) ERROR 05-25 21:23:21 [core.py:1140]   File "/home/minimonk/.local/lib/python3.10/site-packages/vllm/v1/worker/worker_base.py", line 317, in init_device
(EngineCore pid=65068) ERROR 05-25 21:23:21 [core.py:1140]     self.worker.init_device()  # type: ignore
(EngineCore pid=65068) ERROR 05-25 21:23:21 [core.py:1140]   File "/home/minimonk/.local/lib/python3.10/site-packages/vllm/tracing/otel.py", line 178, in sync_wrapper
(EngineCore pid=65068) ERROR 05-25 21:23:21 [core.py:1140]     return func(*args, **kwargs)
(EngineCore pid=65068) ERROR 05-25 21:23:21 [core.py:1140]   File "/home/minimonk/.local/lib/python3.10/site-packages/vllm/v1/worker/gpu_worker.py", line 330, in init_device
(EngineCore pid=65068) ERROR 05-25 21:23:21 [core.py:1140]     self.model_runner = GPUModelRunnerV1(self.vllm_config, self.device)
(EngineCore pid=65068) ERROR 05-25 21:23:21 [core.py:1140]   File "/home/minimonk/.local/lib/python3.10/site-packages/vllm/v1/worker/gpu_model_runner.py", line 629, in __init__
(EngineCore pid=65068) ERROR 05-25 21:23:21 [core.py:1140]     self.input_batch = InputBatch(
(EngineCore pid=65068) ERROR 05-25 21:23:21 [core.py:1140]   File "/home/minimonk/.local/lib/python3.10/site-packages/vllm/v1/worker/gpu_input_batch.py", line 171, in __init__
(EngineCore pid=65068) ERROR 05-25 21:23:21 [core.py:1140]     self.block_table = MultiGroupBlockTable(
(EngineCore pid=65068) ERROR 05-25 21:23:21 [core.py:1140]   File "/home/minimonk/.local/lib/python3.10/site-packages/vllm/v1/worker/block_table.py", line 267, in __init__
(EngineCore pid=65068) ERROR 05-25 21:23:21 [core.py:1140]     self.block_tables = [
(EngineCore pid=65068) ERROR 05-25 21:23:21 [core.py:1140]   File "/home/minimonk/.local/lib/python3.10/site-packages/vllm/v1/worker/block_table.py", line 268, in <listcomp>
(EngineCore pid=65068) ERROR 05-25 21:23:21 [core.py:1140]     BlockTable(
(EngineCore pid=65068) ERROR 05-25 21:23:21 [core.py:1140]   File "/home/minimonk/.local/lib/python3.10/site-packages/vllm/v1/worker/block_table.py", line 70, in __init__
(EngineCore pid=65068) ERROR 05-25 21:23:21 [core.py:1140]     self.block_table = self._make_buffer(
(EngineCore pid=65068) ERROR 05-25 21:23:21 [core.py:1140]   File "/home/minimonk/.local/lib/python3.10/site-packages/vllm/v1/worker/block_table.py", line 218, in _make_buffer
(EngineCore pid=65068) ERROR 05-25 21:23:21 [core.py:1140]     return CpuGpuBuffer(
(EngineCore pid=65068) ERROR 05-25 21:23:21 [core.py:1140]   File "/home/minimonk/.local/lib/python3.10/site-packages/vllm/v1/utils.py", line 120, in __init__
(EngineCore pid=65068) ERROR 05-25 21:23:21 [core.py:1140]     self.gpu = torch.zeros_like(self.cpu, device=device)
(EngineCore pid=65068) ERROR 05-25 21:23:21 [core.py:1140] torch.AcceleratorError: CUDA error: no kernel image is available for execution on the device
(EngineCore pid=65068) ERROR 05-25 21:23:21 [core.py:1140] Search for `cudaErrorNoKernelImageForDevice' in https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__TYPES.html for more information.
(EngineCore pid=65068) ERROR 05-25 21:23:21 [core.py:1140] CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.
(EngineCore pid=65068) ERROR 05-25 21:23:21 [core.py:1140] For debugging consider passing CUDA_LAUNCH_BLOCKING=1
(EngineCore pid=65068) ERROR 05-25 21:23:21 [core.py:1140] Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.
