"함께해서 드러웠고 다시는 보지말자" 가 저절로 떠오르는 보스
괜히 암흑공주라고 욕 먹는게 아닌듯
사실상(?) 퀵 스왑 아니면 깰수가 없다.
실수로 한번 몰려서 얻어 맞으면 피 전부 회복해버리는데
퀵 스왑해서 발리스타랑 로켓런처 번갈아 가면서 쏴야지 1/3~1/2 갂을까 말까라 극딜만이 살길 ㅠㅠ
DLC 2개 까지 해서 47.4 시간. 올림해서 48시간 흐음..
어우 8.3%
| doom eternal , ancient god part 2 최종장 (0) | 2026.06.06 |
|---|---|
| doom eternal ancient god part 2 (0) | 2026.06.04 |
| doom eternal / dlc 1편 완료 (0) | 2026.05.28 |
| 둠 이터널 dlc / ancient god part 1 (0) | 2026.05.11 |
| 둠 이터널 노말 난이도 끝 (2) | 2026.01.24 |
어우. 왜 욕먹었은지 알 것 같네
보스가 왜케 쪼잔해
타이밍 이외에 쏘면 지 체력 회복하고
보스급 들 막 소환하지 않나 너무하네
하다하다 빡쳐서 일단 공략보고 고민중
| doom eternal ancient god part 2 끝! (0) | 2026.06.07 |
|---|---|
| doom eternal ancient god part 2 (0) | 2026.06.04 |
| doom eternal / dlc 1편 완료 (0) | 2026.05.28 |
| 둠 이터널 dlc / ancient god part 1 (0) | 2026.05.11 |
| 둠 이터널 노말 난이도 끝 (2) | 2026.01.24 |
lighthttpd 보다 얼마나 더 가볍길래 micro를 붙인걸까?
C로 작성된 웹서버. rest 까지 지원은 무리겠지? ㅜㅠ
| openscad (0) | 2026.05.27 |
|---|---|
| tmux (0) | 2026.05.11 |
| appimage , AppImageLauncher (0) | 2026.04.20 |
| usb device tree viewer (0) | 2026.02.22 |
| exaone 경량 모델 (0) | 2026.02.04 |
전체 화면을 scaling 하는 방법. 일장일단이 있겠지만
화면 비율이 다르면 답없는 건 매한가지 ㅠㅠ
[링크 : https://doc.qt.io/archives/qt-5.15/qgraphicsproxywidget.html]
| QT 다국어 언어 설정 전파 (0) | 2026.06.05 |
|---|---|
| QT 자식 위젯으로 생성 / 부모 위젯 연결 (0) | 2026.06.05 |
| qt QTabWidget 사용 (0) | 2026.06.04 |
| qt 다국어지원 - 보이지 않는 메시지 추가하기 (0) | 2026.06.02 |
| QCombobox + 다국어 (0) | 2026.05.21 |
QComboBox 등으로 언어를 선택하고 app.installTranslator()를 호출하면
모든 위젯들에게 자동으로 changeLanguage()가 발송된다 (즉, 수동으로 언어 변경 메시지를 전체에 뿌릴 필요가 없다)
| int main(int argc, char *argv[]) { QApplication app(argc, argv); QTranslator myappTranslator; if (myappTranslator.load(QLocale::system(), u"myapp"_s, u"_"_s, u":/i18n"_s)) app.installTranslator(&myappTranslator); return app.exec(); } |
위젯들에게 각각 아래의 이벤트 핸들러를 추가해주면 되는데
헤더에는 protected: 에 override 해서 해주면되고
| protected: void resizeEvent(QResizeEvent *event) override; void changeEvent(QEvent *event) override; |
함수에서는 별거 없이 retranslateUi()를 호출해주면된다.
| void MyWidget::changeEvent(QEvent *event) { if (event->type() == QEvent::LanguageChange) { ui.retranslateUi(this); } else QWidget::changeEvent(event); } |
[링크 : https://doc.qt.io/qt-6/ko/i18n-source-translation.html#prepare-for-dynamic-language-changes]
확실히 이렇게 하니 시그널들 서로 연결한다고 고생안해도 되서 개꿀
| QGraphicsProxyWidget (0) | 2026.06.05 |
|---|---|
| QT 자식 위젯으로 생성 / 부모 위젯 연결 (0) | 2026.06.05 |
| qt QTabWidget 사용 (0) | 2026.06.04 |
| qt 다국어지원 - 보이지 않는 메시지 추가하기 (0) | 2026.06.02 |
| QCombobox + 다국어 (0) | 2026.05.21 |
위젯 생성시 this를 넣어서 하면, 자식으로 생성되어 별도의 창으로 뜨지 않는다.
| QWidget test = new QWidget(); // 독립된 창으로 뜸 test.show(); QWidget test2 = new QWidget(this); // Mainwindow 안에 뜸 test2.show(); |
간단하게(?) parent를 지정해주냐 안해주냐의 차이인듯.
