在 Linux 中安装 vllm 和 部署大模型
环境：
- 操作系统：Debian 12 (在 windows 11 wsl 里的 linux)。
- 查看怎么在 wsl 安装 debian
- 查看 debian 镜像设置
- python 包管理 uv 怎么安装，并设置 uv 的 pypi 镜像。
- GPU：RTX 4090 x 2
- 内存：128GB
- CPU：24 核，32 线程。
安装 vllm
进入 Debian 先安装开发基础库。
bash
sudo apt install build-essential否则运行 vllm 报错：
console
raise BackendCompilerFailed(self.compiler_fn, e) from e
torch._dynamo.exc.BackendCompilerFailed: backend='inductor' raised:
RuntimeError: Failed to find C compiler. Please specify via CC environment variable.
Set TORCH_LOGS="+dynamo" and TORCHDYNAMO_VERBOSE=1 for more information
You can suppress this exception and fall back to eager by setting:
import torch._dynamo
torch._dynamo.config.suppress_errors = True开始安装 vllm。
bash
mkdir ~/vllm
cd ~/vllm
# 查看有没有 python，已经有了就不用安装了。
uv python list
# uv 安装 python。也可以使用系统来安装（比 uv 安装要快）
# 设置 uv 安装 python 目录。也可以使用系统自己的 python
# echo "export UV_PYTHON_INSTALL_DIR=/mnt/d/debian/python" >> ~/.bashrc
# uv python install 3.12
# uv 创建 python 虚拟环境
# https://docs.vllm.ai/en/stable/getting_started/installation/gpu/index.html
# --seed 参数，是把 pip 也放到虚拟环境
uv venv -p 3.12 --seed
source .venv/bin/activate
# 设置 uv 下载超时（单位：秒）
echo "export UV_HTTP_TIMEOUT=180" >> ~/.bashrc
source ~/.bashrc
# 安装 vllm
uv pip install vllm下载 Qwen 和 DeepSeek
两张 4090 显卡可以部署的模型有：
- 14B
- Qwen2.5-14B-Instruct
- DeepSeek-R1-Distill-Qwen-14B
- 32B-AWQ
- Qwen2.5-32B-Instruct-AWQ
- QwQ-32B-AWQ
改变 vllm 的模型源
vllm 默认使用 HuggingFace 的源。国内访问很慢。好在 vllm 可以使用 Modelscope 的模型源。
bash
# 安装 modelscope
uv pip install modelscope
# 设置 modelscope
# https://modelscope.cn/docs/models/download
# 默认 ~/.cache/modelscope/hub 目录下。可以设置 MODELSCOPE_CACHE 环境变量。
# 先用 modelscope 下载，（模型按需选择， https://modelscope.cn/models 可以找到其它大模型）。
modelscope download "Qwen/Qwen2.5-14B-Instruct"
# modelscope download "deepseek-ai/DeepSeek-R1-Distill-Qwen-14B"
# modelscope download "Qwen2.5-32B-Instruct-AWQ"
# modelscope download "Qwen/QwQ-32B-AWQ"modelscope 参数
console
usage: modelscope <command> [<args>] download [-h] [--model MODEL | --dataset DATASET] [--repo-type {model,dataset}]
[--revision REVISION] [--cache_dir CACHE_DIR] [--local_dir LOCAL_DIR]
[--include [INCLUDE ...]] [--exclude [EXCLUDE ...]]
[--max-workers MAX_WORKERS]
[repo_id] [files ...]
positional arguments:
repo_id Optional, ID of the repo to download, It can also be set by --model or --dataset.
files Specify relative path to the repository file(s) to download.(e.g 'tokenizer.json',
'onnx/decoder_model.onnx').
options:
-h, --help show this help message and exit
--model MODEL The id of the model to be downloaded. For download, the id of either a model or dataset must
be provided.
--dataset DATASET The id of the dataset to be downloaded. For download, the id of either a model or dataset must
be provided.
--repo-type {model,dataset}
Type of repo to download from (defaults to 'model').
--revision REVISION Revision of the model.
--cache_dir CACHE_DIR
Cache directory to save model.
--local_dir LOCAL_DIR
File will be downloaded to local location specified bylocal_dir, in this case, cache_dir
parameter will be ignored.