(EngineCore pid=65068) ERROR 05-25 21:23:21 [core.py:1140]
(EngineCore pid=65068) Process EngineCore:
(EngineCore pid=65068) Traceback (most recent call last):
(EngineCore pid=65068)   File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
(EngineCore pid=65068)     self.run()
(EngineCore pid=65068)   File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
(EngineCore pid=65068)     self._target(*self._args, **self._kwargs)
(EngineCore pid=65068)   File "/home/minimonk/.local/lib/python3.10/site-packages/vllm/v1/engine/core.py", line 1144, in run_engine_core
(EngineCore pid=65068)     raise e
(EngineCore pid=65068)   File "/home/minimonk/.local/lib/python3.10/site-packages/vllm/v1/engine/core.py", line 1114, in run_engine_core
(EngineCore pid=65068)     engine_core = EngineCoreProc(*args, engine_index=dp_rank, **kwargs)
(EngineCore pid=65068)   File "/home/minimonk/.local/lib/python3.10/site-packages/vllm/tracing/otel.py", line 178, in sync_wrapper
(EngineCore pid=65068)     return func(*args, **kwargs)
(EngineCore pid=65068)   File "/home/minimonk/.local/lib/python3.10/site-packages/vllm/v1/engine/core.py", line 880, in __init__
(EngineCore pid=65068)     super().__init__(
(EngineCore pid=65068)   File "/home/minimonk/.local/lib/python3.10/site-packages/vllm/v1/engine/core.py", line 118, in __init__
(EngineCore pid=65068)     self.model_executor = executor_class(vllm_config)
(EngineCore pid=65068)   File "/home/minimonk/.local/lib/python3.10/site-packages/vllm/tracing/otel.py", line 178, in sync_wrapper
(EngineCore pid=65068)     return func(*args, **kwargs)
(EngineCore pid=65068)   File "/home/minimonk/.local/lib/python3.10/site-packages/vllm/v1/executor/abstract.py", line 109, in __init__
(EngineCore pid=65068)     self._init_executor()
(EngineCore pid=65068)   File "/home/minimonk/.local/lib/python3.10/site-packages/vllm/v1/executor/uniproc_executor.py", line 60, in _init_executor
(EngineCore pid=65068)     self.driver_worker.init_device()
(EngineCore pid=65068)   File "/home/minimonk/.local/lib/python3.10/site-packages/vllm/v1/worker/worker_base.py", line 317, in init_device
(EngineCore pid=65068)     self.worker.init_device()  # type: ignore
(EngineCore pid=65068)   File "/home/minimonk/.local/lib/python3.10/site-packages/vllm/tracing/otel.py", line 178, in sync_wrapper
(EngineCore pid=65068)     return func(*args, **kwargs)
(EngineCore pid=65068)   File "/home/minimonk/.local/lib/python3.10/site-packages/vllm/v1/worker/gpu_worker.py", line 330, in init_device
(EngineCore pid=65068)     self.model_runner = GPUModelRunnerV1(self.vllm_config, self.device)
(EngineCore pid=65068)   File "/home/minimonk/.local/lib/python3.10/site-packages/vllm/v1/worker/gpu_model_runner.py", line 629, in __init__
(EngineCore pid=65068)     self.input_batch = InputBatch(
(EngineCore pid=65068)   File "/home/minimonk/.local/lib/python3.10/site-packages/vllm/v1/worker/gpu_input_batch.py", line 171, in __init__
(EngineCore pid=65068)     self.block_table = MultiGroupBlockTable(
(EngineCore pid=65068)   File "/home/minimonk/.local/lib/python3.10/site-packages/vllm/v1/worker/block_table.py", line 267, in __init__
(EngineCore pid=65068)     self.block_tables = [
(EngineCore pid=65068)   File "/home/minimonk/.local/lib/python3.10/site-packages/vllm/v1/worker/block_table.py", line 268, in <listcomp>
(EngineCore pid=65068)     BlockTable(
(EngineCore pid=65068)   File "/home/minimonk/.local/lib/python3.10/site-packages/vllm/v1/worker/block_table.py", line 70, in __init__
(EngineCore pid=65068)     self.block_table = self._make_buffer(
(EngineCore pid=65068)   File "/home/minimonk/.local/lib/python3.10/site-packages/vllm/v1/worker/block_table.py", line 218, in _make_buffer
(EngineCore pid=65068)     return CpuGpuBuffer(
(EngineCore pid=65068)   File "/home/minimonk/.local/lib/python3.10/site-packages/vllm/v1/utils.py", line 120, in __init__
(EngineCore pid=65068)     self.gpu = torch.zeros_like(self.cpu, device=device)
(EngineCore pid=65068) torch.AcceleratorError: CUDA error: no kernel image is available for execution on the device
(EngineCore pid=65068) Search for `cudaErrorNoKernelImageForDevice' in https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__TYPES.html for more information.
(EngineCore pid=65068) CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.
(EngineCore pid=65068) For debugging consider passing CUDA_LAUNCH_BLOCKING=1
(EngineCore pid=65068) Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.