| class test : public QWidget { Q_OBJECT public: explicit test(QWidget *parent = nullptr); ~test(); } |
| QGraphicsProxyWidget (0) | 2026.06.05 |
|---|---|
| QT 다국어 언어 설정 전파 (0) | 2026.06.05 |
| qt QTabWidget 사용 (0) | 2026.06.04 |
| qt 다국어지원 - 보이지 않는 메시지 추가하기 (0) | 2026.06.02 |
| QCombobox + 다국어 (0) | 2026.05.21 |
휴. 빡세고만 ㅠㅠ
| doom eternal ancient god part 2 끝! (0) | 2026.06.07 |
|---|---|
| doom eternal , ancient god part 2 최종장 (0) | 2026.06.06 |
| doom eternal / dlc 1편 완료 (0) | 2026.05.28 |
| 둠 이터널 dlc / ancient god part 1 (0) | 2026.05.11 |
| 둠 이터널 노말 난이도 끝 (2) | 2026.01.24 |
| chat template 강제 지정 (가장 흔한 해결책) bash ./llama-cli -m model.gguf \ --chat-template chatml \ # 또는 llama3, qwen, mistral 등 -p "<|im_start|>system\n너는 친절한 AI야<|im_end|>" GGUF 변환 시 chat template 명시적으로 넣기 (최신 llama.cpp) bash python convert_hf_to_gguf.py ./MyModel \ --outfile mymodel.gguf \ --chat-template chatml # 또는 llama3-1 등 |
[링크 : https://x.com/i/grok/share/1f9e9bbccc264a9cbde32f7a95fdb601]
변환 스크립트에서 도움말을 봐도 이렇다할게 없다. 빠졌나?(b9500)
| $ python3 convert_hf_to_gguf.py --help 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] [--mtp] [--no-mtp] [--mistral-format] [--disable-mistral-community-chat-template] [--sentence-transformers-dense-modules] [--fuse-gate-up-exps] [--fp8-as-q8] [model] Convert a huggingface model to a GGML compatible file positional arguments: model directory containing model file or huggingface repository ID (if --remote) options: -h, --help show this help message and exit --vocab-only extract only the vocab --outfile OUTFILE path to write to; default: based on input. {ftype} will be replaced by the outtype. --outtype {f32,f16,bf16,q8_0,tq1_0,tq2_0,auto} output format - use f32 for float32, f16 for float16, bf16 for bfloat16, q8_0 for Q8_0, tq1_0 or tq2_0 for ternary, and auto for the highest-fidelity 16-bit float type --bigendian model is executed on big endian machine --use-temp-file use the tempfile library while processing (helpful when running out of memory, process killed) --no-lazy use more RAM by computing all outputs before writing (use in case lazy evaluation is broken) --model-name MODEL_NAME name of the model --verbose increase output verbosity --split-max-tensors SPLIT_MAX_TENSORS max tensors in each split --split-max-size SPLIT_MAX_SIZE max size per split N(M|G) --dry-run only print out a split plan and exit, without writing any new files --no-tensor-first-split do not add tensors to the first split (disabled by default) --metadata METADATA Specify the path for an authorship metadata override file --print-supported-models Print the supported models --remote (Experimental) Read safetensors file remotely without downloading to disk. Config and tokenizer files will still be downloaded. To use this feature, you need to specify Hugging Face model repo name instead of a local directory. For example: 'HuggingFaceTB/SmolLM2-1.7B-Instruct'. Note: To access gated repo, set HF_TOKEN environment variable to your Hugging Face token. --mmproj Export multimodal projector (mmproj) for vision models. This will only work on some vision models. An 'mmproj-' prefix will be added to the output file name. --mtp Export only the multi-token prediction (MTP) head as a separate GGUF, suitable for use as a speculative draft. An 'mtp-' prefix will be added to the output file name. --no-mtp Exclude the multi-token prediction (MTP) head from the converted GGUF. Pair with --mtp on a second run to publish trunk and MTP as two files. Note: the split form duplicates embeddings, but even though the bundled default is more space-efficient overall, this allows differing quantization which may be more performant. --mistral-format Whether the model is stored following the Mistral format. --disable-mistral-community-chat-template Whether to disable usage of Mistral community chat templates. If set, use the Mistral official `mistral-common` library for tokenization and detokenization of Mistral models. Using `mistral-common` ensure correctness and zero-day support of tokenization for models converted from the Mistral format but requires to manually setup the tokenization server. --sentence-transformers-dense-modules Whether to include sentence-transformers dense modules. It can be used for sentence-transformers models, like google/embeddinggemma-300m. Default these modules are not included. --fuse-gate-up-exps Fuse gate_exps and up_exps tensors into a single gate_up_exps tensor for MoE models. --fp8-as-q8 Store tensors dequantized from FP8 as Q8_0 instead of BF16/F16. |
cli 에서는 --chat-template로 어떻게 될 것 같긴한데.. 다시 해봐야겠다.
| $ ./llama-cli --help ----- common params ----- -h, --help, --usage print usage and exit --version show version and build info -cl, --cache-list show list of models in cache --completion-bash print source-able bash completion script for llama.cpp -t, --threads N number of CPU threads to use during generation (default: -1) (env: LLAMA_ARG_THREADS) -tb, --threads-batch N number of threads to use during batch and prompt processing (default: same as --threads) -C, --cpu-mask M CPU affinity mask: arbitrarily long hex. Complements cpu-range (default: "") -Cr, --cpu-range lo-hi range of CPUs for affinity. Complements --cpu-mask --cpu-strict <0|1> use strict CPU placement (default: 0) --prio N set process/thread priority : low(-1), normal(0), medium(1), high(2), realtime(3) (default: 0) --poll <0...100> use polling level to wait for work (0 - no polling, default: 50) -Cb, --cpu-mask-batch M CPU affinity mask: arbitrarily long hex. Complements cpu-range-batch (default: same as --cpu-mask) -Crb, --cpu-range-batch lo-hi ranges of CPUs for affinity. Complements --cpu-mask-batch --cpu-strict-batch <0|1> use strict CPU placement (default: same as --cpu-strict) --prio-batch N set process/thread priority : 0-normal, 1-medium, 2-high, 3-realtime (default: 0) --poll-batch <0|1> use polling to wait for work (default: same as --poll) -c, --ctx-size N size of the prompt context (default: 0, 0 = loaded from model) (env: LLAMA_ARG_CTX_SIZE) -n, --predict, --n-predict N number of tokens to predict (default: -1, -1 = infinity) (env: LLAMA_ARG_N_PREDICT) -b, --batch-size N logical maximum batch size (default: 2048) (env: LLAMA_ARG_BATCH) -ub, --ubatch-size N physical maximum batch size (default: 512) (env: LLAMA_ARG_UBATCH) --keep N number of tokens to keep from the initial prompt (default: 0, -1 = all) --swa-full use full-size SWA cache (default: false) [(more