--include [INCLUDE ...]
Glob patterns to match files to download.Ignored if file is specified
--exclude [EXCLUDE ...]
Glob patterns to exclude from files to download.Ignored if file is specified
--max-workers MAX_WORKERS
The maximum number of workers to download files.vllm 启动大模型服务
vllm 启用 modelscope 的源。
bash
echo "export VLLM_USE_MODELSCOPE=true" >> ~/.bashrc
source ~/.bashrcvllm 0.7.3 启动 modelscope 报，要改下代码
报错内容如下：
console
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/mnt/d/debian/projects/vllm/.venv/lib/python3.12/site-packages/vllm/transformers_utils/utils.py", line 32, in modelscope_list_repo_files
from modelscope.utils.hf_util import _try_login
ImportError: cannot import name '_try_login' from 'modelscope.utils.hf_util' (/mnt/d/debian/projects/vllm/.venv/lib/python3.12/site-packages/modelscope/utils/hf_util/__init__.py)是已知的 bug：Modify modelscope api usage in transformer_utils
对 .venv/lib/python3.12/site-packages/vllm/transformers_utils/utils.py 照着样子改：
diff
@@ -29,9 +29,8 @@ def modelscope_list_repo_files(
) -> List[str]:
"""List files in a modelscope repo."""
from modelscope.hub.api import HubApi
- from modelscope.utils.hf_util import _try_login
- _try_login(token)
api = HubApi()
+ api.login(token)
# same as huggingface_hub.list_repo_files
files = [
file['Path'] for file in api.get_model_files(vllm 服务参数
console
$ vllm serve -h
INFO 03-12 21:22:27 __init__.py:207] Automatically detected platform cuda.
usage: vllm serve <model_tag> [options]
positional arguments:
model_tag The model tag to serve
options:
--additional-config ADDITIONAL_CONFIG
Additional config for specified platform in JSON format. Different platforms may support
different configs. Make sure the configs are valid for the platform you are using. The input
format is like '{"config_key":"config_value"}'
--allow-credentials Allow credentials.
--allowed-headers ALLOWED_HEADERS
Allowed headers.
--allowed-local-media-path ALLOWED_LOCAL_MEDIA_PATH
Allowing API requests to read local images or videos from directories specified by the server
file system. This is a security risk. Should only be enabled in trusted environments.
--allowed-methods ALLOWED_METHODS
Allowed methods.
--allowed-origins ALLOWED_ORIGINS
Allowed origins.
--api-key API_KEY If provided, the server will require this key to be presented in the header.
--block-size {8,16,32,64,128}
Token block size for contiguous chunks of tokens. This is ignored on neuron devices and set to
``--max-model-len``. On CUDA devices, only block sizes up to 32 are supported. On HPU devices,
block size defaults to 128.
--calculate-kv-scales
This enables dynamic calculation of k_scale and v_scale when kv-cache-dtype is fp8. If
calculate-kv-scales is false, the scales will be loaded from the model checkpoint if
available. Otherwise, the scales will default to 1.0.
--chat-template CHAT_TEMPLATE
The file path to the chat template, or the template in single-line form for the specified
model.
--chat-template-content-format {auto,string,openai}
The format to render message content within a chat template. * "string" will render the
content as a string. Example: ``"Hello World"`` * "openai" will render the content as a list
of dictionaries, similar to OpenAI schema. Example: ``[{"type": "text", "text": "Hello
world!"}]``
--code-revision CODE_REVISION
The specific revision to use for the model code on Hugging Face Hub. It can be a branch name,
a tag name, or a commit id. If unspecified, will use the default version.
--collect-detailed-traces COLLECT_DETAILED_TRACES
Valid choices are model,worker,all. It makes sense to set this only if ``--otlp-traces-
endpoint`` is set. If set, it will collect detailed traces for the specified modules. This
involves use of possibly costly and or blocking operations and hence might have a performance
impact.
--compilation-config COMPILATION_CONFIG, -O COMPILATION_CONFIG
torch.compile configuration for the model.When it is a number (0, 1, 2, 3), it will be
interpreted as the optimization level. NOTE: level 0 is the default level without any
optimization. level 1 and 2 are for internal testing only. level 3 is the recommended level
for production. To specify the full compilation config, use a JSON string. Following the
convention of traditional compilers, using -O without space is also supported. -O3 is
equivalent to -O 3.
