常规语言模型（Qwen2.5-72B-Instruct）

• 启动脚本

• crul 测试

推理模型（R1-Distill-Qwen-32B）

• crul 测试

多模态模型（Qwen2.5-VL-72B-Instruct）

• crul 测试

embedding 模型（bce-embedding-base）

• crul 测试

rerank 模型（bce-reranker-base）

• crul 测试

• -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. (default: ) --code-revision CODE_REVISION The specific revision to use for the model code on the Hugging Face Hub. It can be a branch name, a tag name, or a commit id. If unspecified, will use the default version. (default: None) --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. - "hf" will load the config in hf format. - "mistral" will load the config in mistral format. (default: auto) --disable-async-output-proc Disable async output processing. This may result in lower performance. (default: False) --disable-cascade-attn, --no-disable-cascade-attn Disable cascade attention for V1. While cascade attention does not change the mathematical correctness, disabling it could be useful for preventing potential numerical issues. Note that even if this is set to False, cascade attention will be only used when the heuristic tells that it's beneficial. (default: False) --disable-sliding-window, --no-disable-sliding-window Whether to disable sliding window. If True, we will disable the sliding window functionality of the model, capping to sliding window size. If the model does not support sliding window, this argument is ignored. (default: False) --dtype {auto,bfloat16,float,float16,float32,half} 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. (default: auto) --enable-prompt-embeds, --no-enable-prompt-embeds If True, enables passing text embeddings as inputs via the prompt_embeds key. Note that enabling this will double the time required for graph compilation. (default: False) --enable-sleep-mode, --no-enable-sleep-mode Enable sleep mode for the engine (only cuda platform is supported). (default: False) --enforce-eager, --no-enforce-eager Whether to always use eager-mode PyTorch. If True, we will disable CUDA graph and always execute the model in eager mode. If False, we will use CUDA graph and eager execution in hybrid for maximal performance and flexibility. (default: False) --generation-config GENERATION_CONFIG The folder path to the generation config. Defaults to "auto", the generation config will be loaded from model path. If set to "vllm", no generation config is loaded, vLLM defaults will be used. 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. (default: auto) --hf-config-path HF_CONFIG_PATH Name or path of the Hugging Face config to use. If unspecified, model name or path will be used. (default: None) --hf-overrides HF_OVERRIDES If a dictionary, contains arguments to be forwarded to the Hugging Face config. If a callable, it is called to update the HuggingFace config. (default: {}) --hf-token [HF_TOKEN] The token to use as HTTP bearer authorization for remote files . If True, will use the token generated when running huggingface-cli login (stored in ~/.huggingface). (default: None) --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. (default: None) --max-logprobs MAX_LOGPROBS Maximum number of log probabilities to return when logprobs is specified in SamplingParams. The default value comes the default for the OpenAI Chat Completions API. (default: 20) --max-model-len MAX_MODEL_LEN Model context length (prompt and output). If unspecified, will be automatically derived from the model config. When passing via -max-model-len, supports k/m/g/K/M/G in human-readable format. Examples: - 1k -> 1000 - 1K -> 1024 - 25.6k -> 25,600 (default: None) --max-seq-len-to-capture MAX_SEQ_LEN_TO_CAPTURE Maximum sequence len 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. (default: 8192) --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. (default: auto) --override-generation-config OVERRIDE_GENERATION_CONFIG Overrides or sets generation config. 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 used with -generation-config vllm, only the override parameters are used. Should either be a valid JSON string or JSON keys passed individually. For example, the following sets of arguments are equivalent: - -json-arg '{"key1": "value1", "key2": {"key3": "value2"}}' - -json-arg.key1 value1 --json-arg.key2.key3 value2 (default: {}) --override-neuron-config OVERRIDE_NEURON_CONFIG Initialize non-default neuron config or override default neuron config that are specific to Neuron devices, this argument will be used to configure the neuron config that can not be gathered from the vllm arguments. e.g. {"cast_logits_dtype": "bloat16"}. Should either be a valid JSON string or JSON keys passed individually. For example, the following sets of arguments are equivalent: - -json-arg '{"key1": "value1", "key2": {"key3": "value2"}}' - -json-arg.key1 value1 --json-arg.key2.key3 value2 (default: {}) --override-pooler-config OVERRIDE_POOLER_CONFIG Initialize non-default pooling config or override default pooling config for the pooling model. e.g. {"pooling_type": "mean", "normalize": false}. (default: None) --quantization {aqlm,auto-round,awq,awq_marlin,bitblas,bitsandbytes,compressed-tensors,deepspeedfp,experts_int8,fbgemm_fp8,fp8,gguf,gptq,gptq_bitblas,gptq_marlin,gptq_marlin_24,hqq,ipex,marlin,modelopt,modelopt_fp4,moe_wna16,neuron_quant,ptpc_fp8,qqq,quark,torchao,tpu_int8,None}, -q {aqlm,auto-round,awq,awq_marlin,bitblas,bitsandbytes,compressed-tensors,deepspeedfp,experts_int8,fbgemm_fp8,fp8,gguf,gptq,gptq_bitblas,gptq_marlin,gptq_marlin_24,hqq,ipex,marlin,modelopt,modelopt_fp4,moe_wna16,neuron_quant,ptpc_fp8,qqq,quark,torchao,tpu_int8,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. (default: None) --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. (default: None) --rope-scaling ROPE_SCALING RoPE scaling configuration. For example, {"rope_type":"dynamic","factor":2.0}. Should either be a valid JSON string or JSON keys passed individually. For example, the following sets of arguments are equivalent: - -json-arg '{"key1": "value1", "key2": {"key3": "value2"}}' - -json-arg.key1 value1 --json-arg.key2.key3 value2 (default: {}) --rope-theta ROPE_THETA RoPE theta. Use with rope_scaling. In some cases, changing the RoPE theta improves the performance of the scaled model. (default: None) --seed SEED Random seed for reproducibility. Initialized to None in V0, but initialized to 0 in V1. (default: None) --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. (default: None) --skip-tokenizer-init, --no-skip-tokenizer-init Skip initialization of tokenizer and detokenizer. Expects valid prompt_token_ids and None for prompt from the input. The generated output will contain token ids. (default: False) --task {auto,classify,draft,embed,embedding,generate,reward,score,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. (default: auto) --tokenizer TOKENIZER Name or path of the Hugging Face tokenizer to use. If unspecified, model name or path will be used. (default: None) --tokenizer-mode {auto,custom,mistral,slow} Tokenizer mode: - "auto" will use the fast tokenizer if available. - "slow" will always use the slow tokenizer. - "mistral" will always use the tokenizer from mistral_common. - "custom" will use --tokenizer to select the preregistered tokenizer. (default: auto) --tokenizer-revision TOKENIZER_REVISION The specific revision to use for the tokenizer on the Hugging Face Hub. It can be a branch name, a tag name, or a commit id. If unspecified, will use the default version. (default: None) --trust-remote-code, --no-trust-remote-code Trust remote code (e.g., from HuggingFace) when downloading the model and tokenizer. (default: False)