info)](https://github.com/ggml-org/llama.cpp/pull/13194#issuecomment-2868343055) (env: LLAMA_ARG_SWA_FULL) -fa, --flash-attn [on|off|auto] set Flash Attention use ('on', 'off', or 'auto', default: 'auto') (env: LLAMA_ARG_FLASH_ATTN) -p, --prompt PROMPT prompt to start generation with; for system message, use -sys --perf, --no-perf whether to enable internal libllama performance timings (default: false) (env: LLAMA_ARG_PERF) -f, --file FNAME a file containing the prompt (default: none) -bf, --binary-file FNAME binary file containing the prompt (default: none) -e, --escape, --no-escape whether to process escapes sequences (\n, \r, \t, \', \", \\) (default: true) --rope-scaling {none,linear,yarn} RoPE frequency scaling method, defaults to linear unless specified by the model (env: LLAMA_ARG_ROPE_SCALING_TYPE) --rope-scale N RoPE context scaling factor, expands context by a factor of N (env: LLAMA_ARG_ROPE_SCALE) --rope-freq-base N RoPE base frequency, used by NTK-aware scaling (default: loaded from model) (env: LLAMA_ARG_ROPE_FREQ_BASE) --rope-freq-scale N RoPE frequency scaling factor, expands context by a factor of 1/N (env: LLAMA_ARG_ROPE_FREQ_SCALE) --yarn-orig-ctx N YaRN: original context size of model (default: 0 = model training context size) (env: LLAMA_ARG_YARN_ORIG_CTX) --yarn-ext-factor N YaRN: extrapolation mix factor (default: -1.00, 0.0 = full interpolation) (env: LLAMA_ARG_YARN_EXT_FACTOR) --yarn-attn-factor N YaRN: scale sqrt(t) or attention magnitude (default: -1.00) (env: LLAMA_ARG_YARN_ATTN_FACTOR) --yarn-beta-slow N YaRN: high correction dim or alpha (default: -1.00) (env: LLAMA_ARG_YARN_BETA_SLOW) --yarn-beta-fast N YaRN: low correction dim or beta (default: -1.00) (env: LLAMA_ARG_YARN_BETA_FAST) -kvo, --kv-offload, -nkvo, --no-kv-offload whether to enable KV cache offloading (default: enabled) (env: LLAMA_ARG_KV_OFFLOAD) --repack, -nr, --no-repack whether to enable weight repacking (default: enabled) (env: LLAMA_ARG_REPACK) --no-host bypass host buffer allowing extra buffers to be used (env: LLAMA_ARG_NO_HOST) -ctk, --cache-type-k TYPE KV cache data type for K allowed values: f32, f16, bf16, q8_0, q4_0, q4_1, iq4_nl, q5_0, q5_1 (default: f16) (env: LLAMA_ARG_CACHE_TYPE_K) -ctv, --cache-type-v TYPE KV cache data type for V allowed values: f32, f16, bf16, q8_0, q4_0, q4_1, iq4_nl, q5_0, q5_1 (default: f16) (env: LLAMA_ARG_CACHE_TYPE_V) -dt, --defrag-thold N KV cache defragmentation threshold (DEPRECATED) (env: LLAMA_ARG_DEFRAG_THOLD) -np, --parallel N number of parallel sequences to decode (default: 1) (env: LLAMA_ARG_N_PARALLEL) --rpc SERVERS comma-separated list of RPC servers (host:port) (env: LLAMA_ARG_RPC) --mlock force system to keep model in RAM rather than swapping or compressing (env: LLAMA_ARG_MLOCK) --mmap, --no-mmap whether to memory-map model. (if mmap disabled, slower load but may reduce pageouts if not using mlock) (default: enabled) (env: LLAMA_ARG_MMAP) -dio, --direct-io, -ndio, --no-direct-io use DirectIO if available. (default: disabled) (env: LLAMA_ARG_DIO) --numa TYPE attempt optimizations that help on some NUMA systems - distribute: spread execution evenly over all nodes - isolate: only spawn threads on CPUs on the node that execution started on - numactl: use the CPU map provided by numactl if run without this previously, it is recommended to drop the system page cache before using this see https://github.com/ggml-org/llama.cpp/issues/1437 (env: LLAMA_ARG_NUMA) -dev, --device <dev1,dev2,..> comma-separated list of devices to use for offloading (none = don't offload) use --list-devices to see a list of available devices (env: LLAMA_ARG_DEVICE) --list-devices print list of available devices and exit -ot, --override-tensor <tensor name pattern>=<buffer type>,... override tensor buffer type (env: LLAMA_ARG_OVERRIDE_TENSOR) -cmoe, --cpu-moe keep all Mixture of Experts (MoE) weights in the CPU (env: LLAMA_ARG_CPU_MOE) -ncmoe, --n-cpu-moe N keep the Mixture of Experts (MoE) weights of the first N layers in the CPU (env: LLAMA_ARG_N_CPU_MOE) -ngl, --gpu-layers, --n-gpu-layers N max. number of layers to store in VRAM, either an exact number, 'auto', or 'all' (default: auto) (env: LLAMA_ARG_N_GPU_LAYERS) -sm, --split-mode {none,layer,row,tensor} how to split the model across multiple GPUs, one of: - none: use one GPU only - layer (default): split layers and KV across GPUs (pipelined) - row: split weight across GPUs by rows (parallelized) - tensor: split weights and KV across GPUs (parallelized, EXPERIMENTAL) (env: LLAMA_ARG_SPLIT_MODE) -ts, --tensor-split N0,N1,N2,... fraction of the model to offload to each GPU, comma-separated list of proportions, e.g. 3,1 (env: LLAMA_ARG_TENSOR_SPLIT) -mg, --main-gpu INDEX the GPU to use for the model (with split-mode = none), or for intermediate results and KV (with split-mode = row) (default: 0) (env: LLAMA_ARG_MAIN_GPU) -fit, --fit [on|off] whether to adjust unset arguments to fit in device memory ('on' or 'off', default: 'on') (env: LLAMA_ARG_FIT) -fitt, --fit-target MiB0,MiB1,MiB2,... target margin per device for --fit, comma-separated list of values, single value is broadcast across all devices, default: 1024 (env: LLAMA_ARG_FIT_TARGET) -fitc, --fit-ctx N minimum ctx size that can be set by --fit option, default: 4096 (env: LLAMA_ARG_FIT_CTX) --check-tensors check model tensor data for invalid values (default: false) --override-kv KEY=TYPE:VALUE,... advanced option to override model metadata by key. to specify multiple overrides, either use comma-separated values. types: int, float, bool, str. example: --override-kv tokenizer.ggml.add_bos_token=bool:false,tokenizer.ggml.add_eos_token=bool:false --op-offload, --no-op-offload whether to offload host tensor operations to device (default: true) --lora FNAME path to LoRA adapter (use comma-separated values to load multiple adapters) --lora-scaled FNAME:SCALE,... path to LoRA adapter with user defined scaling (format: FNAME:SCALE,...) note: use comma-separated values --control-vector FNAME add a control vector note: use comma-separated values to add multiple control vectors --control-vector-scaled FNAME:SCALE,... add a control vector with user defined scaling SCALE note: use comma-separated values (format: FNAME:SCALE,...) --control-vector-layer-range START END layer range to apply the control vector(s) to, start and end inclusive -m, --model FNAME model path to load (env: LLAMA_ARG_MODEL) -mu, --model-url