--config CONFIG Read CLI options from a config file.Must be a YAML with the following
options:https://docs.vllm.ai/en/latest/serving/openai_compatible_server.html#cli-reference
--config-format {auto,hf,mistral}
The format of the model config to load. * "auto" will try to load the config in hf format if
available else it will try to load in mistral format
--cpu-offload-gb CPU_OFFLOAD_GB
The space in GiB to offload to CPU, per GPU. Default is 0, which means no offloading.
Intuitively, this argument can be seen as a virtual way to increase the GPU memory size. For
example, if you have one 24 GB GPU and set this to 10, virtually you can think of it as a 34
GB GPU. Then you can load a 13B model with BF16 weight, which requires at least 26GB GPU
memory. Note that this requires fast CPU-GPU interconnect, as part of the model is loaded from
CPU memory to GPU memory on the fly in each model forward pass.
--device {auto,cuda,neuron,cpu,openvino,tpu,xpu,hpu}
Device type for vLLM execution.
--disable-async-output-proc
Disable async output processing. This may result in lower performance.
--disable-custom-all-reduce
See ParallelConfig.
--disable-fastapi-docs
Disable FastAPI's OpenAPI schema, Swagger UI, and ReDoc endpoint.
--disable-frontend-multiprocessing
If specified, will run the OpenAI frontend server in the same process as the model serving
engine.
--disable-log-requests
Disable logging requests.
--disable-log-stats Disable logging statistics.
--disable-logprobs-during-spec-decoding [DISABLE_LOGPROBS_DURING_SPEC_DECODING]
If set to True, token log probabilities are not returned during speculative decoding. If set
to False, log probabilities are returned according to the settings in SamplingParams. If not
specified, it defaults to True. Disabling log probabilities during speculative decoding
reduces latency by skipping logprob calculation in proposal sampling, target sampling, and
after accepted tokens are determined.
--disable-mm-preprocessor-cache
If true, then disables caching of the multi-modal preprocessor/mapper. (not recommended)
--disable-sliding-window
Disables sliding window, capping to sliding window size.
--distributed-executor-backend {ray,mp,uni,external_launcher}
Backend to use for distributed model workers, either "ray" or "mp" (multiprocessing). If the
product of pipeline_parallel_size and tensor_parallel_size is less than or equal to the number
of GPUs available, "mp" will be used to keep processing on a single host. Otherwise, this will
default to "ray" if Ray is installed and fail otherwise. Note that tpu only supports Ray for
distributed inference.
--download-dir DOWNLOAD_DIR
Directory to download and load the weights, default to the default cache dir of huggingface.
--dtype {auto,half,float16,bfloat16,float,float32}
Data type for model weights and activations. * "auto" will use FP16 precision for FP32 and
FP16 models, and BF16 precision for BF16 models. * "half" for FP16. Recommended for AWQ
quantization. * "float16" is the same as "half". * "bfloat16" for a balance between precision
and range. * "float" is shorthand for FP32 precision. * "float32" for FP32 precision.
--enable-auto-tool-choice
Enable auto tool choice for supported models. Use ``--tool-call-parser`` to specify which
parser to use.
--enable-chunked-prefill [ENABLE_CHUNKED_PREFILL]
If set, the prefill requests can be chunked based on the max_num_batched_tokens.
--enable-lora If True, enable handling of LoRA adapters.
--enable-lora-bias If True, enable bias for LoRA adapters.
--enable-prefix-caching, --no-enable-prefix-caching
Enables automatic prefix caching. Use ``--no-enable-prefix-caching`` to disable explicitly.
--enable-prompt-adapter
If True, enable handling of PromptAdapters.
--enable-prompt-tokens-details
If set to True, enable prompt_tokens_details in usage.
--enable-reasoning Whether to enable reasoning_content for the model. If enabled, the model will be able to
generate reasoning content.
--enable-request-id-headers
If specified, API server will add X-Request-Id header to responses. Caution: this hurts
performance at high QPS.