• -download-dir DOWNLOAD_DIR Directory to download and load the weights, default to the default cache directory of Hugging Face. (default: None) --ignore-patterns IGNORE_PATTERNS [IGNORE_PATTERNS ...] The list of patterns to ignore when loading the model. Default to "original/**/*" to avoid repeated loading of llama's checkpoints. (default: None) --load-format {auto,pt,safetensors,npcache,dummy,tensorizer,sharded_state,gguf,bitsandbytes,mistral,runai_streamer,runai_streamer_sharded,fastsafetensors} 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 use CoreWeave's tensorizer library for fast weight loading. See the Tensorize vLLM Model script in the Examples section for more information. - "runai_streamer" will load the Safetensors weights using Run:ai Model Streamer. - "bitsandbytes" will load the weights using bitsandbytes quantization. - "sharded_state" will load weights from pre-sharded checkpoint files, supporting efficient loading of tensor-parallel models. - "gguf" will load weights from GGUF format files (details specified in https://github.com/ggml- org/ggml/blob/master/docs/gguf.md). - "mistral" will load weights from consolidated safetensors files used by Mistral models. (default: auto) --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. Should either be a valid JSON string or JSON keys passed individually. For example, the following sets of arguments are equivalent: - -json-arg '{"key1": "value1", "key2": {"key3": "value2"}}' - -json-arg.key1 value1 --json-arg.key2.key3 value2 (default: {}) --pt-load-map-location PT_LOAD_MAP_LOCATION pt_load_map_location: the map location for loading pytorch checkpoint, to support loading checkpoints can only be loaded on certain devices like "cuda", this is equivalent to {"": "cuda"}. Another supported format is mapping from different devices like from GPU 1 to GPU 0: {"cuda:1": "cuda:0"}. Note that when passed from command line, the strings in dictionary needs to be double quoted for json parsing. For more details, see original doc for map_location in https://pytorch.org/docs/stable/generated/torch.load.html (default: cpu) --qlora-adapter-name-or-path QLORA_ADAPTER_NAME_OR_PATH The -qlora-adapter-name-or-path has no effect, do not set it, and it will be removed in v0.10.0. (default: None) --use-tqdm-on-load, --no-use-tqdm-on-load Whether to enable tqdm for showing progress bar when loading model weights. (default: True)