MODEL_URL model download url (default: unused) (env: LLAMA_ARG_MODEL_URL) -dr, --docker-repo [<repo>/]<model>[:quant] Docker Hub model repository. repo is optional, default to ai/. quant is optional, default to :latest. example: gemma3 (default: unused) (env: LLAMA_ARG_DOCKER_REPO) -hf, -hfr, --hf-repo <user>/<model>[:quant] Hugging Face model repository; quant is optional, case-insensitive, default to Q4_K_M, or falls back to the first file in the repo if Q4_K_M doesn't exist. mmproj is also downloaded automatically if available. to disable, add --no-mmproj example: ggml-org/GLM-4.7-Flash-GGUF:Q4_K_M (default: unused) (env: LLAMA_ARG_HF_REPO) -hff, --hf-file FILE Hugging Face model file. If specified, it will override the quant in --hf-repo (default: unused) (env: LLAMA_ARG_HF_FILE) -hfv, -hfrv, --hf-repo-v <user>/<model>[:quant] Hugging Face model repository for the vocoder model (default: unused) (env: LLAMA_ARG_HF_REPO_V) -hffv, --hf-file-v FILE Hugging Face model file for the vocoder model (default: unused) (env: LLAMA_ARG_HF_FILE_V) -hft, --hf-token TOKEN Hugging Face access token (default: value from HF_TOKEN environment variable) (env: HF_TOKEN) --log-disable Log disable --log-file FNAME Log to file (env: LLAMA_ARG_LOG_FILE) --log-colors [on|off|auto] Set colored logging ('on', 'off', or 'auto', default: 'auto') 'auto' enables colors when output is to a terminal (env: LLAMA_ARG_LOG_COLORS) -v, --verbose, --log-verbose Set verbosity level to infinity (i.e. log all messages, useful for debugging) --offline Offline mode: forces use of cache, prevents network access (env: LLAMA_ARG_OFFLINE) -lv, --verbosity, --log-verbosity N Set the verbosity threshold. Messages with a higher verbosity will be ignored. Values: - 0: generic output - 1: error - 2: warning - 3: info - 4: trace (more info) - 5: debug (default: 1) (env: LLAMA_ARG_LOG_VERBOSITY) --log-prefix, --no-log-prefix Enable prefix in log messages (env: LLAMA_ARG_LOG_PREFIX) --log-timestamps, --no-log-timestamps Enable timestamps in log messages (env: LLAMA_ARG_LOG_TIMESTAMPS) --spec-draft-type-k, -ctkd, --cache-type-k-draft TYPE KV cache data type for K for the draft model allowed values: f32, f16, bf16, q8_0, q4_0, q4_1, iq4_nl, q5_0, q5_1 (default: f16) (env: LLAMA_ARG_SPEC_DRAFT_CACHE_TYPE_K) --spec-draft-type-v, -ctvd, --cache-type-v-draft TYPE KV cache data type for V for the draft model allowed values: f32, f16, bf16, q8_0, q4_0, q4_1, iq4_nl, q5_0, q5_1 (default: f16) (env: LLAMA_ARG_SPEC_DRAFT_CACHE_TYPE_V) ----- sampling params ----- --samplers SAMPLERS samplers that will be used for generation in the order, separated by ';' (default: penalties;dry;top_n_sigma;top_k;typ_p;top_p;min_p;xtc;temperature) -s, --seed SEED RNG seed (default: -1, use random seed for -1) --sampler-seq, --sampling-seq SEQUENCE simplified sequence for samplers that will be used (default: edskypmxt) --ignore-eos ignore end of stream token and continue generating (implies --logit-bias EOS-inf) --temp, --temperature N temperature (default: 0.80) --top-k N top-k sampling (default: 40, 0 = disabled) (env: LLAMA_ARG_TOP_K) --top-p N top-p sampling (default: 0.95, 1.0 = disabled) --min-p N min-p sampling (default: 0.05, 0.0 = disabled) --top-nsigma, --top-n-sigma N top-n-sigma sampling (default: -1.00, -1.0 = disabled) --xtc-probability N xtc probability (default: 0.00, 0.0 = disabled) --xtc-threshold N xtc threshold (default: 0.10, 1.0 = disabled) --typical, --typical-p N locally typical sampling, parameter p (default: 1.00, 1.0 = disabled) --repeat-last-n N last n tokens to consider for penalize (default: 64, 0 = disabled, -1 = ctx_size) --repeat-penalty N penalize repeat sequence of tokens (default: 1.00, 1.0 = disabled) --presence-penalty N repeat alpha presence penalty (default: 0.00, 0.0 = disabled) --frequency-penalty N repeat alpha frequency penalty (default: 0.00, 0.0 = disabled) --dry-multiplier N set DRY sampling multiplier (default: 0.00, 0.0 = disabled) --dry-base N set DRY sampling base value (default: 1.75) --dry-allowed-length N set allowed length for DRY sampling (default: 2) --dry-penalty-last-n N set DRY penalty for the last n tokens (default: -1, 0 = disable, -1 = context size) --dry-sequence-breaker STRING add sequence breaker for DRY sampling, clearing out default breakers ('\n', ':', '"', '*') in the process; use "none" to not use any sequence breakers --adaptive-target N adaptive-p: select tokens near this probability (valid range 0.0 to 1.0; negative = disabled) (default: -1.00) [(more info)](https://github.com/ggml-org/llama.cpp/pull/17927) --adaptive-decay N adaptive-p: decay rate for target adaptation over time. lower values are more reactive, higher values are more stable. (valid range 0.0 to 0.99) (default: 0.90) --dynatemp-range N dynamic temperature range (default: 0.00, 0.0 = disabled) --dynatemp-exp N dynamic temperature exponent (default: 1.00) --mirostat N use Mirostat sampling. Top K, Nucleus and Locally Typical samplers are ignored if used. (default: 0, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0) --mirostat-lr N Mirostat learning rate, parameter eta (default: 0.10) --mirostat-ent N Mirostat target entropy, parameter tau (default: 5.00) -l, --logit-bias TOKEN_ID(+/-)BIAS modifies the likelihood of token appearing in the completion, i.e. `--logit-bias 15043+1` to increase likelihood of token ' Hello', or `--logit-bias 15043-1` to decrease likelihood of token ' Hello' --grammar GRAMMAR BNF-like grammar to constrain generations (see samples in grammars/ dir) --grammar-file FNAME file to read grammar from -j, --json-schema SCHEMA JSON schema to constrain generations (https://json-schema.org/), e.g. `{}` for any JSON object For schemas w/ external $refs, use --grammar + example/json_schema_to_grammar.py instead -jf, --json-schema-file FILE File containing a JSON schema to constrain generations (https://json-schema.org/), e.g. `{}` for any JSON object For schemas w/ external $refs, use --grammar + example/json_schema_to_grammar.py instead -bs, --backend-sampling enable backend sampling (experimental) (default: disabled) (env: LLAMA_ARG_BACKEND_SAMPLING) ----- speculative params ----- --spec-draft-hf, -hfd, -hfrd, --hf-repo-draft <user>/<model>[:quant] Same as --hf-repo, but for the draft model (default: unused) (env: LLAMA_ARG_SPEC_DRAFT_HF_REPO) --spec-draft-threads, -td, --threads-draft N number of threads to use during generation (default: same as --threads) --spec-draft-threads-batch, -tbd, --threads-batch-draft N number of threads to use during batch and prompt processing (default: same as --threads-draft) --spec-draft-cpu-mask, -Cd, --cpu-mask-draft M Draft model CPU affinity mask. Complements cpu-range-draft (default: same as --cpu-mask) --spec-draft-cpu-range, -Crd, --cpu-range-draft lo-hi Ranges of CPUs for affinity. Complements --cpu-mask-draft --spec-draft-cpu-strict, --cpu-strict-draft <0|1> Use strict CPU placement for draft model (default: same as --cpu-strict) --spec-draft-prio, --prio-draft N set draft process/thread priority : 0-normal, 1-medium, 2-high, 3-realtime (default: 0) --spec-draft-poll, --poll-draft <0|1> Use polling to wait for draft model work (default: same as --poll) --spec-draft-cpu-mask-batch, -Cbd, --cpu-mask-batch-draft M Draft model CPU affinity mask. Complements cpu-range-draft (default: same as --cpu-mask) --spec-draft-cpu-strict-batch, --cpu-strict-batch-draft <0|1> Use strict CPU placement for draft model (default: --cpu-strict-draft) --spec-draft-prio-batch, --prio-batch-draft N set draft process/thread priority : 0-normal, 1-medium, 2-high, 3-realtime (default: 0) --spec-draft-poll-batch, --poll-batch-draft <0|1> Use polling to wait for draft model work (default: --poll-draft) --spec-draft-override-tensor, -otd, --override-tensor-draft <tensor name pattern>=<buffer type>,... override tensor buffer type for draft model --spec-draft-cpu-moe, -cmoed, --cpu-moe-draft keep all Mixture of Experts (MoE) weights in the CPU for the draft model (env: LLAMA_ARG_SPEC_DRAFT_CPU_MOE) --spec-draft-n-cpu-moe, --spec-draft-ncmoe, -ncmoed, --n-cpu-moe-draft N keep the Mixture of Experts (MoE) weights of the first N layers in the CPU for the draft model (env: LLAMA_ARG_SPEC_DRAFT_N_CPU_MOE) --spec-draft-n-max N number of tokens to draft for speculative decoding (default: 3) (env: LLAMA_ARG_SPEC_DRAFT_N_MAX) --spec-draft-n-min N minimum number of draft tokens to use for speculative decoding (default: 0) (env: LLAMA_ARG_SPEC_DRAFT_N_MIN) --spec-draft-p-split, --draft-p-split P speculative decoding split probability (default: 0.10) (env: LLAMA_ARG_SPEC_DRAFT_P_SPLIT) --spec-draft-p-min, --draft-p-min P minimum speculative decoding probability (greedy) (default: 0.00) (env: LLAMA_ARG_SPEC_DRAFT_P_MIN) --spec-draft-backend-sampling, --no-spec-draft-backend-sampling offload draft sampling to the backend (default: enabled) (env: LLAMA_ARG_SPEC_DRAFT_BACKEND_SAMPLING) --spec-draft-device, -devd, --device-draft <dev1,dev2,..> comma-separated list of devices to use for offloading the draft model (none = don't offload) use --list-devices to see a list of available devices --spec-draft-ngl, -ngld, --gpu-layers-draft, --n-gpu-layers-draft N max. number of draft model layers to store in VRAM, either an exact number, 'auto', or 'all' (default: auto) (env: LLAMA_ARG_N_GPU_LAYERS_DRAFT) --spec-draft-model, -md, --model-draft FNAME draft model for speculative decoding (default: unused) (env: LLAMA_ARG_SPEC_DRAFT_MODEL) --spec-type none,draft-simple,draft-eagle3,draft-mtp,ngram-simple,ngram-map-k,ngram-map-k4v,ngram-mod,ngram-cache comma-separated list of types of speculative decoding to use (default: none) (env: LLAMA_ARG_SPEC_TYPE) --spec-ngram-mod-n-min N minimum number of ngram tokens to use for ngram-based speculative decoding (default: 48) --spec-ngram-mod-n-max N maximum number of ngram tokens to use for ngram-based speculative decoding (default: 64) --spec-ngram-mod-n-match N ngram-mod lookup length (default: 24) --spec-ngram-simple-size-n N ngram size N for ngram-simple speculative decoding, length of lookup n-gram (default: 12) --spec-ngram-simple-size-m N ngram size M for ngram-simple speculative decoding, length of draft m-gram (default: 48) --spec-ngram-simple-min-hits N minimum hits for ngram-simple speculative decoding (default: 1) --spec-ngram-map-k-size-n N ngram size N for ngram-map-k speculative decoding, length of lookup n-gram (default: 12) --spec-ngram-map-k-size-m N ngram size M for ngram-map-k speculative decoding, length of draft m-gram (default: 48) --spec-ngram-map-k-min-hits N minimum hits for ngram-map-k speculative decoding (default: 1) --spec-ngram-map-k4v-size-n N ngram size N for ngram-map-k4v speculative decoding, length of lookup n-gram (default: 12) --spec-ngram-map-k4v-size-m N ngram size M for ngram-map-k4v speculative decoding, length of draft m-gram (default: 48) --spec-ngram-map-k4v-min-hits N minimum hits for ngram-map-k4v speculative decoding (default: 1) --draft, --draft-n, --draft-max N the argument has been removed. use --spec-draft-n-max or --spec-ngram-mod-n-max (env: LLAMA_ARG_DRAFT_MAX) --draft-min, --draft-n-min N the argument has been removed. use --spec-draft-n-min or --spec-ngram-mod-n-min (env: LLAMA_ARG_DRAFT_MIN) ----- example-specific params ----- --verbose-prompt print a verbose prompt before generation (default: false) --display-prompt, --no-display-prompt whether to print prompt at generation (default: true) -co, --color [on|off|auto] Colorize output to distinguish prompt and user input from generations ('on', 'off', or 'auto', default: 'auto') 'auto' enables colors when output is to a terminal -ctxcp, --ctx-checkpoints, --swa-checkpoints N max number of context checkpoints to create per slot (default: 32)[(more info)](https://github.com/ggml-org/llama.cpp/pull/15293) (env: LLAMA_ARG_CTX_CHECKPOINTS) -cram, --cache-ram N set the maximum cache size in MiB (default: 8192, -1 - no limit, 0 - disable)[(more info)](https://github.com/ggml-org/llama.cpp/pull/16391) (env: LLAMA_ARG_CACHE_RAM) --context-shift, --no-context-shift whether to use context shift on infinite text generation (default: disabled) (env: LLAMA_ARG_CONTEXT_SHIFT) -sys, --system-prompt PROMPT system prompt to use with model (if applicable, depending on chat template) --show-timings, --no-show-timings whether to show timing information after each response (default: true) (env: LLAMA_ARG_SHOW_TIMINGS) -sysf, --system-prompt-file FNAME a file containing the system prompt (default: none) -r, --reverse-prompt PROMPT halt generation at PROMPT, return control in interactive mode -sp, --special special tokens output enabled (default: false) -cnv, --conversation, -no-cnv, --no-conversation whether to run in conversation mode: - does not print special tokens and suffix/prefix - interactive mode is also enabled (default: auto enabled if