--enable-sleep-mode Enable sleep mode for the engine. (only cuda platform is supported)
--enforce-eager Always use eager-mode PyTorch. If False, will use eager mode and CUDA graph in hybrid for
maximal performance and flexibility.
--fully-sharded-loras
By default, only half of the LoRA computation is sharded with tensor parallelism. Enabling
this will use the fully sharded layers. At high sequence length, max rank or tensor parallel
size, this is likely faster.
--generation-config GENERATION_CONFIG
The folder path to the generation config. Defaults to None, no generation config is loaded,
vLLM defaults will be used. If set to 'auto', the generation config will be loaded from model
path. If set to a folder path, the generation config will be loaded from the specified folder
path. If `max_new_tokens` is specified in generation config, then it sets a server-wide limit
on the number of output tokens for all requests.
--gpu-memory-utilization GPU_MEMORY_UTILIZATION
The fraction of GPU memory to be used for the model executor, which can range from 0 to 1. For
example, a value of 0.5 would imply 50% GPU memory utilization. If unspecified, will use the
default value of 0.9. This is a per-instance limit, and only applies to the current vLLM
instance.It does not matter if you have another vLLM instance running on the same GPU. For
example, if you have two vLLM instances running on the same GPU, you can set the GPU memory
utilization to 0.5 for each instance.
--guided-decoding-backend {outlines,lm-format-enforcer,xgrammar}
Which engine will be used for guided decoding (JSON schema / regex etc) by default. Currently
support https://github.com/outlines-dev/outlines, https://github.com/mlc-ai/xgrammar, and
https://github.com/noamgat/lm-format-enforcer. Can be overridden per request via
guided_decoding_backend parameter.
--hf-overrides HF_OVERRIDES
Extra arguments for the HuggingFace config. This should be a JSON string that will be parsed
into a dictionary.
--host HOST Host name.
--ignore-patterns IGNORE_PATTERNS
The pattern(s) to ignore when loading the model.Default to `original/**/*` to avoid repeated
loading of llama's checkpoints.
--kv-cache-dtype {auto,fp8,fp8_e5m2,fp8_e4m3}
Data type for kv cache storage. If "auto", will use model data type. CUDA 11.8+ supports fp8
(=fp8_e4m3) and fp8_e5m2. ROCm (AMD GPU) supports fp8 (=fp8_e4m3)
--kv-transfer-config KV_TRANSFER_CONFIG
The configurations for distributed KV cache transfer. Should be a JSON string.
--limit-mm-per-prompt LIMIT_MM_PER_PROMPT
For each multimodal plugin, limit how many input instances to allow for each prompt. Expects a
comma-separated list of items, e.g.: `image=16,video=2` allows a maximum of 16 images and 2
videos per prompt. Defaults to 1 for each modality.
--load-format {auto,pt,safetensors,npcache,dummy,tensorizer,sharded_state,gguf,bitsandbytes,mistral,runai_streamer}
The format of the model weights to load. * "auto" will try to load the weights in the
safetensors format and fall back to the pytorch bin format if safetensors format is not
available. * "pt" will load the weights in the pytorch bin format. * "safetensors" will load
the weights in the safetensors format. * "npcache" will load the weights in pytorch format and
store a numpy cache to speed up the loading. * "dummy" will initialize the weights with random
values, which is mainly for profiling. * "tensorizer" will load the weights using tensorizer
from CoreWeave. See the Tensorize vLLM Model script in the Examples section for more
information. * "runai_streamer" will load the Safetensors weights using Run:aiModel Streamer *
"bitsandbytes" will load the weights using bitsandbytes quantization.
--logits-processor-pattern LOGITS_PROCESSOR_PATTERN
Optional regex pattern specifying valid logits processor qualified names that can be passed
with the `logits_processors` extra completion argument. Defaults to None, which allows no
processors.
--long-lora-scaling-factors LONG_LORA_SCALING_FACTORS
Specify multiple scaling factors (which can be different from base model scaling factor - see
eg. Long LoRA) to allow for multiple LoRA adapters trained with those scaling factors to be
used at the same time. If not specified, only adapters trained with the base model scaling
factor are allowed.