• -enable-reasoning, --no-enable-reasoning [DEPRECATED] The -enable-reasoning flag is deprecated as of v0.9.0. Use -reasoning-parser to specify the reasoning parser backend instead. This flag (-enable-reasoning) will be removed in v0.10.0. When -reasoning-parser is specified, reasoning mode is automatically enabled. (default: None) --guided-decoding-backend {auto,guidance,lm-format-enforcer,outlines,xgrammar} Which engine will be used for guided decoding (JSON schema / regex etc) by default. With "auto", we will make opinionated choices based on request contents and what the backend libraries currently support, so the behavior is subject to change in each release. (default: auto) --guided-decoding-disable-additional-properties, --no-guided-decoding-disable-additional-properties If True, the guidance backend will not use additionalProperties in the JSON schema. This is only supported for the guidance backend and is used to better align its behaviour with outlines and xgrammar. (default: False) --guided-decoding-disable-any-whitespace, --no-guided-decoding-disable-any-whitespace If True, the model will not generate any whitespace during guided decoding. This is only supported for xgrammar and guidance backends. (default: False) --guided-decoding-disable-fallback, --no-guided-decoding-disable-fallback If True, vLLM will not fallback to a different backend on error. (default: False) --reasoning-parser {deepseek_r1,granite,qwen3} Select the reasoning parser depending on the model that you're using. This is used to parse the reasoning content into OpenAI API format. (default: )