(EngineCore pid=65068)
[rank0]:[W525 21:23:22.743241268 ProcessGroupNCCL.cpp:1575] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
(APIServer pid=65016) Traceback (most recent call last):
(APIServer pid=65016)   File "/home/minimonk/.local/bin/vllm", line 8, in <module>
(APIServer pid=65016)     sys.exit(main())
(APIServer pid=65016)   File "/home/minimonk/.local/lib/python3.10/site-packages/vllm/entrypoints/cli/main.py", line 92, in main
(APIServer pid=65016)     args.dispatch_function(args)
(APIServer pid=65016)   File "/home/minimonk/.local/lib/python3.10/site-packages/vllm/entrypoints/cli/serve.py", line 122, in cmd
(APIServer pid=65016)     uvloop.run(run_server(args))
(APIServer pid=65016)   File "/home/minimonk/.local/lib/python3.10/site-packages/uvloop/__init__.py", line 69, in run
(APIServer pid=65016)     return loop.run_until_complete(wrapper())
(APIServer pid=65016)   File "uvloop/loop.pyx", line 1518, in uvloop.loop.Loop.run_until_complete
(APIServer pid=65016)   File "/home/minimonk/.local/lib/python3.10/site-packages/uvloop/__init__.py", line 48, in wrapper
(APIServer pid=65016)     return await main
(APIServer pid=65016)   File "/home/minimonk/.local/lib/python3.10/site-packages/vllm/entrypoints/openai/api_server.py", line 693, in run_server
(APIServer pid=65016)     await run_server_worker(listen_address, sock, args, **uvicorn_kwargs)
(APIServer pid=65016)   File "/home/minimonk/.local/lib/python3.10/site-packages/vllm/entrypoints/openai/api_server.py", line 707, in run_server_worker
(APIServer pid=65016)     async with build_async_engine_client(
(APIServer pid=65016)   File "/usr/lib/python3.10/contextlib.py", line 199, in __aenter__
(APIServer pid=65016)     return await anext(self.gen)
(APIServer pid=65016)   File "/home/minimonk/.local/lib/python3.10/site-packages/vllm/entrypoints/openai/api_server.py", line 100, in build_async_engine_client
(APIServer pid=65016)     async with build_async_engine_client_from_engine_args(
(APIServer pid=65016)   File "/usr/lib/python3.10/contextlib.py", line 199, in __aenter__
(APIServer pid=65016)     return await anext(self.gen)
(APIServer pid=65016)   File "/home/minimonk/.local/lib/python3.10/site-packages/vllm/entrypoints/openai/api_server.py", line 136, in build_async_engine_client_from_engine_args
(APIServer pid=65016)     async_llm = AsyncLLM.from_vllm_config(
(APIServer pid=65016)   File "/home/minimonk/.local/lib/python3.10/site-packages/vllm/v1/engine/async_llm.py", line 217, in from_vllm_config
(APIServer pid=65016)     return cls(
(APIServer pid=65016)   File "/home/minimonk/.local/lib/python3.10/site-packages/vllm/v1/engine/async_llm.py", line 146, in __init__
(APIServer pid=65016)     self.engine_core = EngineCoreClient.make_async_mp_client(
(APIServer pid=65016)   File "/home/minimonk/.local/lib/python3.10/site-packages/vllm/tracing/otel.py", line 178, in sync_wrapper
(APIServer pid=65016)     return func(*args, **kwargs)
(APIServer pid=65016)   File "/home/minimonk/.local/lib/python3.10/site-packages/vllm/v1/engine/core_client.py", line 130, in make_async_mp_client
(APIServer pid=65016)     return AsyncMPClient(*client_args)
(APIServer pid=65016)   File "/home/minimonk/.local/lib/python3.10/site-packages/vllm/tracing/otel.py", line 178, in sync_wrapper
(APIServer pid=65016)     return func(*args, **kwargs)
(APIServer pid=65016)   File "/home/minimonk/.local/lib/python3.10/site-packages/vllm/v1/engine/core_client.py", line 900, in __init__
(APIServer pid=65016)     super().__init__(
(APIServer pid=65016)   File "/home/minimonk/.local/lib/python3.10/site-packages/vllm/v1/engine/core_client.py", line 535, in __init__
(APIServer pid=65016)     with launch_core_engines(
(APIServer pid=65016)   File "/usr/lib/python3.10/contextlib.py", line 142, in __exit__
(APIServer pid=65016)     next(self.gen)
(APIServer pid=65016)   File "/home/minimonk/.local/lib/python3.10/site-packages/vllm/v1/engine/utils.py", line 1128, in launch_core_engines
(APIServer pid=65016)     wait_for_engine_startup(
(APIServer pid=65016)   File "/home/minimonk/.local/lib/python3.10/site-packages/vllm/v1/engine/utils.py", line 1187, in wait_for_engine_startup
(APIServer pid=65016)     raise RuntimeError(
(APIServer pid=65016) RuntimeError: Engine core initialization failed. See root cause above. Failed core proc(s): {}
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