Supported Models

vLLM supports generative and pooling models across various tasks. If a model supports more than one task, you can set the task via the --task argument.

For each task, we list the model architectures that have been implemented in vLLM. Alongside each architecture, we include some popular models that use it.

Model Implementation

vLLM

If vLLM natively supports a model, its implementation can be found in vllm/model_executor/models.

These models are what we list in supported-text-models and supported-mm-models.

Transformers

vLLM also supports model implementations that are available in Transformers. This does not currently work for all models, but most decoder language models are supported, and vision language model support is planned!

To check if the modeling backend is Transformers, you can simply do this:

from vllm import LLM
llm = LLM(model=..., task="generate")  # Name or path of your model
llm.apply_model(lambda model: print(type(model)))

If it is TransformersForCausalLM then it means it's based on Transformers!

Tip

You can force the use of TransformersForCausalLM by setting model_impl="transformers" for offline-inference or --model-impl transformers for the openai-compatible-server.

Note

vLLM may not fully optimise the Transformers implementation so you may see degraded performance if comparing a native model to a Transformers model in vLLM.

Custom models

If a model is neither supported natively by vLLM or Transformers, it can still be used in vLLM!

For a model to be compatible with the Transformers backend for vLLM it must:

• be a Transformers compatible custom model (see Transformers - Customizing models):
  • The model directory must have the correct structure (e.g. config.json is present).
  • config.json must contain auto_map.AutoModel.
• be a Transformers backend for vLLM compatible model (see writing-custom-models):
  • Customisation should be done in the base model (e.g. in MyModel, not MyModelForCausalLM).

If the compatible model is:

• on the Hugging Face Model Hub, simply set trust_remote_code=True for offline-inference or --trust-remote-code for the openai-compatible-server.
• in a local directory, simply pass directory path to model=<MODEL_DIR> for offline-inference or vllm serve <MODEL_DIR> for the openai-compatible-server.

This means that, with the Transformers backend for vLLM, new models can be used before they are officially supported in Transformers or vLLM!

Writing custom models

This section details the necessary modifications to make to a Transformers compatible custom model that make it compatible with the Transformers backend for vLLM. (We assume that a Transformers compatible custom model has already been created, see Transformers - Customizing models).

To make your model compatible with the Transformers backend, it needs:

1. kwargs passed down through all modules from MyModel to MyAttention.
2. MyAttention must use ALL_ATTENTION_FUNCTIONS to call attention.
3. MyModel must contain _supports_attention_backend = True.

modeling_my_model.py

from transformers import PreTrainedModel
from torch import nn

class MyAttention(nn.Module):

    def forward(self, hidden_states, **kwargs):
        ...
        attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
        attn_output, attn_weights = attention_interface(
            self,
            query_states,
            key_states,
            value_states,
            **kwargs,
        )
        ...

class MyModel(PreTrainedModel):
    _supports_attention_backend = True

Here is what happens in the background when this model is loaded:

1. The config is loaded.
2. MyModel Python class is loaded from the auto_map in config, and we check that the model is_backend_compatible().
3. MyModel is loaded into TransformersForCausalLM (see vllm/model_executor/models/transformers.py) which sets self.config._attn_implementation = "vllm" so that vLLM's attention layer is used.

That's it!

For your model to be compatible with vLLM's tensor parallel and/or pipeline parallel features, you must add base_model_tp_plan and/or base_model_pp_plan to your model's config class:

configuration_my_model.py

from transformers import PretrainedConfig

class MyConfig(PretrainedConfig):
    base_model_tp_plan = {
        "layers.*.self_attn.k_proj": "colwise",
        "layers.*.self_attn.v_proj": "colwise",
        "layers.*.self_attn.o_proj": "rowwise",
        "layers.*.mlp.gate_proj": "colwise",
        "layers.*.mlp.up_proj": "colwise",
        "layers.*.mlp.down_proj": "rowwise",
    }
    base_model_pp_plan = {
        "embed_tokens": (["input_ids"], ["inputs_embeds"]),
        "layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
        "norm": (["hidden_states"], ["hidden_states"]),
    }