• -data-parallel-address DATA_PARALLEL_ADDRESS, -dpa DATA_PARALLEL_ADDRESS Address of data parallel cluster head-node. (default: None) --data-parallel-rpc-port DATA_PARALLEL_RPC_PORT, -dpp DATA_PARALLEL_RPC_PORT Port for data parallel RPC communication. (default: None) --data-parallel-size DATA_PARALLEL_SIZE, -dp DATA_PARALLEL_SIZE Number of data parallel groups. MoE layers will be sharded according to the product of the tensor parallel size and data parallel size. (default: 1) --data-parallel-size-local DATA_PARALLEL_SIZE_LOCAL, -dpl DATA_PARALLEL_SIZE_LOCAL Number of data parallel replicas to run on this node. (default: None) --disable-custom-all-reduce, --no-disable-custom-all-reduce Disable the custom all-reduce kernel and fall back to NCCL. (default: False) --distributed-executor-backend {external_launcher,mp,ray,uni,None} 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 and hpu only support Ray for distributed inference. (default: None) --enable-expert-parallel, --no-enable-expert-parallel Use expert parallelism instead of tensor parallelism for MoE layers. (default: False) --max-parallel-loading-workers MAX_PARALLEL_LOADING_WORKERS Maximum number of parallel loading workers when loading model sequentially in multiple batches. To avoid RAM OOM when using tensor parallel and large models. (default: None) --pipeline-parallel-size PIPELINE_PARALLEL_SIZE, -pp PIPELINE_PARALLEL_SIZE Number of pipeline parallel groups. (default: 1) --ray-workers-use-nsight, --no-ray-workers-use-nsight Whether to profile Ray workers with nsight, see https://docs.ray.io/en/latest/ray- observability/user-guides/profiling.html#profiling-nsight-profiler. (default: False) --tensor-parallel-size TENSOR_PARALLEL_SIZE, -tp TENSOR_PARALLEL_SIZE Number of tensor parallel groups. (default: 1) --worker-cls WORKER_CLS The full name of the worker class to use. If "auto", the worker class will be determined based on the platform. (default: auto) --worker-extension-cls WORKER_EXTENSION_CLS The full name of the worker extension class to use. The worker extension class is dynamically inherited by the worker class. This is used to inject new attributes and methods to the worker class for use in collective_rpc calls. (default: )

• -block-size {1,8,16,32,64,128} Size of a contiguous cache block in number 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. This config has no static default. If left unspecified by the user, it will be set in Platform.check_and_update_configs() based on the current platform. (default: None) --calculate-kv-scales, --no-calculate-kv-scales This enables dynamic calculation of k_scale and v_scale when kv_cache_dtype is fp8. If False, the scales will be loaded from the model checkpoint if available. Otherwise, the scales will default to 1.0. (default: False) --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. (default: 0) --enable-prefix-caching, --no-enable-prefix-caching Whether to enable prefix caching. Disabled by default for V0. Enabled by default for V1. (default: None) --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. (default: 0.9) --kv-cache-dtype {auto,fp8,fp8_e4m3,fp8_e5m2} 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). (default: auto) --num-gpu-blocks-override NUM_GPU_BLOCKS_OVERRIDE Number of GPU blocks to use. This overrides the profiled num_gpu_blocks if specified. Does nothing if None. Used for testing preemption. (default: None) --prefix-caching-hash-algo {builtin,sha256} Set the hash algorithm for prefix caching: - "builtin" is Python's built-in hash. - "sha256" is collision resistant but with certain overheads. (default: builtin) --swap-space SWAP_SPACE Size of the CPU swap space per GPU (in GiB). (default: 4)

• -tokenizer-pool-extra-config TOKENIZER_POOL_EXTRA_CONFIG This parameter is deprecated and will be removed in a future release. Passing this parameter will have no effect. Please remove it from your configurations. Should either be a valid JSON string or JSON keys passed individually. For example, the following sets of arguments are equivalent: - -json-arg '{"key1": "value1", "key2": {"key3": "value2"}}' - -json-arg.key1 value1 --json-arg.key2.key3 value2 (default: {}) --tokenizer-pool-size TOKENIZER_POOL_SIZE This parameter is deprecated and will be removed in a future release. Passing this parameter will have no effect. Please remove it from your configurations. (default: 0) --tokenizer-pool-type TOKENIZER_POOL_TYPE This parameter is deprecated and will be removed in a future release. Passing this parameter will have no effect. Please remove it from your configurations. (default: ray)