chat template is available) -st, --single-turn run conversation for a single turn only, then exit when done will not be interactive if first turn is predefined with --prompt (default: false) -mli, --multiline-input allows you to write or paste multiple lines without ending each in '\' --warmup, --no-warmup whether to perform warmup with an empty run (default: enabled) -mm, --mmproj FILE path to a multimodal projector file. see tools/mtmd/README.md note: if -hf is used, this argument can be omitted (env: LLAMA_ARG_MMPROJ) -mmu, --mmproj-url URL URL to a multimodal projector file. see tools/mtmd/README.md (env: LLAMA_ARG_MMPROJ_URL) --mmproj-auto, --no-mmproj, --no-mmproj-auto whether to use multimodal projector file (if available), useful when using -hf (default: enabled) (env: LLAMA_ARG_MMPROJ_AUTO) --mmproj-offload, --no-mmproj-offload whether to enable GPU offloading for multimodal projector (default: enabled) (env: LLAMA_ARG_MMPROJ_OFFLOAD) --image, --audio FILE path to an image or audio file. use with multimodal models, use comma-separated values for multiple files --image-min-tokens N minimum number of tokens each image can take, only used by vision models with dynamic resolution (default: read from model) (env: LLAMA_ARG_IMAGE_MIN_TOKENS) --image-max-tokens N maximum number of tokens each image can take, only used by vision models with dynamic resolution (default: read from model) (env: LLAMA_ARG_IMAGE_MAX_TOKENS) --chat-template-kwargs STRING sets additional params for the json template parser, must be a valid json object string, e.g. '{"key1":"value1","key2":"value2"}' (env: LLAMA_ARG_CHAT_TEMPLATE_KWARGS) --jinja, --no-jinja whether to use jinja template engine for chat (default: enabled) (env: LLAMA_ARG_JINJA) --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) --chat-template JINJA_TEMPLATE set custom jinja chat template (default: template taken from model's metadata) if suffix/prefix are specified, template will be disabled only commonly used templates are accepted (unless --jinja is set before this flag): list of built-in templates: bailing, bailing-think, bailing2, chatglm3, chatglm4, chatml, command-r, deepseek, deepseek-ocr, deepseek2, deepseek3, exaone-moe, exaone3, exaone4, falcon3, gemma, gigachat, glmedge, gpt-oss, granite, granite-4.0, granite-4.1, grok-2, hunyuan-dense, hunyuan-moe, hunyuan-vl, kimi-k2, llama2, llama2-sys, llama2-sys-bos, llama2-sys-strip, llama3, llama4, megrez, minicpm, mistral-v1, mistral-v3, mistral-v3-tekken, mistral-v7, mistral-v7-tekken, monarch, openchat, orion, pangu-embedded, phi3, phi4, rwkv-world, seed_oss, smolvlm, solar-open, vicuna, vicuna-orca, yandex, zephyr (env: LLAMA_ARG_CHAT_TEMPLATE) --chat-template-file JINJA_TEMPLATE_FILE set custom jinja chat template file (default: template taken from model's metadata) if suffix/prefix are specified, template will be disabled only commonly used templates are accepted (unless --jinja is set before this flag): list of built-in templates: bailing, bailing-think, bailing2, chatglm3, chatglm4, chatml, command-r, deepseek, deepseek-ocr, deepseek2, deepseek3, exaone-moe, exaone3, exaone4, falcon3, gemma, gigachat, glmedge, gpt-oss, granite, granite-4.0, granite-4.1, grok-2, hunyuan-dense, hunyuan-moe, hunyuan-vl, kimi-k2, llama2, llama2-sys, llama2-sys-bos, llama2-sys-strip, llama3, llama4, megrez, minicpm, mistral-v1, mistral-v3, mistral-v3-tekken, mistral-v7, mistral-v7-tekken, monarch, openchat, orion, pangu-embedded, phi3, phi4, rwkv-world, seed_oss, smolvlm, solar-open, vicuna, vicuna-orca, yandex, zephyr (env: LLAMA_ARG_CHAT_TEMPLATE_FILE) --skip-chat-parsing, --no-skip-chat-parsing force a pure content parser, even if a Jinja template is specified; model will output everything in the content section, including any reasoning and/or tool calls (default: disabled) (env: LLAMA_ARG_SKIP_CHAT_PARSING) --simple-io use basic IO for better compatibility in subprocesses and limited consoles --gpt-oss-20b-default use gpt-oss-20b (note: can download weights from the internet) --gpt-oss-120b-default use gpt-oss-120b (note: can download weights from the internet) --vision-gemma-4b-default use Gemma 3 4B QAT (note: can download weights from the internet) --vision-gemma-12b-default use Gemma 3 12B QAT (note: can download weights from the internet) --spec-default enable default speculative decoding config |
[링크 : https://www.sktenterprise.com/bizInsight/blogDetail/dev/10236]
[링크 : https://huggingface.co/google/gemma-1.1-2b-it]
| gemma 12b, tesla t4 16GB / 1080 ti 11GB * 2 (0) | 2026.06.04 |
|---|---|
| nvidia tesla t4 16GB (0) | 2026.06.02 |
| llama.cpp reasoning 옵션 (0) | 2026.06.01 |
| safetensors to gguf 일단 실패 (0) | 2026.06.01 |
| antigravity 2 (0) | 2026.05.28 |
플랫폼 llama.cpp B9500 vulkan / ubuntu 22.04 / 32GB
명령줄
| $ llama-b9500/llama-server --host 0.0.0.0 --model ./model/gemma4-12b/gemma-4-12b-it-Q4_0.gguf -mm ./model/gemma4-12b/mmproj-F16.gguf -sm layer |
결론 : t4가 이상하게 12B 모델은 힘을 못쓴다. e4b에 비하면 1080 ti 도 절반정도 성능.
하드웨어 nvidia tesla t4 16GB
| gemma-4 12B it Q4_0.gguf Reading Generation 25 tokens 1.5s 16.46 t/s gemma-4 12B it Q4_0.gguf Reading Generation 260 tokens 17s 15.23 t/s gemma-4 12B it Q4_0.gguf Reading Generation 1,262 tokens 1min 35s 13.23 t/s gemma-4 12B it UD Q2_K_XL.gguf Reading Generation 36 tokens 2.1s 17.06 t/s gemma-4 12B it UD Q2_K_XL.gguf Reading Generation 360 tokens 22s 16.29 t/s gemma-4 12B it UD Q2_K_XL.gguf Reading Generation 1,379 tokens 2min 2s 11.27 t/s |
하드웨어 1080 ti -sm none
| gemma-4 12B it Q4_0.gguf Reading Generation 25 tokens 0.9s 27.94 t/s gemma-4 12B it Q4_0.gguf Reading Generation 255 tokens 8.9s 28.78 t/s gemma-4 12B it Q4_0.gguf Reading Generation 1,404 tokens 55s 25.45 t/s gemma-4 12B it UD Q2_K_XL.gguf Reading Generation 29 tokens 1.2s 23.71 t/s gemma-4 12B it UD Q2_K_XL.gguf Reading Generation 373 tokens 16s 22.28 t/s gemma-4 12B it UD Q2_K_XL.gguf Reading Generation 806 tokens 37s 21.34 t/s (터짐) |
하드웨어 1080 ti -sm layer
| gemma-4 12B it Q4_0.gguf Reading Generation 25 tokens 0.8s 31.04 t/s gemma-4 12B it Q4_0.gguf Reading Generation 265 tokens 9.0s 29.60 t/s gemma-4 12B it Q4_0.gguf Reading Generation 1,340 tokens 54s 24.43 t/s gemma-4 12B it UD Q2_K_XL.gguf Reading Generation 31 tokens 1.3s 24.16 t/s gemma-4 12B it UD Q2_K_XL.gguf Reading Generation 263 tokens 11s 23.70 t/s gemma-4 12B it UD Q2_K_XL.gguf Reading Generation 620 tokens 29s 20.70 t/s (터짐) |
---------
llama.cpp 버전업을 해야 하려나..