• base_model_tp_plan is a dict that maps fully qualified layer name patterns to tensor parallel styles (currently only "colwise" and "rowwise" are supported).
• base_model_pp_plan is a dict that maps direct child layer names to tuples of lists of strs:
  • You only need to do this for layers which are not present on all pipeline stages
  • vLLM assumes that there will be only one nn.ModuleList, which is distributed across the pipeline stages
  • The list in the first element of the tuple contains the names of the input arguments
  • The list in the last element of the tuple contains the names of the variables the layer outputs to in your modeling code

Loading a Model

Hugging Face Hub

By default, vLLM loads models from Hugging Face (HF) Hub. To change the download path for models, you can set the HF_HOME environment variable; for more details, refer to their official documentation.

To determine whether a given model is natively supported, you can check the config.json file inside the HF repository. If the "architectures" field contains a model architecture listed below, then it should be natively supported.

Models do not need to be natively supported to be used in vLLM. The Transformers backend enables you to run models directly using their Transformers implementation (or even remote code on the Hugging Face Model Hub!).

Tip

The easiest way to check if your model is really supported at runtime is to run the program below:

from vllm import LLM

# For generative models (task=generate) only
llm = LLM(model=..., task="generate")  # Name or path of your model
output = llm.generate("Hello, my name is")
print(output)

# For pooling models (task={embed,classify,reward,score}) only
llm = LLM(model=..., task="embed")  # Name or path of your model
output = llm.encode("Hello, my name is")
print(output)

If vLLM successfully returns text (for generative models) or hidden states (for pooling models), it indicates that your model is supported.

Otherwise, please refer to Adding a New Model for instructions on how to implement your model in vLLM. Alternatively, you can open an issue on GitHub to request vLLM support.

Download a model

If you prefer, you can use the Hugging Face CLI to download a model or specific files from a model repository:

# Download a model
huggingface-cli download HuggingFaceH4/zephyr-7b-beta

# Specify a custom cache directory
huggingface-cli download HuggingFaceH4/zephyr-7b-beta --cache-dir ./path/to/cache

# Download a specific file from a model repo
huggingface-cli download HuggingFaceH4/zephyr-7b-beta eval_results.json

List the downloaded models

Use the Hugging Face CLI to manage models stored in local cache:

# List cached models
huggingface-cli scan-cache

# Show detailed (verbose) output
huggingface-cli scan-cache -v

# Specify a custom cache directory
huggingface-cli scan-cache --dir ~/.cache/huggingface/hub

Delete a cached model

Use the Hugging Face CLI to interactively delete downloaded model from the cache:

Commands

# The `delete-cache` command requires extra dependencies to work with the TUI.
# Please run `pip install huggingface_hub[cli]` to install them.

# Launch the interactive TUI to select models to delete
$ huggingface-cli delete-cache
? Select revisions to delete: 1 revisions selected counting for 438.9M.
  ○ None of the following (if selected, nothing will be deleted).
Model BAAI/bge-base-en-v1.5 (438.9M, used 1 week ago)
❯ ◉ a5beb1e3: main # modified 1 week ago

Model BAAI/bge-large-en-v1.5 (1.3G, used 1 week ago)
  ○ d4aa6901: main # modified 1 week ago

Model BAAI/bge-reranker-base (1.1G, used 4 weeks ago)
  ○ 2cfc18c9: main # modified 4 weeks ago

Press <space> to select, <enter> to validate and <ctrl+c> to quit without modification.

# Need to confirm after selected
? Select revisions to delete: 1 revision(s) selected.
? 1 revisions selected counting for 438.9M. Confirm deletion ? Yes
Start deletion.
Done. Deleted 1 repo(s) and 0 revision(s) for a total of 438.9M.

