1
jetson nano 에는 nvidia-smi가 없어서 비슷한거 찾는데
gpt님께서 jtop을 점지해주심
| sudo apt install python3-pip sudo -H pip3 install -U jetson-stats sudo systemctl restart jtop.service jtop |
cuda 예제 빌드해서 갈구니까
GPU에 올라가긴 한다
| jetson nano 4GB / qwen3.5 0.8B (0) | 2026.07.20 |
|---|---|
| jetson nano 과 llama.cpp (가속, 빌드 포기) (0) | 2026.07.20 |
| jetson nano devloper kit SD card image(A02) (0) | 2026.07.20 |
| jetracer donkey car (0) | 2026.04.20 |
| jetson nano nvcc 빌드 (0) | 2026.04.06 |
콘솔로 하니 이상하게 종료시 엔터가 미친듯이 나와서 웹으로 시도.
vulkan 인데도 vulkan 가속을 못 받는지 cpu 100%로 돈다.
최대 성능으로 가동
| $ sudo nvpmodel -q NVPM WARN: fan mode is not set! NV Power Mode: MAXN 0 |
docker 로 서비스 실행
| $ sudo docker run -p 8080:8080 -v /home/jetson/llm:/models ghcr.io/ggml-org/llama.cpp:server-vulkan --chat-template-kwargs "{\"enable_thinking\": false}" -m /models/Qwen3.5-0.8B-Q4_0.gguf -c 4096 --port 8080 --host 0.0.0.0 warn: LLAMA_ARG_HOST environment variable is set, but will be overwritten by command line argument --host 0.00.136.037 W Setting 'enable_thinking' via --chat-template-kwargs is deprecated. Use --reasoning on / --reasoning off instead. 0.00.136.924 I cmn common_param: common_params_print_info: verbosity = 3 (adjust with the `-lv N` CLI arg) 0.00.138.536 W srv llama_server: ----------------- 0.00.138.551 W srv llama_server: CORS is set to allow all origins ('*') and no API key is set 0.00.138.552 W srv llama_server: this can be a security risk (cross-origin attacks) 0.00.138.553 W srv llama_server: more info: https://github.com/ggml-org/llama.cpp/pull/25655 0.00.138.554 W srv llama_server: ----------------- 0.00.139.997 I srv load_model: loading model '/models/Qwen3.5-0.8B-Q4_0.gguf' 0.04.582.967 I srv load_model: initializing, n_slots = 4, n_ctx_slot = 4096, kv_unified = 'true' 0.04.681.224 I srv llama_server: model loaded 0.04.681.254 I srv llama_server: listening on http://0.0.0.0:8080 0.19.911.095 I slot get_availabl: id 3 | task -1 | selected slot by LRU, t_last = -1 0.19.911.286 I slot launch_slot_: id 3 | task 0 | processing task, is_child = 0 0.22.612.244 I slot print_timing: id 3 | task 0 | prompt eval time = 1093.85 ms / 13 tokens ( 84.14 ms per token, 11.88 tokens per second) 0.22.612.263 I slot print_timing: id 3 | task 0 | eval time = 1606.96 ms / 12 tokens ( 133.91 ms per token, 7.47 tokens per second) 0.22.612.266 I slot print_timing: id 3 | task 0 | total time = 2700.81 ms / 25 tokens 0.22.612.276 I slot print_timing: id 3 | task 0 | graphs reused = 11 0.22.612.345 I slot release: id 3 | task 0 | stop processing: n_tokens = 24, truncated = 0 0.32.329.433 I slot get_availabl: id 3 | task -1 | selected slot by LCP similarity, sim_best = 0.243 (> 0.100 thold), f_keep = 0.375 0.32.406.612 I slot launch_slot_: id 3 | task 14 | processing task, is_child = 0 0.37.382.135 I slot print_timing: id 3 | task 14 | prompt eval time = 2254.95 ms / 28 tokens ( 80.53 ms per token, 12.42 tokens per second) 0.37.382.154 I slot print_timing: id 3 | task 14 | eval time = 2720.41 ms / 18 tokens ( 151.13 ms per token, 6.62 tokens per second) 0.37.382.157 I slot print_timing: id 3 | task 14 | total time = 4975.37 ms / 46 tokens 0.37.382.160 I slot print_timing: id 3 | task 14 | graphs reused = 27 0.37.382.205 I slot release: id 3 | task 14 | stop processing: n_tokens = 54, truncated = 0 0.52.606.713 I slot get_availabl: id 3 | task -1 | selected slot by LCP similarity, sim_best = 0.471 (> 0.100 thold), f_keep = 0.611 0.52.606.847 I slot launch_slot_: id 3 | task 35 | processing task, is_child = 0 1.10.385.311 I slot print_timing: id 3 | task 35 | n_decoded = 100, tg = 6.70 t/s, tg_3s = 6.70 t/s 1.11.031.932 I slot print_timing: id 3 | task 35 | prompt eval time = 2847.89 ms / 37 tokens ( 76.97 ms per token, 12.99 tokens per second) 1.11.031.951 I slot print_timing: id 3 | task 35 | eval time = 15577.07 ms / 104 tokens ( 149.78 ms per token, 6.68 tokens per second) 1.11.031.954 I slot print_timing: id 3 | task 35 | total time = 18424.96 ms / 141 tokens 1.11.031.960 I slot print_timing: id 3 | task 35 | graphs reused = 129 1.11.032.011 I slot release: id 3 | task 35 | stop processing: n_tokens = 173, truncated = 0 3.50.029.790 I slot get_availabl: id 3 | task -1 | selected slot by LCP similarity, sim_best = 0.333 (> 0.100 thold), f_keep = 0.382 3.50.175.603 I slot launch_slot_: id 3 | task 142 | processing task, is_child = 0 3.57.905.059 I slot print_timing: id 3 | task 142 | prompt processing, n_tokens = 105, progress = 0.86, t = 7.73 s / 13.58 tokens per second 3.59.618.519 I slot print_timing: id 3 | task 142 | prompt processing, n_tokens = 128, progress = 0.98, t = 9.44 s / 13.56 tokens per second 4.10.877.834 I slot print_timing: id 3 | task 142 | prompt eval time = 9819.15 ms / 132 tokens ( 74.39 ms per token, 13.44 tokens per second) 4.10.877.855 I slot print_timing: id 3 | task 142 | eval time = 10882.92 ms / 72 tokens ( 151.15 ms per token, 6.62 tokens per second) 4.10.877.858 I slot print_timing: id 3 | task 142 | total time = 20702.07 ms / 204 tokens 4.10.877.862 I slot print_timing: id 3 | task 142 | graphs reused = 198 4.10.877.944 I slot release: id 3 | task 142 | stop processing: n_tokens = 269, truncated = 0 |
cpu 에서 10 token/sec 미만으로 나온다.