| /mnt/Downloads$ llama-b9305/llama-server --host 0.0.0.0 --model ./model/gemma4-12b/gemma-4-12b-it-UD-Q2_K_XL.gguf -mm ./model/gemma4-12b/mmproj-F32.gguf -sm layer 0.00.253.692 I log_info: verbosity = 3 (adjust with the `-lv N` CLI arg) 0.00.253.695 I device_info: 0.00.260.623 I - Vulkan0 : Intel(R) UHD Graphics 630 (CFL GT2) (23816 MiB, 23816 MiB free) 0.00.266.859 I - Vulkan1 : NVIDIA GeForce GTX 1080 Ti (11510 MiB, 11247 MiB free) 0.00.274.269 I - Vulkan2 : NVIDIA GeForce GTX 1080 Ti (11510 MiB, 11389 MiB free) 0.00.274.274 I - CPU : Intel(R) Core(TM) i5-8500 CPU @ 3.00GHz (31754 MiB, 31754 MiB free) 0.00.274.306 I system_info: n_threads = 6 (n_threads_batch = 6) / 6 | CPU : SSE3 = 1 | SSSE3 = 1 | AVX = 1 | AVX2 = 1 | F16C = 1 | FMA = 1 | BMI2 = 1 | LLAMAFILE = 1 | OPENMP = 1 | REPACK = 1 | 0.00.274.309 I srv llama_server: n_parallel is set to auto, using n_parallel = 4 and kv_unified = true 0.00.274.334 I srv init: running without SSL 0.00.274.366 I srv init: using 8 threads for HTTP server 0.00.274.493 I srv start: binding port with default address family 0.00.275.753 I srv llama_server: loading model 0.00.275.766 I srv load_model: loading model './model/gemma4-12b/gemma-4-12b-it-UD-Q2_K_XL.gguf' 0.00.319.114 E mtmd_get_memory_usage: error: Failed to load CLIP model from ./model/gemma4-12b/mmproj-F32.gguf 0.00.319.119 E srv load_model: [mtmd] failed to get memory usage of mmproj 0.00.319.134 I common_init_result: fitting params to device memory ... 0.00.319.134 I common_init_result: (for bugs during this step try to reproduce them with -fit off, or provide --verbose logs if the bug only occurs with -fit on) 0.01.922.555 W load: control-looking token: 212 '</s>' was not control-type; this is probably a bug in the model. its type will be overridden 0.01.923.284 W load: control-looking token: 50 '<|tool_response>' was not control-type; this is probably a bug in the model. its type will be overridden 0.01.933.392 W load: control-looking token: 1 '<eos>' was not control-type; this is probably a bug in the model. its type will be overridden 0.01.966.022 W load: special_eog_ids contains '<|tool_response>', removing '</s>' token from EOG list 0.04.567.079 I common_init_from_params: warming up the model with an empty run - please wait ... (--no-warmup to disable) 0.04.903.218 E clip_init: failed to load model './model/gemma4-12b/mmproj-F32.gguf': load_hparams: unknown projector type: gemma4uv 0.04.903.588 E mtmd_init_from_file: error: Failed to load CLIP model from ./model/gemma4-12b/mmproj-F32.gguf 0.04.903.600 E srv load_model: failed to load multimodal model, './model/gemma4-12b/mmproj-F32.gguf' 0.04.903.603 I srv operator(): operator(): cleaning up before exit... 0.04.904.452 E srv llama_server: exiting due to model loading error |
b9500 까지 나왔으니 언넝 최신으로 ㄱㄱ
| mtmd: enable non-causal vision for gemma 4 unified (#24082) |
[링크 : https://github.com/ggml-org/llama.cpp/releases/tag/b9494]
1080 ti 에서 멀티모달은 일단 포기하고 -sm none 으로 테스트 한 결과는 아래와 같다.
| gemma-4 12B it Q4_0.gguf Reading Generation 302 tokens 10s 29.78 t/s gemma-4 12B it Q4_0.gguf Reading Generation 1,262 tokens 42s 29.96 t/s gemma-4 12B it Q4_0.gguf Reading Generation 2,390 tokens 1min 21s 29.47 t/s gemma-4 12B it UD Q2_K_XL.gguf Reading Generation 327 tokens 13s 24.12 t/s gemma-4 12B it UD Q2_K_XL.gguf Reading Generation 943 tokens 38s 24.48 t/s gemma-4 12B it UDQ2_K_XL.gguf Reading Generation 2,135 tokens 1min 38s 21.78 t/s |
파이썬 프로그램은 좀 생성한다 싶으면 터져서 무한반복해서 쓸 수 있나 모르겠다.
+
b9500 으로 하니 문제없이 실행된다.
| /mnt/Downloads$ llama-b9500/llama-server --host 0.0.0.0 --model ./model/gemma4-12b/gemma-4-12b-it-UD-Q2_K_XL.gguf -mm ./model/gemma4-12b/mmproj-F32.gguf -sm layer 0.00.007.970 I log_info: verbosity = 3 (adjust with the `-lv N` CLI arg) 0.00.007.972 I device_info: 0.00.007.994 I - CPU : Intel(R) Core(TM) i5-8500 CPU @ 3.00GHz (31754 MiB, 31754 MiB free) 0.00.008.016 I system_info: n_threads = 6 (n_threads_batch = 6) / 6 | CPU : SSE3 = 1 | SSSE3 = 1 | AVX = 1 | AVX2 = 1 | F16C = 1 | FMA = 1 | BMI2 = 1 | LLAMAFILE = 1 | OPENMP = 1 | REPACK = 1 | 0.00.008.019 I srv llama_server: n_parallel is set to auto, using n_parallel = 4 and kv_unified = true 0.00.008.053 I srv init: running without SSL 0.00.008.087 I srv init: using 8 threads for HTTP server 0.00.008.191 I srv start: binding port with default address family 0.00.009.347 I srv llama_server: loading model 0.00.009.369 I srv load_model: loading model './model/gemma4-12b/gemma-4-12b-it-UD-Q2_K_XL.gguf' 0.00.143.685 I srv load_model: [mtmd] estimated worst-case memory usage of mmproj is 373.20 MiB 0.00.143.699 I common_init_result: fitting params to device memory ... 