Using a proxy

Here are some tips for loading/downloading models from Hugging Face using a proxy:

• Set the proxy globally for your session (or set it in the profile file):

export http_proxy=http://your.proxy.server:port
export https_proxy=http://your.proxy.server:port

• Set the proxy for just the current command:

https_proxy=http://your.proxy.server:port huggingface-cli download <model_name>

# or use vllm cmd directly
https_proxy=http://your.proxy.server:port  vllm serve <model_name> --disable-log-requests

• Set the proxy in Python interpreter:

import os

os.environ['http_proxy'] = 'http://your.proxy.server:port'
os.environ['https_proxy'] = 'http://your.proxy.server:port'

ModelScope

To use models from ModelScope instead of Hugging Face Hub, set an environment variable:

export VLLM_USE_MODELSCOPE=True

And use with trust_remote_code=True.

from vllm import LLM

llm = LLM(model=..., revision=..., task=..., trust_remote_code=True)

# For generative models (task=generate) only
output = llm.generate("Hello, my name is")
print(output)

# For pooling models (task={embed,classify,reward,score}) only
output = llm.encode("Hello, my name is")
print(output)

Feature Status Legend

• ✅︎ indicates that the feature is supported for the model.

• 🚧 indicates that the feature is planned but not yet supported for the model.

• ⚠️ indicates that the feature is available but may have known issues or limitations.

List of Text-only Language Models

Generative Models

See this page for more information on how to use generative models.

Text Generation

Specified using --task generate.