| msec | token | token/sec |
| 2700.81 (안녕) | 25 | 9.25648231456489 |
| 4975.37 (안녕) | 46 | 9.24554354751506 |
| 18424.96 (너에 대해 소개) | 141 | 7.65266247525096 |
| 20702.07 (golang으로 hello world) | 204 | 9.85408705506261 |
| jetson nano jtop (0) | 2026.07.20 |
|---|---|
| jetson nano 과 llama.cpp (가속, 빌드 포기) (0) | 2026.07.20 |
| jetson nano devloper kit SD card image(A02) (0) | 2026.07.20 |
| jetracer donkey car (0) | 2026.04.20 |
| jetson nano nvcc 빌드 (0) | 2026.04.06 |
by claude
| Jetson Nano 사양 Compute Capability: 5.3 Jetson Nano는 Maxwell 아키텍처 기반 GPU(128 CUDA 코어)를 사용하며, compute capability는 5.3입니다. CUDA 버전 Jetson Nano는 JetPack 버전에 따라 CUDA 버전이 결정됩니다. Nano에서 지원되는 마지막(최신) JetPack은 JetPack 4.6.x 계열이며, 이때 CUDA 10.2가 탑재됩니다. 참고로 Jetson Nano는 32비트 시절부터 나온 구형 라인업(2019년 출시)이라 JetPack 5.x(CUDA 11.x 이상)는 지원하지 않습니다. JetPack 5 이상은 Xavier, Orin 계열부터 지원됩니다 |
netson nano / maxwell 기반 128 cuda core
| GPU NVIDIA Maxwell architecture with 128 NVIDIA CUDA® cores CPU Quad-core ARM Cortex-A57 MPCore processor Memory 4 GB 64-bit LPDDR4, 1600MHz 25.6 GB/s |
[링크 : https://developer.nvidia.com/embedded/jetson-nano]
맥스웰 compute capability - 5.x
| all compute-capability 3.x (Kepler) devices but are not supported on compute-capability 5.x (Maxwell) |
[링크 : https://docs.nvidia.com/cuda/maxwell-compatibility-guide/]
cuda 10.2
| $ cat /etc/nv_tegra_release # R32 (release), REVISION: 7.1, GCID: 29818004, BOARD: t210ref, EABI: aarch64, DATE: Sat Feb 19 17:05:08 UTC 2022 $ nvcc --version nvcc: NVIDIA (R) Cuda compiler driver Copyright (c) 2005-2021 NVIDIA Corporation Built on Sun_Feb_28_22:34:44_PST_2021 Cuda compilation tools, release 10.2, V10.2.300 Build cuda_10.2_r440.TC440_70.29663091_0 |
b10068 인데 cuda 12 와 cuda 13에 대해서 지원한다.
소스에서 빌드한다고 해서 될 게 아닌듯..
| Windows: Windows x64 (CPU) Windows arm64 (CPU) Windows arm64 (OpenCL Adreno) Windows x64 (CUDA 12) - CUDA 12.4 DLLs Windows x64 (CUDA 13) - CUDA 13.3 DLLs Windows x64 (Vulkan) Windows x64 (OpenVINO) Windows x64 (SYCL) Windows x64 (HIP) |
[링크 : https://github.com/ggml-org/llama.cpp/releases]
+
이건 컴파일러 버전이 낮아서라고.. 에라이
| [ 5%] Building CXX object tools/ui/CMakeFiles/llama-ui-embed.dir/embed.cpp.o /home/jetson/src/llama.cpp/tools/ui/embed.cpp:17:10: fatal error: filesystem: No such file or directory #include <filesystem> ^~~~~~~~~~~~ compilation terminated. |
filesystem은 c+17에 도입되었고 8.x 부터 지원.
[링크 : https://jtrimind.github.io/troubleshooting/filesystem/]
| $ g++ --version g++ (Ubuntu/Linaro 7.5.0-3ubuntu1~18.04) 7.5.0 Copyright (C) 2017 Free Software Foundation, Inc. This is free software; see the source for copying conditions. There is NO warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. $ sudo apt install gcc-8 g++-8 $ sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-8 800 --slave /usr/bin/g++ g++ /usr/bin/g++-8 update-alternatives: using /usr/bin/gcc-8 to provide /usr/bin/gcc (gcc) in auto mode $ g++ --version g++ (Ubuntu/Linaro 8.4.0-1ubuntu1~18.04) 8.4.0 Copyright (C) 2018 Free Software Foundation, Inc. This is free software; see the source for copying conditions. There is NO warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. |
cmake 깔고 이상한짓 해도 안되서 포기. vulkan도 잘 안되는 것 같고 머지..?