--long-prefill-token-threshold LONG_PREFILL_TOKEN_THRESHOLD
For chunked prefill, a request is considered long if the prompt is longer than this number of
tokens. Defaults to 4% of the model's context length.
--lora-dtype {auto,float16,bfloat16}
Data type for LoRA. If auto, will default to base model dtype.
--lora-extra-vocab-size LORA_EXTRA_VOCAB_SIZE
Maximum size of extra vocabulary that can be present in a LoRA adapter (added to the base
model vocabulary).
--lora-modules LORA_MODULES [LORA_MODULES ...]
LoRA module configurations in either 'name=path' formator JSON format. Example (old format):
``'name=path'`` Example (new format): ``{"name": "name", "path": "lora_path",
"base_model_name": "id"}``
--max-cpu-loras MAX_CPU_LORAS
Maximum number of LoRAs to store in CPU memory. Must be >= than max_loras. Defaults to
max_loras.
--max-log-len MAX_LOG_LEN
Max number of prompt characters or prompt ID numbers being printed in log. Default: Unlimited
--max-logprobs MAX_LOGPROBS
Max number of log probs to return logprobs is specified in SamplingParams.
--max-long-partial-prefills MAX_LONG_PARTIAL_PREFILLS
For chunked prefill, the maximum number of prompts longer than --long-prefill-token-threshold
that will be prefilled concurrently. Setting this less than --max-num-partial-prefills will
allow shorter prompts to jump the queue in front of longer prompts in some cases, improving
latency. Defaults to 1.
--max-lora-rank MAX_LORA_RANK
Max LoRA rank.
--max-loras MAX_LORAS
Max number of LoRAs in a single batch.
--max-model-len MAX_MODEL_LEN
Model context length. If unspecified, will be automatically derived from the model config.
--max-num-batched-tokens MAX_NUM_BATCHED_TOKENS
Maximum number of batched tokens per iteration.
--max-num-partial-prefills MAX_NUM_PARTIAL_PREFILLS
For chunked prefill, the max number of concurrent partial prefills.Defaults to 1
--max-num-seqs MAX_NUM_SEQS
Maximum number of sequences per iteration.
--max-parallel-loading-workers MAX_PARALLEL_LOADING_WORKERS
Load model sequentially in multiple batches, to avoid RAM OOM when using tensor parallel and
large models.
--max-prompt-adapter-token MAX_PROMPT_ADAPTER_TOKEN
Max number of PromptAdapters tokens
--max-prompt-adapters MAX_PROMPT_ADAPTERS
Max number of PromptAdapters in a batch.
--max-seq-len-to-capture MAX_SEQ_LEN_TO_CAPTURE
Maximum sequence length covered by CUDA graphs. When a sequence has context length larger than
this, we fall back to eager mode. Additionally for encoder-decoder models, if the sequence
length of the encoder input is larger than this, we fall back to the eager mode.
--middleware MIDDLEWARE
Additional ASGI middleware to apply to the app. We accept multiple --middleware arguments. The
value should be an import path. If a function is provided, vLLM will add it to the server
using ``@app.middleware('http')``. If a class is provided, vLLM will add it to the server
using ``app.add_middleware()``.
--mm-processor-kwargs MM_PROCESSOR_KWARGS
Overrides for the multimodal input mapping/processing, e.g., image processor. For example:
``{"num_crops": 4}``.
--model MODEL Name or path of the huggingface model to use.
--model-impl {auto,vllm,transformers}
Which implementation of the model to use. * "auto" will try to use the vLLM implementation if
it exists and fall back to the Transformers implementation if no vLLM implementation is
available. * "vllm" will use the vLLM model implementation. * "transformers" will use the
Transformers model implementation.
--model-loader-extra-config MODEL_LOADER_EXTRA_CONFIG
Extra config for model loader. This will be passed to the model loader corresponding to the
chosen load_format. This should be a JSON string that will be parsed into a dictionary.
--multi-step-stream-outputs [MULTI_STEP_STREAM_OUTPUTS]
If False, then multi-step will stream outputs at the end of all steps
--ngram-prompt-lookup-max NGRAM_PROMPT_LOOKUP_MAX
Max size of window for ngram prompt lookup in speculative decoding.
--ngram-prompt-lookup-min NGRAM_PROMPT_LOOKUP_MIN
Min size of window for ngram prompt lookup in speculative decoding.