Architecture	Models	Example HF Models	LoRA	PP	V1
AquilaForCausalLM	Aquila, Aquila2	BAAI/Aquila-7B, BAAI/AquilaChat-7B, etc.	✅︎	✅︎	✅︎
ArcticForCausalLM	Arctic	Snowflake/snowflake-arctic-base, Snowflake/snowflake-arctic-instruct, etc.		✅︎	✅︎
BaiChuanForCausalLM	Baichuan2, Baichuan	baichuan-inc/Baichuan2-13B-Chat, baichuan-inc/Baichuan-7B, etc.	✅︎	✅︎	✅︎
BambaForCausalLM	Bamba	ibm-ai-platform/Bamba-9B-fp8, ibm-ai-platform/Bamba-9B	✅︎	✅︎
BloomForCausalLM	BLOOM, BLOOMZ, BLOOMChat	bigscience/bloom, bigscience/bloomz, etc.		✅︎
BartForConditionalGeneration	BART	facebook/bart-base, facebook/bart-large-cnn, etc.
ChatGLMModel, ChatGLMForConditionalGeneration	ChatGLM	THUDM/chatglm2-6b, THUDM/chatglm3-6b, ShieldLM-6B-chatglm3, etc.	✅︎	✅︎	✅︎
CohereForCausalLM, Cohere2ForCausalLM	Command-R	CohereForAI/c4ai-command-r-v01, CohereForAI/c4ai-command-r7b-12-2024, etc.	✅︎	✅︎	✅︎
DbrxForCausalLM	DBRX	databricks/dbrx-base, databricks/dbrx-instruct, etc.		✅︎	✅︎
DeciLMForCausalLM	DeciLM	nvidia/Llama-3_3-Nemotron-Super-49B-v1, etc.	✅︎	✅︎	✅︎
DeepseekForCausalLM	DeepSeek	deepseek-ai/deepseek-llm-67b-base, deepseek-ai/deepseek-llm-7b-chat etc.		✅︎	✅︎
DeepseekV2ForCausalLM	DeepSeek-V2	deepseek-ai/DeepSeek-V2, deepseek-ai/DeepSeek-V2-Chat etc.		✅︎	✅︎
DeepseekV3ForCausalLM	DeepSeek-V3	deepseek-ai/DeepSeek-V3-Base, deepseek-ai/DeepSeek-V3 etc.		✅︎	✅︎
Dots1ForCausalLM	dots.llm1	rednote-hilab/dots.llm1.base, rednote-hilab/dots.llm1.inst etc.		✅︎	✅︎
Ernie4_5_ForCausalLM	Ernie4.5	baidu/ERNIE-4.5-0.3B-PT,etc.		✅︎	✅︎
Ernie4_5_MoeForCausalLM	Ernie4.5MoE	baidu/ERNIE-4.5-21B-A3B-PT, baidu/ERNIE-4.5-300B-A47B-PT, etc.		✅︎	✅︎
ExaoneForCausalLM	EXAONE-3	LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct, etc.	✅︎	✅︎	✅︎
FalconForCausalLM	Falcon	tiiuae/falcon-7b, tiiuae/falcon-40b, tiiuae/falcon-rw-7b, etc.		✅︎	✅︎
FalconMambaForCausalLM	FalconMamba	tiiuae/falcon-mamba-7b, tiiuae/falcon-mamba-7b-instruct, etc.		✅︎	✅︎
FalconH1ForCausalLM	Falcon-H1	tiiuae/Falcon-H1-34B-Base, tiiuae/Falcon-H1-34B-Instruct, etc.	✅︎	✅︎
GemmaForCausalLM	Gemma	google/gemma-2b, google/gemma-1.1-2b-it, etc.	✅︎	✅︎	✅︎
Gemma2ForCausalLM	Gemma 2	google/gemma-2-9b, google/gemma-2-27b, etc.	✅︎	✅︎	✅︎
Gemma3ForCausalLM	Gemma 3	google/gemma-3-1b-it, etc.	✅︎	✅︎	✅︎
Gemma3nForConditionalGeneration	Gemma 3n	google/gemma-3n-E2B-it, google/gemma-3n-E4B-it, etc.			✅︎
GlmForCausalLM	GLM-4	THUDM/glm-4-9b-chat-hf, etc.	✅︎	✅︎	✅︎
Glm4ForCausalLM	GLM-4-0414	THUDM/GLM-4-32B-0414, etc.	✅︎	✅︎	✅︎
GPT2LMHeadModel	GPT-2	gpt2, gpt2-xl, etc.		✅︎	✅︎
GPTBigCodeForCausalLM	StarCoder, SantaCoder, WizardCoder	bigcode/starcoder, bigcode/gpt_bigcode-santacoder, WizardLM/WizardCoder-15B-V1.0, etc.	✅︎	✅︎	✅︎
GPTJForCausalLM	GPT-J	EleutherAI/gpt-j-6b, nomic-ai/gpt4all-j, etc.		✅︎	✅︎
GPTNeoXForCausalLM	GPT-NeoX, Pythia, OpenAssistant, Dolly V2, StableLM	EleutherAI/gpt-neox-20b, EleutherAI/pythia-12b, OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5, databricks/dolly-v2-12b, stabilityai/stablelm-tuned-alpha-7b, etc.		✅︎	✅︎
GraniteForCausalLM	Granite 3.0, Granite 3.1, PowerLM	ibm-granite/granite-3.0-2b-base, ibm-granite/granite-3.1-8b-instruct, ibm/PowerLM-3b, etc.	✅︎	✅︎	✅︎
GraniteMoeForCausalLM	Granite 3.0 MoE, PowerMoE	ibm-granite/granite-3.0-1b-a400m-base, ibm-granite/granite-3.0-3b-a800m-instruct, ibm/PowerMoE-3b, etc.	✅︎	✅︎	✅︎
GraniteMoeHybridForCausalLM	Granite 4.0 MoE Hybrid	ibm-granite/granite-4.0-tiny-preview, etc.	✅︎	✅︎
GraniteMoeSharedForCausalLM	Granite MoE Shared	ibm-research/moe-7b-1b-active-shared-experts (test model)	✅︎	✅︎	✅︎
GritLM	GritLM	parasail-ai/GritLM-7B-vllm.	✅︎	✅︎
Grok1ModelForCausalLM	Grok1	hpcai-tech/grok-1.	✅︎	✅︎	✅︎
HunYuanMoEV1ForCausalLM	Hunyuan-80B-A13B	tencent/Hunyuan-A13B-Instruct, tencent/Hunyuan-A13B-Pretrain, tencent/Hunyuan-A13B-Instruct-FP8etc.			✅︎
InternLMForCausalLM	InternLM	internlm/internlm-7b, internlm/internlm-chat-7b, etc.	✅︎	✅︎	✅︎
InternLM2ForCausalLM	InternLM2	internlm/internlm2-7b, internlm/internlm2-chat-7b, etc.	✅︎	✅︎	✅︎