| sudo apt-get install libssl-dev libssl1.1 /opt/cmake/bin/cmake -B build -DLLAMA_BUILD_SERVER=ON -DLLAMA_BUILD_TOOLS=OFF -DLLAMA_BUILD_UI=OFF /opt/cmake/bin/cmake --build build -j4 |
sharerc 빌드하고 하려니 이 난리. 에라이
| /home/jetson/src/shaderc/third_party/abseil_cpp/absl/base/policy_checks.h:59:2: error: #error "This package requires GCC 10 or higher." #error "This package requires GCC 10 or higher." |
| jetson nano jtop (0) | 2026.07.20 |
|---|---|
| jetson nano 4GB / qwen3.5 0.8B (0) | 2026.07.20 |
| jetson nano devloper kit SD card image(A02) (0) | 2026.07.20 |
| jetracer donkey car (0) | 2026.04.20 |
| jetson nano nvcc 빌드 (0) | 2026.04.06 |
jetpack 이 4.5.1 vs 4.6.1 이라서
많은건 안바라고(!) 용량만 좀 넉넉하면 좋겠네
[링크 : https://developer.nvidia.com/embedded/downloads]
이건 이전에 구워놨던 jetracer 용으로 만들어진 이미지
[링크 : https://github.com/NVIDIA-AI-IOT/jetracer/blob/master/docs/software_setup.md]
| jetson nano 4GB / qwen3.5 0.8B (0) | 2026.07.20 |
|---|---|
| jetson nano 과 llama.cpp (가속, 빌드 포기) (0) | 2026.07.20 |
| jetracer donkey car (0) | 2026.04.20 |
| jetson nano nvcc 빌드 (0) | 2026.04.06 |
| jetson nvcc 실행하기 (0) | 2026.04.06 |
일일이 다 뒤졌는데, b8599 부터 지원하는 듯.
물론 jetson nano 에서는 glic 버전 이슈로 돌아는 안간다. -_ㅠ
| $ ./llama-b8599/llama-cli ./llama-b8599/llama-cli: /lib/aarch64-linux-gnu/libm.so.6: version `GLIBC_2.38' not found (required by ./llama-b8599/llama-cli) ./llama-b8599/llama-cli: /lib/aarch64-linux-gnu/libm.so.6: version `GLIBC_2.29' not found (required by ./llama-b8599/llama-cli) ./llama-b8599/llama-cli: /usr/lib/aarch64-linux-gnu/libssl.so.3: version `OPENSSL_3.0.0' not found (required by ./llama-b8599/llama-cli) ./llama-b8599/llama-cli: /usr/lib/aarch64-linux-gnu/libcrypto.so.3: version `OPENSSL_3.0.0' not found (required by ./llama-b8599/llama-cli) ./llama-b8599/llama-cli: /usr/lib/aarch64-linux-gnu/libstdc++.so.6: version `GLIBCXX_3.4.32' not found (required by ./llama-b8599/llama-cli) ./llama-b8599/llama-cli: /usr/lib/aarch64-linux-gnu/libstdc++.so.6: version `GLIBCXX_3.4.30' not found (required by ./llama-b8599/llama-cli) ./llama-b8599/llama-cli: /usr/lib/aarch64-linux-gnu/libstdc++.so.6: version `CXXABI_1.3.15' not found (required by ./llama-b8599/llama-cli) ./llama-b8599/llama-cli: /usr/lib/aarch64-linux-gnu/libstdc++.so.6: version `GLIBCXX_3.4.29' not found (required by ./llama-b8599/llama-cli) ./llama-b8599/llama-cli: /usr/lib/aarch64-linux-gnu/libstdc++.so.6: version `CXXABI_1.3.13' not found (required by ./llama-b8599/llama-cli) ./llama-b8599/llama-cli: /usr/lib/aarch64-linux-gnu/libstdc++.so.6: version `GLIBCXX_3.4.26' not found (required by ./llama-b8599/llama-cli) ./llama-b8599/llama-cli: /lib/aarch64-linux-gnu/libc.so.6: version `GLIBC_2.32' not found (required by ./llama-b8599/llama-cli) ./llama-b8599/llama-cli: /lib/aarch64-linux-gnu/libc.so.6: version `GLIBC_2.34' not found (required by ./llama-b8599/llama-cli) ./llama-b8599/llama-cli: /lib/aarch64-linux-gnu/libc.so.6: version `GLIBC_2.33' not found (required by ./llama-b8599/llama-cli) ./llama-b8599/llama-cli: /lib/aarch64-linux-gnu/libc.so.6: version `GLIBC_2.38' not found (required by ./llama-b8599/llama-cli) ./llama-b8599/llama-cli: /lib/aarch64-linux-gnu/libm.so.6: version `GLIBC_2.29' not found (required by /home/jetson/llm/llama-b8599/libmtmd.so.0) ./llama-b8599/llama-cli: /lib/aarch64-linux-gnu/libc.so.6: version `GLIBC_2.38' not found (required by /home/jetson/llm/llama-b8599/libmtmd.so.0) ./llama-b8599/llama-cli: /usr/lib/aarch64-linux-gnu/libstdc++.so.6: version `GLIBCXX_3.4.29' not found (required by /home/jetson/llm/llama-b8599/libmtmd.so.0) ./llama-b8599/llama-cli: /lib/aarch64-linux-gnu/libm.so.6: version `GLIBC_2.29' not found (required by /home/jetson/llm/llama-b8599/libllama.so.0) ./llama-b8599/llama-cli: /lib/aarch64-linux-gnu/libc.so.6: version `GLIBC_2.33' not found (required by /home/jetson/llm/llama-b8599/libllama.so.0) ./llama-b8599/llama-cli: /lib/aarch64-linux-gnu/libc.so.6: version `GLIBC_2.32' not found (required by /home/jetson/llm/llama-b8599/libllama.so.0) ./llama-b8599/llama-cli: /lib/aarch64-linux-gnu/libc.so.6: version `GLIBC_2.34' not found (required by /home/jetson/llm/llama-b8599/libllama.so.0) ./llama-b8599/llama-cli: /lib/aarch64-linux-gnu/libc.so.6: version `GLIBC_2.38' not found (required by /home/jetson/llm/llama-b8599/libllama.so.0) ./llama-b8599/llama-cli: /usr/lib/aarch64-linux-gnu/libstdc++.so.6: version `GLIBCXX_3.4.29' not found (required by /home/jetson/llm/llama-b8599/libllama.so.0) ./llama-b8599/llama-cli: /usr/lib/aarch64-linux-gnu/libstdc++.so.6: version `CXXABI_1.3.13' not found (required by /home/jetson/llm/llama-b8599/libllama.so.0) ./llama-b8599/llama-cli: /usr/lib/aarch64-linux-gnu/libstdc++.so.6: version `GLIBCXX_3.4.26' not found (required by /home/jetson/llm/llama-b8599/libggml.so.0) ./llama-b8599/llama-cli: /lib/aarch64-linux-gnu/libc.so.6: version `GLIBC_2.32' not found (required by /home/jetson/llm/llama-b8599/libggml.so.0) ./llama-b8599/llama-cli: /lib/aarch64-linux-gnu/libc.so.6: version `GLIBC_2.34' not found (required by /home/jetson/llm/llama-b8599/libggml.so.0) ./llama-b8599/llama-cli: /usr/lib/aarch64-linux-gnu/libstdc++.so.6: version `GLIBCXX_3.4.29' not found (required by /home/jetson/llm/llama-b8599/libggml-base.so.0) ./llama-b8599/llama-cli: /lib/aarch64-linux-gnu/libc.so.6: version `GLIBC_2.38' not found (required by /home/jetson/llm/llama-b8599/libggml-base.so.0) |
[링크 : https://github.com/ggml-org/llama.cpp/releases/tag/b8599]
| qwen3.5 0.8B (0) | 2026.07.19 |
|---|---|
| LLM 지식 증류(knowledge distilation), lora (0) | 2026.07.18 |
| sLLM (경량 언어모델) (0) | 2026.07.18 |
| 딥러닝은 참.. 전기가 많이 드는구나.. (0) | 2026.07.16 |
| claude code /model (0) | 2026.07.08 |
모든 엔딩은 다 보았고
이름을 남기기 완료
자 10회차 가자고 ㅋㅋㅋ
| 니어 오토마타 엔딩 수집 완료 (0) | 2026.07.18 |
|---|---|
| 니어 오토마타 - 무기 3식 전술창 (0) | 2026.07.18 |
| 니어 오토마타 : 엔딩 Q, E 완료 (0) | 2026.07.14 |
| 니어 오토마타 - 무기 수집 / 엔딩 수집 (2) | 2026.07.12 |
| 니어 오토마타 - 에밀의 추억 (0) | 2026.07.11 |
테스트 해보니
IQ2_M 은 영어로 하던 한글로 하던 반복해서 쓸수가 없고
Q4 / Q8은 생각하지 않게하니 응답은 한다.