• -disable-mm-preprocessor-cache, --no-disable-mm-preprocessor-cache If True, disable caching of the processed multi-modal inputs. (default: False) --limit-mm-per-prompt LIMIT_MM_PER_PROMPT The maximum number of input items allowed per prompt for each modality. Defaults to 1 (V0) or 999 (V1) for each modality. For example, to allow up to 16 images and 2 videos per prompt: {"images": 16, "videos": 2} Should either be a valid JSON string or JSON keys passed individually. For example, the following sets of arguments are equivalent: - -json-arg '{"key1": "value1", "key2": {"key3": "value2"}}' - -json-arg.key1 value1 --json-arg.key2.key3 value2 (default: {}) --mm-processor-kwargs MM_PROCESSOR_KWARGS Overrides for the multi-modal processor obtained from transformers.AutoProcessor.from_pretrained. The available overrides depend on the model that is being run. For example, for Phi-3-Vision: {"num_crops": 4}. Should either be a valid JSON string or JSON keys passed individually. For example, the following sets of arguments are equivalent: - -json-arg '{"key1": "value1", "key2": {"key3": "value2"}}' - -json-arg.key1 value1 --json-arg.key2.key3 value2 (default: None)

• -enable-lora, --no-enable-lora If True, enable handling of LoRA adapters. (default: None) --enable-lora-bias, --no-enable-lora-bias Enable bias for LoRA adapters. (default: False) --fully-sharded-loras, --no-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. (default: False) --long-lora-scaling-factors 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. (default: None) --lora-dtype {auto,bfloat16,float16} Data type for LoRA. If auto, will default to base model dtype. (default: auto) --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). (default: 256) --max-cpu-loras MAX_CPU_LORAS Maximum number of LoRAs to store in CPU memory. Must be >= than max_loras. (default: None) --max-lora-rank MAX_LORA_RANK Max LoRA rank. (default: 16) --max-loras MAX_LORAS Max number of LoRAs in a single batch. (default: 1)

• -enable-prompt-adapter, --no-enable-prompt-adapter If True, enable handling of PromptAdapters. (default: None) --max-prompt-adapter-token MAX_PROMPT_ADAPTER_TOKEN Max number of PromptAdapters tokens. (default: 0) --max-prompt-adapters MAX_PROMPT_ADAPTERS Max number of PromptAdapters in a batch. (default: 1)

• -device {auto,cpu,cuda,hpu,neuron,tpu,xpu} Device type for vLLM execution. This parameter is deprecated and will be removed in a future release. It will now be set automatically based on the current platform. (default: auto)

• -speculative-config SPECULATIVE_CONFIG The configurations for speculative decoding. Should be a JSON string. (default: None)

• -collect-detailed-traces {all,model,worker,None} [{all,model,worker,None} ...] 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. Note that collecting detailed timing information for each request can be expensive. (default: None) --otlp-traces-endpoint OTLP_TRACES_ENDPOINT Target URL to which OpenTelemetry traces will be sent. (default: None) --show-hidden-metrics-for-version SHOW_HIDDEN_METRICS_FOR_VERSION Enable deprecated Prometheus metrics that have been hidden since the specified version. For example, if a previously deprecated metric has been hidden since the v0.7.0 release, you use -show-hidden-metrics-for-version=0.7 as a temporary escape hatch while you migrate to new metrics. The metric is likely to be removed completely in an upcoming release. (default: None)