--num-gpu-blocks-override NUM_GPU_BLOCKS_OVERRIDE
If specified, ignore GPU profiling result and use this number of GPU blocks. Used for testing
preemption.
--num-lookahead-slots NUM_LOOKAHEAD_SLOTS
Experimental scheduling config necessary for speculative decoding. This will be replaced by
speculative config in the future; it is present to enable correctness tests until then.
--num-scheduler-steps NUM_SCHEDULER_STEPS
Maximum number of forward steps per scheduler call.
--num-speculative-tokens NUM_SPECULATIVE_TOKENS
The number of speculative tokens to sample from the draft model in speculative decoding.
--otlp-traces-endpoint OTLP_TRACES_ENDPOINT
Target URL to which OpenTelemetry traces will be sent.
--override-generation-config OVERRIDE_GENERATION_CONFIG
Overrides or sets generation config in JSON format. e.g. ``{"temperature": 0.5}``. If used
with --generation-config=auto, the override parameters will be merged with the default config
from the model. If generation-config is None, only the override parameters are used.
--override-neuron-config OVERRIDE_NEURON_CONFIG
Override or set neuron device configuration. e.g. ``{"cast_logits_dtype": "bloat16"}``.
--override-pooler-config OVERRIDE_POOLER_CONFIG
Override or set the pooling method for pooling models. e.g. ``{"pooling_type": "mean",
"normalize": false}``.
--pipeline-parallel-size PIPELINE_PARALLEL_SIZE, -pp PIPELINE_PARALLEL_SIZE
Number of pipeline stages.
--port PORT Port number.
--preemption-mode PREEMPTION_MODE
If 'recompute', the engine performs preemption by recomputing; If 'swap', the engine performs
preemption by block swapping.
--prompt-adapters PROMPT_ADAPTERS [PROMPT_ADAPTERS ...]
Prompt adapter configurations in the format name=path. Multiple adapters can be specified.
--qlora-adapter-name-or-path QLORA_ADAPTER_NAME_OR_PATH
Name or path of the QLoRA adapter.
--quantization {aqlm,awq,deepspeedfp,tpu_int8,fp8,ptpc_fp8,fbgemm_fp8,modelopt,marlin,gguf,gptq_marlin_24,gptq_marlin,awq_marlin,gptq,compressed-tensors,bitsandbytes,qqq,hqq,experts_int8,neuron_quant,ipex,quark,moe_wna16,None}, -q {aqlm,awq,deepspeedfp,tpu_int8,fp8,ptpc_fp8,fbgemm_fp8,modelopt,marlin,gguf,gptq_marlin_24,gptq_marlin,awq_marlin,gptq,compressed-tensors,bitsandbytes,qqq,hqq,experts_int8,neuron_quant,ipex,quark,moe_wna16,None}
Method used to quantize the weights. If None, we first check the `quantization_config`
attribute in the model config file. If that is None, we assume the model weights are not
quantized and use `dtype` to determine the data type of the weights.
--ray-workers-use-nsight
If specified, use nsight to profile Ray workers.
--reasoning-parser {deepseek_r1}
Select the reasoning parser depending on the model that you're using. This is used to parse
the reasoning content into OpenAI API format. Required for ``--enable-reasoning``.
--response-role RESPONSE_ROLE
The role name to return if ``request.add_generation_prompt=true``.
--return-tokens-as-token-ids
When ``--max-logprobs`` is specified, represents single tokens as strings of the form
'token_id:{token_id}' so that tokens that are not JSON-encodable can be identified.
--revision REVISION The specific model version to use. It can be a branch name, a tag name, or a commit id. If
unspecified, will use the default version.
--root-path ROOT_PATH
FastAPI root_path when app is behind a path based routing proxy.
--rope-scaling ROPE_SCALING
RoPE scaling configuration in JSON format. For example,
``{"rope_type":"dynamic","factor":2.0}``
--rope-theta ROPE_THETA
RoPE theta. Use with `rope_scaling`. In some cases, changing the RoPE theta improves the
performance of the scaled model.