| llama-b10068-bin-win-cuda-12.4-x64>llama-cli.exe --chat-template-kwargs "{\"enable_thinking\": false}" -m Qwen3.5-0.8B-UD-IQ2_M.gguf |
1060 6GB
| 모델 | 파일 크기 | VRAM | tokens per second |
| Qwen3.5-0.8B-UD-IQ2_M.gguf | 354 MB | 3897 - 86 MiB | ~ 89 |
| Qwen3.5-0.8B-UD-Q4_K_XL.gguf | 532 MB | 4073 - 86 MiB | ~ 86 |
| Qwen3.5-0.8B-UD-Q8_K_XL.gguf | 1.10 GB | 4313 - 86 MiB | ~ 62 |
아래 처럼하면 엄청 줄일 순 있는데
| llama-b10068-bin-win-cuda-12.4-x64>llama-cli -c 4096 -ctk q4_0 --chat-template-kwargs "{\"enable_thinking\": false}" -m Qwen3.5-0.8B-UD-Q4_K_XL.gguf |
컨텍스트만 해도 이정도로 줄어든다.
-c 4096 797 MiB
-c 8192 861 MiB
-c 16384 953 MiB
Q8은 많이 줄어들진 않는다.
| llama-b10068-bin-win-cuda-12.4-x64>llama-cli -c 4096 --chat-template-kwargs "{\"enable_thinking\": false}" -m Qwen3.5-0.8B-UD-Q8_K_XL.gguf |
-c 4096 1395 MiB
-c 8192 1447 MiB
-c 16384 1551 MiB
그래도 이정도면 2GB 에서도 어떻게 돌릴순 있을듯 한데?
+
1187 MiB. 와우
ffprobe와 ffmpeg을 넣어줘도 big bunny 60MB 짜리를 못하네 쩝.. 어떻게 해야 돌릴수 있지?
| D:\study\llm\llama-b10068-bin-win-cuda-12.4-x64>llama-cli -c 4096 -ctk q4_0 --chat-template-kwargs "{\"enable_thinking\": false}" -m Qwen3.5-0.8B-UD-Q4_K_XL.gguf -mm mmproj-F16.gguf Loading model... ▄▄ ▄▄ ██ ██ ██ ██ ▀▀█▄ ███▄███▄ ▀▀█▄ ▄████ ████▄ ████▄ ██ ██ ▄█▀██ ██ ██ ██ ▄█▀██ ██ ██ ██ ██ ██ ██ ██ ▀█▄██ ██ ██ ██ ▀█▄██ ██ ▀████ ████▀ ████▀ ██ ██ ▀▀ ▀▀ build : b10068-571d0d540 model : Qwen3.5-0.8B-UD-Q4_K_XL.gguf ftype : Q4_K - Medium modalities : text, vision, video 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 /image <file> add an image file /video <file> add a video file |
+
2026.07.19
jetson nano에 하려고 했더니
ollama 업데이트 -> glibc 버전 문제로 먹통
llama.cpp -> libssl.so.3 / libcrypto.so.3 문제로 실행불가
빌드 하려니 32GB 짜리인데도 93% 라서 먼가 할수 없는 상황 -_-
| llama.cpp for arm64 (0) | 2026.07.20 |
|---|---|
| LLM 지식 증류(knowledge distilation), lora (0) | 2026.07.18 |
| sLLM (경량 언어모델) (0) | 2026.07.18 |
| 딥러닝은 참.. 전기가 많이 드는구나.. (0) | 2026.07.16 |
| claude code /model (0) | 2026.07.08 |
후.. 또 언제 탄막하고 10회차 가지? ㅋㅋㅋ
3식 전술창 얻고 강화하기 전
강화하고 나니 빨간점이 하나 생긴다.
