python3 -m vllm.entrypoints.openai.api_server \ --model Qwen/Qwen1.5-72B-Chat \ --tensor-parallel-size 8 \ --trust-remote-code \ --enable-prefix-caching \ # 开启vLLM Automatic Prefix Caching --enforce-eager \ --gpu-memory-utilization 0.9 0x09 Prefix Caching优化相关的其他论文 Prefix Ca...
max_seq_length = max_seq_length, load_in_4bit = True, fast_inference = True, max_lora_rank = lora_rank, gpu_memory_utilization = 0.6, float8_kv_cache = True, )如果想在vLLM中使用
model,tokenizer = FastLanguageModel.from_pretrained(model_name="meta-llama/meta-Llama-3.1-8B-Instruct",max_seq_length=max_seq_length,load_in_4bit=True,fast_inference=True,max_lora_rank=lora_rank,gpu_memory_utilization=0.6,float8_kv_cache=True,) 如果想在vLLM中使用min_p=0.1或其他采样参数,...
"vllm serve /model/DeepSeek-R1-Distill-Qwen-7B --port 8000 --trust-remote-code --served-model-name deepseek-r1 --max-model-len 32768 --gpu-memory-utilization 0.95 --enforce-eager" 特别说明:使用ACS GPU算力需要使用以下label来说明。 --label=alibabacloud.com/acs="true" --label=alibabaclou...
值得注意的是,VLLM会默认将显卡的全部显存预先申请以提高缓存大小和推理速度,用户可以通过参数gpu_memory_utilization控制缓存大小。 首先安装VLLM: pip install vllm 1. import os os.environ['VLLM_USE_MODELSCOPE'] = 'True' from vllm import LLM, SamplingParams ...
init_cache() File "/h2ogpt_conda/vllm_env/lib/python3.10/site-packages/vllm/engine/llm_engine.py", line 227, in _init_cache raise ValueError("No available memory for the cache blocks. " ValueError: No available memory for the cache blocks. Try increasing `gpu_memory_utilization` when ...
为了评估 LLM 训练时的效率,业界通常会使用Model FLOPS Utilization(MFU)和Hardware FLOPS Utilization(HFU)两个关键指标来评估模型的 Forward 和 Backward 过程中(包括任何的网络同步开销和 DataLoader IO)硬件的利用率。 MFU= 预估 FLOPS/硬件理论 FLOPS。其中,预估 FLOPS 就是模型训练时理论需要的计算量,并不包括各...
1 python -m vllm.entrypoints.openai.api_server --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.9 --max-model-len 8192 --model /gm-data/Qwen1.5-0.5B-Chat --tensor-parallel-size 1 参数说明: 6. curl命令调用 vLLM 接口 7. 使用Python调用 vLLM 接口 ...
vllm/engine/llm_engine.py", line 284, in _init_cache raise ValueError( ValueError: The model's max seq len (4096) is larger than the maximum number of tokens that can be stored in KV cache (3664). Try increasinggpu_memory_utilizationor decreasingmax_model_lenwhen initializing the engine...
Transformer Engine dramatically accelerates AI performance and improves memory utilization for both training and inference. Harnessing the power of the Ada Lovelace fourth-generation Tensor Cores, Transformer Engine intelligently scans the layers of transformer architecture neural networks and automatically recast...