--scheduler-cls SCHEDULER_CLS
The scheduler class to use. "vllm.core.scheduler.Scheduler" is the default scheduler. Can be a
class directly or the path to a class of form "mod.custom_class".
--scheduler-delay-factor SCHEDULER_DELAY_FACTOR
Apply a delay (of delay factor multiplied by previous prompt latency) before scheduling next
prompt.
--scheduling-policy {fcfs,priority}
The scheduling policy to use. "fcfs" (first come first served, i.e. requests are handled in
order of arrival; default) or "priority" (requests are handled based on given priority (lower
value means earlier handling) and time of arrival deciding any ties).
--seed SEED Random seed for operations.
--served-model-name SERVED_MODEL_NAME [SERVED_MODEL_NAME ...]
The model name(s) used in the API. If multiple names are provided, the server will respond to
any of the provided names. The model name in the model field of a response will be the first
name in this list. If not specified, the model name will be the same as the ``--model``
argument. Noted that this name(s) will also be used in `model_name` tag content of prometheus
metrics, if multiple names provided, metrics tag will take the first one.
--skip-tokenizer-init
Skip initialization of tokenizer and detokenizer.
--spec-decoding-acceptance-method {rejection_sampler,typical_acceptance_sampler}
Specify the acceptance method to use during draft token verification in speculative decoding.
Two types of acceptance routines are supported: 1) RejectionSampler which does not allow
changing the acceptance rate of draft tokens, 2) TypicalAcceptanceSampler which is
configurable, allowing for a higher acceptance rate at the cost of lower quality, and vice
versa.
--speculative-disable-by-batch-size SPECULATIVE_DISABLE_BY_BATCH_SIZE
Disable speculative decoding for new incoming requests if the number of enqueue requests is
larger than this value.
--speculative-disable-mqa-scorer
If set to True, the MQA scorer will be disabled in speculative and fall back to batch
expansion
--speculative-draft-tensor-parallel-size SPECULATIVE_DRAFT_TENSOR_PARALLEL_SIZE, -spec-draft-tp SPECULATIVE_DRAFT_TENSOR_PARALLEL_SIZE
Number of tensor parallel replicas for the draft model in speculative decoding.
--speculative-max-model-len SPECULATIVE_MAX_MODEL_LEN
The maximum sequence length supported by the draft model. Sequences over this length will skip
speculation.
--speculative-model SPECULATIVE_MODEL
The name of the draft model to be used in speculative decoding.
--speculative-model-quantization {aqlm,awq,deepspeedfp,tpu_int8,fp8,ptpc_fp8,fbgemm_fp8,modelopt,marlin,gguf,gptq_marlin_24,gptq_marlin,awq_marlin,gptq,compressed-tensors,bitsandbytes,qqq,hqq,experts_int8,neuron_quant,ipex,quark,moe_wna16,None}
Method used to quantize the weights of speculative model. If None, we first check the
`quantization_config` attribute in the model config file. If that is None, we assume the model
weights are not quantized and use `dtype` to determine the data type of the weights.
--ssl-ca-certs SSL_CA_CERTS
The CA certificates file.
--ssl-cert-reqs SSL_CERT_REQS
Whether client certificate is required (see stdlib ssl module's).
--ssl-certfile SSL_CERTFILE
The file path to the SSL cert file.
--ssl-keyfile SSL_KEYFILE
The file path to the SSL key file.
--swap-space SWAP_SPACE
CPU swap space size (GiB) per GPU.
--task {auto,generate,embedding,embed,classify,score,reward,transcription}
The task to use the model for. Each vLLM instance only supports one task, even if the same
model can be used for multiple tasks. When the model only supports one task, ``"auto"`` can be
used to select it; otherwise, you must specify explicitly which task to use.
--tensor-parallel-size TENSOR_PARALLEL_SIZE, -tp TENSOR_PARALLEL_SIZE
Number of tensor parallel replicas.
--tokenizer TOKENIZER
Name or path of the huggingface tokenizer to use. If unspecified, model name or path will be
used.
--tokenizer-mode {auto,slow,mistral,custom}
The tokenizer mode. * "auto" will use the fast tokenizer if available. * "slow" will always
use the slow tokenizer. * "mistral" will always use the `mistral_common` tokenizer. * "custom"
will use --tokenizer to select the preregistered tokenizer.