에밀이 무엇을 결심할걸까
에일리언들이 지구 와서 겁나 후회했을 것 같은 비주얼 1
에일리언 : 아니 환공포증 아니아니 게슈탈트 붕괴 올뻔했다고 (어?!)
에일리언들이 지구 와서 겁나 후회했을 것 같은 비주얼 2
에일리언 : 아니 쟤네 눈에서 레이저가 나온다고!!! C8!!!!
이거 놓치면 안된다고 했는데
정작 못 잡아서 한 번 더 봄 ㅋㅋㅋㅋ
솔찍히 내가 만나던 에밀이 흑화해서 다 죽이겠다! 하는게 아니라서 다행이야 ㅠㅠ
| 굿바이 니어 오토마타? (1) | 2026.07.19 |
|---|---|
| 니어 오토마타 - 무기 3식 전술창 (0) | 2026.07.18 |
| 니어 오토마타 : 엔딩 Q, E 완료 (0) | 2026.07.14 |
| 니어 오토마타 - 무기 수집 / 엔딩 수집 (2) | 2026.07.12 |
| 니어 오토마타 - 에밀의 추억 (0) | 2026.07.11 |
어? Stale diffusion 에서 들었던 lora가 나오네? stable diffusion 만의 기술은 아니었나 보다.
그나저나 돌려볼 엄두도 안나긴 하지만.. 코드가 잘 보이지 않네. 한번 시도는 해보고 싶은디.
LoRA - Low Rank Adaptation
QLoRA - Quantized LoRa
[링크 : https://wikidocs.net/293565]
[링크 : https://news.hada.io/topic?id=28211]
[링크 : https://littlefoxdiary.tistory.com/m/134]
| llama.cpp for arm64 (0) | 2026.07.20 |
|---|---|
| qwen3.5 0.8B (0) | 2026.07.19 |
| sLLM (경량 언어모델) (0) | 2026.07.18 |
| 딥러닝은 참.. 전기가 많이 드는구나.. (0) | 2026.07.16 |
| claude code /model (0) | 2026.07.08 |
문득 젯슨 나노 4GB 모델에서 돌려볼만한 모델이 있나 찾아보는중.
2GB가 대부분이라 돌려볼 엄두도 안날것 같긴한데..
4GB 면은 그래도 1B 모델 정도는 가능하지 않을까 해서 검토
[링크 : https://huggingface.co/microsoft/Phi-3-mini-4k-instruct/]
[링크 : https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0/]
[링크 : https://www.jetson-ai-lab.com/models/gemma3-1b/]
[링크 : https://www.jetson-ai-lab.com/models/qwen3-5-0-8b/]
[링크 : https://huggingface.co/Qwen/Qwen3.5-0.8B]
[링크 : https://huggingface.co/unsloth/Qwen3.5-0.8B-GGUF] multimodal - image / video
[링크 : https://huggingface.co/unsloth/Qwen3.5-0.8B-MTP-GGUF]
[링크 : https://huggingface.co/unsloth/tinyllama/]
[링크 : https://huggingface.co/unsloth/gemma-2-2b]
[링크 : https://huggingface.co/unsloth/gemma-2b]
| qwen3.5 0.8B (0) | 2026.07.19 |
|---|---|
| LLM 지식 증류(knowledge distilation), lora (0) | 2026.07.18 |
| 딥러닝은 참.. 전기가 많이 드는구나.. (0) | 2026.07.16 |
| claude code /model (0) | 2026.07.08 |
| claude code 세션 정보 (0) | 2026.07.06 |
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