InternLM3ForCausalLM	InternLM3	internlm/internlm3-8b-instruct, etc.	✅︎	✅︎	✅︎
JAISLMHeadModel	Jais	inceptionai/jais-13b, inceptionai/jais-13b-chat, inceptionai/jais-30b-v3, inceptionai/jais-30b-chat-v3, etc.		✅︎	✅︎
JambaForCausalLM	Jamba	ai21labs/AI21-Jamba-1.5-Large, ai21labs/AI21-Jamba-1.5-Mini, ai21labs/Jamba-v0.1, etc.	✅︎	✅︎
LlamaForCausalLM	Llama 3.1, Llama 3, Llama 2, LLaMA, Yi	meta-llama/Meta-Llama-3.1-405B-Instruct, meta-llama/Meta-Llama-3.1-70B, meta-llama/Meta-Llama-3-70B-Instruct, meta-llama/Llama-2-70b-hf, 01-ai/Yi-34B, etc.	✅︎	✅︎	✅︎
MambaForCausalLM	Mamba	state-spaces/mamba-130m-hf, state-spaces/mamba-790m-hf, state-spaces/mamba-2.8b-hf, etc.		✅︎
Mamba2ForCausalLM	Mamba2	mistralai/Mamba-Codestral-7B-v0.1, etc.		✅︎
MiniCPMForCausalLM	MiniCPM	openbmb/MiniCPM-2B-sft-bf16, openbmb/MiniCPM-2B-dpo-bf16, openbmb/MiniCPM-S-1B-sft, etc.	✅︎	✅︎	✅︎
MiniCPM3ForCausalLM	MiniCPM3	openbmb/MiniCPM3-4B, etc.	✅︎	✅︎	✅︎
MistralForCausalLM	Mistral, Mistral-Instruct	mistralai/Mistral-7B-v0.1, mistralai/Mistral-7B-Instruct-v0.1, etc.	✅︎	✅︎	✅︎
MixtralForCausalLM	Mixtral-8x7B, Mixtral-8x7B-Instruct	mistralai/Mixtral-8x7B-v0.1, mistralai/Mixtral-8x7B-Instruct-v0.1, mistral-community/Mixtral-8x22B-v0.1, etc.	✅︎	✅︎	✅︎
MPTForCausalLM	MPT, MPT-Instruct, MPT-Chat, MPT-StoryWriter	mosaicml/mpt-7b, mosaicml/mpt-7b-storywriter, mosaicml/mpt-30b, etc.		✅︎	✅︎
NemotronForCausalLM	Nemotron-3, Nemotron-4, Minitron	nvidia/Minitron-8B-Base, mgoin/Nemotron-4-340B-Base-hf-FP8, etc.	✅︎	✅︎	✅︎
NemotronHForCausalLM	Nemotron-H	nvidia/Nemotron-H-8B-Base-8K, nvidia/Nemotron-H-47B-Base-8K, nvidia/Nemotron-H-56B-Base-8K, etc.	✅︎	✅︎
OLMoForCausalLM	OLMo	allenai/OLMo-1B-hf, allenai/OLMo-7B-hf, etc.		✅︎	✅︎
OLMo2ForCausalLM	OLMo2	allenai/OLMo-2-0425-1B, etc.		✅︎	✅︎
OLMoEForCausalLM	OLMoE	allenai/OLMoE-1B-7B-0924, allenai/OLMoE-1B-7B-0924-Instruct, etc.		✅︎	✅︎
OPTForCausalLM	OPT, OPT-IML	facebook/opt-66b, facebook/opt-iml-max-30b, etc.		✅︎	✅︎
OrionForCausalLM	Orion	OrionStarAI/Orion-14B-Base, OrionStarAI/Orion-14B-Chat, etc.		✅︎	✅︎
PhiForCausalLM	Phi	microsoft/phi-1_5, microsoft/phi-2, etc.	✅︎	✅︎	✅︎
Phi3ForCausalLM	Phi-4, Phi-3	microsoft/Phi-4-mini-instruct, microsoft/Phi-4, microsoft/Phi-3-mini-4k-instruct, microsoft/Phi-3-mini-128k-instruct, microsoft/Phi-3-medium-128k-instruct, etc.	✅︎	✅︎	✅︎
Phi3SmallForCausalLM	Phi-3-Small	microsoft/Phi-3-small-8k-instruct, microsoft/Phi-3-small-128k-instruct, etc.		✅︎	✅︎
PhiMoEForCausalLM	Phi-3.5-MoE	microsoft/Phi-3.5-MoE-instruct, etc.	✅︎	✅︎	✅︎
PersimmonForCausalLM	Persimmon	adept/persimmon-8b-base, adept/persimmon-8b-chat, etc.		✅︎	✅︎
Plamo2ForCausalLM	PLaMo2	pfnet/plamo-2-1b, pfnet/plamo-2-8b, etc.
QWenLMHeadModel	Qwen	Qwen/Qwen-7B, Qwen/Qwen-7B-Chat, etc.	✅︎	✅︎	✅︎
Qwen2ForCausalLM	QwQ, Qwen2	Qwen/QwQ-32B-Preview, Qwen/Qwen2-7B-Instruct, Qwen/Qwen2-7B, etc.	✅︎	✅︎	✅︎
Qwen2MoeForCausalLM	Qwen2MoE	Qwen/Qwen1.5-MoE-A2.7B, Qwen/Qwen1.5-MoE-A2.7B-Chat, etc.		✅︎	✅︎
Qwen3ForCausalLM	Qwen3	Qwen/Qwen3-8B, etc.	✅︎	✅︎	✅︎
Qwen3MoeForCausalLM	Qwen3MoE	Qwen/Qwen3-30B-A3B, etc.		✅︎	✅︎
StableLmForCausalLM	StableLM	stabilityai/stablelm-3b-4e1t, stabilityai/stablelm-base-alpha-7b-v2, etc.			✅︎
Starcoder2ForCausalLM	Starcoder2	bigcode/starcoder2-3b, bigcode/starcoder2-7b, bigcode/starcoder2-15b, etc.		✅︎	✅︎
SolarForCausalLM	Solar Pro	upstage/solar-pro-preview-instruct, etc.	✅︎	✅︎	✅︎
TeleChat2ForCausalLM	TeleChat2	Tele-AI/TeleChat2-3B, Tele-AI/TeleChat2-7B, Tele-AI/TeleChat2-35B, etc.	✅︎	✅︎	✅︎
TeleFLMForCausalLM	TeleFLM	CofeAI/FLM-2-52B-Instruct-2407, CofeAI/Tele-FLM, etc.	✅︎	✅︎	✅︎
XverseForCausalLM	XVERSE	xverse/XVERSE-7B-Chat, xverse/XVERSE-13B-Chat, xverse/XVERSE-65B-Chat, etc.	✅︎	✅︎	✅︎
MiniMaxM1ForCausalLM	MiniMax-Text	MiniMaxAI/MiniMax-M1-40k, MiniMaxAI/MiniMax-M1-80ketc.
MiniMaxText01ForCausalLM	MiniMax-Text	MiniMaxAI/MiniMax-Text-01, etc.
Zamba2ForCausalLM	Zamba2	Zyphra/Zamba2-7B-instruct, Zyphra/Zamba2-2.7B-instruct, Zyphra/Zamba2-1.2B-instruct, etc.