--tokenizer-pool-extra-config TOKENIZER_POOL_EXTRA_CONFIG
Extra config for tokenizer pool. This should be a JSON string that will be parsed into a
dictionary. Ignored if tokenizer_pool_size is 0.
--tokenizer-pool-size TOKENIZER_POOL_SIZE
Size of tokenizer pool to use for asynchronous tokenization. If 0, will use synchronous
tokenization.
--tokenizer-pool-type TOKENIZER_POOL_TYPE
Type of tokenizer pool to use for asynchronous tokenization. Ignored if tokenizer_pool_size is
0.
--tokenizer-revision TOKENIZER_REVISION
Revision of the huggingface tokenizer to use. It can be a branch name, a tag name, or a commit
id. If unspecified, will use the default version.
--tool-call-parser {granite-20b-fc,granite,hermes,internlm,jamba,llama3_json,mistral,pythonic} or name registered in --tool-parser-plugin
Select the tool call parser depending on the model that you're using. This is used to parse
the model-generated tool call into OpenAI API format. Required for ``--enable-auto-tool-
choice``.
--tool-parser-plugin TOOL_PARSER_PLUGIN
Special the tool parser plugin write to parse the model-generated tool into OpenAI API format,
the name register in this plugin can be used in ``--tool-call-parser``.
--trust-remote-code Trust remote code from huggingface.
--typical-acceptance-sampler-posterior-alpha TYPICAL_ACCEPTANCE_SAMPLER_POSTERIOR_ALPHA
A scaling factor for the entropy-based threshold for token acceptance in the
TypicalAcceptanceSampler. Typically defaults to sqrt of --typical-acceptance-sampler-
posterior-threshold i.e. 0.3
--typical-acceptance-sampler-posterior-threshold TYPICAL_ACCEPTANCE_SAMPLER_POSTERIOR_THRESHOLD
Set the lower bound threshold for the posterior probability of a token to be accepted. This
threshold is used by the TypicalAcceptanceSampler to make sampling decisions during
speculative decoding. Defaults to 0.09
--use-v2-block-manager
[DEPRECATED] block manager v1 has been removed and SelfAttnBlockSpaceManager (i.e. block
manager v2) is now the default. Setting this flag to True or False has no effect on vLLM
behavior.
--uvicorn-log-level {debug,info,warning,error,critical,trace}
Log level for uvicorn.
--worker-cls WORKER_CLS
The worker class to use for distributed execution.
-h, --help show this help message and exit启动 Qwen2.5-14B
bash
# 没有激活 source .venv/bin/activate 就加上 uv run 。前缀
vllm serve Qwen/Qwen2.5-14B-Instruct \
--served-model-name "Qwen2.5-14B-Instruct" \
--tensor-parallel-size 2 \
--max-model-len 16384 \
--enforce-eager启动 DeepSeek:14B
bash
uv run vllm serve deepseek-ai/DeepSeek-R1-Distill-Qwen-14B \
--served-model-name DeepSeek-R1-Distill-Qwen-14B \
--tensor-parallel-size 2 \
--max-model-len 32768 \
--enforce-eager启动 Qwen2.5-32B-Instruct-AWQ
bash
uv run vllm serve Qwen/Qwen2.5-32B-Instruct-AWQ \
--served-model-name "Qwen2.5-32B-Instruct-AWQ" \
--quantization "awq" --dtype "half" \
--tensor-parallel-size 2 \
--max-model-len 16384 \
--enforce-eager启动 QwQ-32B-AWQ
bash
uv run vllm serve Qwen/QwQ-32B-AWQ \
--served-model-name "QwQ-32B-AWQ" \
--quantization "awq" --dtype "half" \
--tensor-parallel-size 2 \
--max-model-len 16384 \
--enforce-eager与大模型对话
curl 发请求
bash
curl http://localhost:8000/v1/completions \
-H "Content-Type: application/json" \
-d '{
"model": "Qwen2.5-14B-Instruct",
"prompt": "你是谁?"
}'chatbox 发起对话
chatbox 先设置 vllm 本地服务
输入模型名（启动 vllm 时用 --served-model-name 参数指定的） 
新增对话后，就可以输入内容了 