0.00.143.699 I common_init_result: (for bugs during this step try to reproduce them with -fit off, or provide --verbose logs if the bug only occurs with -fit on) 0.00.982.328 I common_params_fit_impl: projected to use 8171 MiB of host memory vs. 31754 MiB of total host memory 0.01.591.123 W load: control-looking token: 212 '</s>' was not control-type; this is probably a bug in the model. its type will be overridden 0.01.591.878 W load: control-looking token: 50 '<|tool_response>' was not control-type; this is probably a bug in the model. its type will be overridden 0.01.602.087 W load: control-looking token: 1 '<eos>' was not control-type; this is probably a bug in the model. its type will be overridden 0.01.635.174 W load: special_eog_ids contains '<|tool_response>', removing '</s>' token from EOG list 0.04.047.792 I common_init_from_params: warming up the model with an empty run - please wait ... (--no-warmup to disable) 0.04.547.906 W init_audio: audio input is in experimental stage and may have reduced quality: https://github.com/ggml-org/llama.cpp/discussions/13759 0.04.547.912 I srv load_model: loaded multimodal model, './model/gemma4-12b/mmproj-F32.gguf' 0.04.547.935 I srv load_model: initializing slots, n_slots = 4 0.05.193.680 W common_speculative_init: no implementations specified for speculative decoding 0.05.193.688 I slot load_model: id 0 | task -1 | new slot, n_ctx = 131072 0.05.193.694 I slot load_model: id 1 | task -1 | new slot, n_ctx = 131072 0.05.193.694 I slot load_model: id 2 | task -1 | new slot, n_ctx = 131072 0.05.193.694 I slot load_model: id 3 | task -1 | new slot, n_ctx = 131072 0.05.193.753 I srv load_model: prompt cache is enabled, size limit: 8192 MiB 0.05.193.753 I srv load_model: use `--cache-ram 0` to disable the prompt cache 0.05.193.754 I srv load_model: for more info see https://github.com/ggml-org/llama.cpp/pull/16391 0.05.193.754 I srv load_model: context checkpoints enabled, max = 32, min spacing = 256 0.05.193.776 I srv init: idle slots will be saved to prompt cache and cleared upon starting a new task 0.05.202.571 I init: chat template, example_format: '<|turn>system <|think|> You are a helpful assistant<turn|> <|turn>user Hello<turn|> <|turn>model Hi there<turn|> <|turn>user How are you?<turn|> <|turn>model ' 0.05.203.449 I srv init: init: chat template, thinking = 1 0.05.203.479 I srv llama_server: model loaded 0.05.203.483 I srv llama_server: server is listening on http://0.0.0.0:8080 0.05.203.488 I srv update_slots: all slots are idle |
느려서 sm none 하니까 터진다. 머냐? llama.cpp 버전 올라가면서 문제인가.. 아니면 메모리 소모량이 늘은거냐..
| $ llama-b9500/llama-server --host 0.0.0.0 --model ./model/gemma4-12b/gemma-4-12b-it-UD-Q2_K_XL.gguf -mm ./model/gemma4-12b/mmproj-F16.gguf -sm none 0.00.007.825 I log_info: verbosity = 3 (adjust with the `-lv N` CLI arg) 0.00.007.827 I device_info: 0.00.007.849 I - CPU : Intel(R) Core(TM) i5-8500 CPU @ 3.00GHz (31754 MiB, 31754 MiB free) 0.00.007.871 I system_info: n_threads = 6 (n_threads_batch = 6) / 6 | CPU : SSE3 = 1 | SSSE3 = 1 | AVX = 1 | AVX2 = 1 | F16C = 1 | FMA = 1 | BMI2 = 1 | LLAMAFILE = 1 | OPENMP = 1 | REPACK = 1 | 0.00.007.873 I srv llama_server: n_parallel is set to auto, using n_parallel = 4 and kv_unified = true 0.00.007.906 I srv init: running without SSL 0.00.007.941 I srv init: using 8 threads for HTTP server 0.00.008.026 I srv start: binding port with default address family 0.00.009.238 I srv llama_server: loading model 0.00.009.273 I srv load_model: loading model './model/gemma4-12b/gemma-4-12b-it-UD-Q2_K_XL.gguf' 0.00.083.085 I srv load_model: [mtmd] estimated worst-case memory usage of mmproj is 239.14 MiB 0.00.083.101 I common_init_result: fitting params to device memory ... 0.00.083.101 I common_init_result: (for bugs during this step try to reproduce them with -fit off, or provide --verbose logs if the bug only occurs with -fit on) 0.00.352.254 E llama_prepare_model_devices: invalid value for main_gpu: 0 (available devices: 0) 0.00.355.698 E llama_model_load_from_file_impl: failed to load model 0.00.355.750 E common_fit_params: encountered an error while trying to fit params to free device memory: failed to load model 0.00.604.770 E llama_prepare_model_devices: invalid value for main_gpu: 0 (available devices: 0) 0.00.609.674 E llama_model_load_from_file_impl: failed to load model 0.00.609.680 E common_init_from_params: failed to load model './model/gemma4-12b/gemma-4-12b-it-UD-Q2_K_XL.gguf' 0.00.609.684 E srv load_model: failed to load model, './model/gemma4-12b/gemma-4-12b-it-UD-Q2_K_XL.gguf' 0.00.609.685 I srv operator(): operator(): cleaning up before exit... 0.00.610.314 E srv llama_server: exiting due to model loading error |
+
TESLA T4 / llama.cpp B9500 vulkan 사용
이상하게 낮게 나오네.
| gemma-4 12B it UD Q2_K_XL.gguf Reading Generation 36 tokens 2.1s 17.06 t/s gemma-4 12B it UD Q2_K_XL.gguf Reading Generation 360 tokens 22s 16.29 t/s gemma-4 12B it UD Q2_K_XL.gguf Reading Generation 1,379 tokens 2min 2s 11.27 t/s gemma-4 12B it Q4_0.gguf Reading Generation 25 tokens 1.5s 16.46 t/s gemma-4 12B it Q4_0.gguf Reading Generation 260 tokens 17s 15.23 t/s gemma-4 12B it Q4_0.gguf Reading Generation 1,262 tokens 1min 35s 13.23 t/s |
| chatML (0) | 2026.06.04 |
|---|---|
| nvidia tesla t4 16GB (0) | 2026.06.02 |
| llama.cpp reasoning 옵션 (0) | 2026.06.01 |
| safetensors to gguf 일단 실패 (0) | 2026.06.01 |
| antigravity 2 (0) | 2026.05.28 |
도움은 크게 안되었지만 아이디어를 얻은 페이지
[링크 : https://stackoverflow.com/questions/59594800/confining-background-color-to-triangular-tab-on-qtabbar]
Step 1. widget에 Tab Widget을 드래그 한다.
Step 2. Tab Widget 사이즈 조절. 만만한게 바로 grid 커져라 얍!
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Step 3. 탭하나 추가하기
원하는 탭을 하나 선택하고
상위 QTabWidget 에서 우클릭해서 Insert Page - After Current Page
그러면 tab_3 추가
gui 상에서 우클릭해도 바로 되긴한다.
| QT 다국어 언어 설정 전파 (0) | 2026.06.05 |
|---|---|
| QT 자식 위젯으로 생성 / 부모 위젯 연결 (0) | 2026.06.05 |
| qt 다국어지원 - 보이지 않는 메시지 추가하기 (0) | 2026.06.02 |
| QCombobox + 다국어 (0) | 2026.05.21 |
| qt widget 에서 배경화면 스타일 시트 적용 안될 경우 (0) | 2026.05.20 |
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