• -cuda-graph-sizes CUDA_GRAPH_SIZES [CUDA_GRAPH_SIZES ...] Cuda graph capture sizes, default is 512. 1. if one value is provided, then the capture list would follow the pattern: [1, 2, 4] + [i for i in range(8, cuda_graph_sizes + 1, 8)] 2. more than one value (e.g. 1 2 128) is provided, then the capture list will follow the provided list. (default: [512]) --disable-chunked-mm-input, --no-disable-chunked-mm-input If set to true and chunked prefill is enabled, we do not want to partially schedule a multimodal item. Only used in V1 This ensures that if a request has a mixed prompt (like text tokens TTTT followed by image tokens IIIIIIIIII) where only some image tokens can be scheduled (like TTTTIIIII, leaving IIIII), it will be scheduled as TTTT in one step and IIIIIIIIII in the next. (default: False) --enable-chunked-prefill, --no-enable-chunked-prefill If True, prefill requests can be chunked based on the remaining max_num_batched_tokens. (default: None) --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. (default: 0) --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. (default: 1) --max-num-batched-tokens MAX_NUM_BATCHED_TOKENS Maximum number of tokens to be processed in a single iteration. This config has no static default. If left unspecified by the user, it will be set in EngineArgs.create_engine_config based on the usage context. (default: None) --max-num-partial-prefills MAX_NUM_PARTIAL_PREFILLS For chunked prefill, the maximum number of sequences that can be partially prefilled concurrently. (default: 1) --max-num-seqs MAX_NUM_SEQS Maximum number of sequences to be processed in a single iteration. This config has no static default. If left unspecified by the user, it will be set in EngineArgs.create_engine_config based on the usage context. (default: None) --multi-step-stream-outputs, --no-multi-step-stream-outputs If False, then multi-step will stream outputs at the end of all steps (default: True) --num-lookahead-slots NUM_LOOKAHEAD_SLOTS The number of slots to allocate per sequence per step, beyond the known token ids. This is used in speculative decoding to store KV activations of tokens which may or may not be accepted. NOTE: This will be replaced by speculative config in the future; it is present to enable correctness tests until then. (default: 0) --num-scheduler-steps NUM_SCHEDULER_STEPS Maximum number of forward steps per scheduler call. (default: 1) --preemption-mode {recompute,swap,None} Whether to perform preemption by swapping or recomputation. If not specified, we determine the mode as follows: We use recomputation by default since it incurs lower overhead than swapping. However, when the sequence group has multiple sequences (e.g., beam search), recomputation is not currently supported. In such a case, we use swapping instead. (default: None) --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". (default: vllm.core.scheduler.Scheduler) --scheduler-delay-factor SCHEDULER_DELAY_FACTOR Apply a delay (of delay factor multiplied by previous prompt latency) before scheduling next prompt. (default: 0.0) --scheduling-policy {fcfs,priority} The scheduling policy to use: - "fcfs" means first come first served, i.e. requests are handled in order of arrival. - "priority" means requests are handled based on given priority (lower value means earlier handling) and time of arrival deciding any ties). (default: fcfs)

• -additional-config ADDITIONAL_CONFIG Additional config for specified platform. Different platforms may support different configs. Make sure the configs are valid for the platform you are using. Contents must be hashable. (default: {}) --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. Following the convention of traditional compilers, using O without space is also supported. O3 is equivalent to O 3. You can specify the full compilation config like so: {"level": 3, "cudagraph_capture_sizes": [1, 2, 4, 8]} Should either be a valid JSON string or JSON keys passed individually. For example, the following sets of arguments are equivalent: - -json-arg '{"key1": "value1", "key2": {"key3": "value2"}}' - -json-arg.key1 value1 --json-arg.key2.key3 value2 (default: {"inductor_compile_config": {"enable_auto_functionalized_v2": false}}) --kv-events-config KV_EVENTS_CONFIG The configurations for event publishing. Should either be a valid JSON string or JSON keys passed individually. For example, the following sets of arguments are equivalent: - -json-arg '{"key1": "value1", "key2": {"key3": "value2"}}' - -json-arg.key1 value1 --json-arg.key2.key3 value2 (default: None) --kv-transfer-config KV_TRANSFER_CONFIG The configurations for distributed KV cache transfer. Should either be a valid JSON string or JSON keys passed individually. For example, the following sets of arguments are equivalent: - -json-arg '{"key1": "value1", "key2": {"key3": "value2"}}' - -json-arg.key1 value1 --json-arg.key2.key3 value2 (default: None)

• To view a argument group: --help=ModelConfig

• To view a single argument: --help=max-num-seqs

• To search by keyword: --help=max

• To list all groups: --help=listgroup