Note

Currently, the ROCm version of vLLM supports Mistral and Mixtral only for context lengths up to 4096.

Note

Only text inputs are currently supported for Gemma3nForConditionalGeneration. To use this model, please upgrade Hugging Face Transformers to version 4.53.0.

Pooling Models

See this page for more information on how to use pooling models.

Important

Since some model architectures support both generative and pooling tasks, you should explicitly specify the task type to ensure that the model is used in pooling mode instead of generative mode.

Text Embedding

Specified using --task embed.

Architecture	Models	Example HF Models	LoRA	PP	V1
BertModel	BERT-based	BAAI/bge-base-en-v1.5, Snowflake/snowflake-arctic-embed-xs, etc.
Gemma2Model	Gemma 2-based	BAAI/bge-multilingual-gemma2, etc.	✅︎		✅︎
GritLM	GritLM	parasail-ai/GritLM-7B-vllm.	✅︎	✅︎
GteModel	Arctic-Embed-2.0-M	Snowflake/snowflake-arctic-embed-m-v2.0.	︎
GteNewModel	mGTE-TRM (see note)	Alibaba-NLP/gte-multilingual-base, etc.	︎	︎
ModernBertModel	ModernBERT-based	Alibaba-NLP/gte-modernbert-base, etc.	︎	︎
NomicBertModel	Nomic BERT	nomic-ai/nomic-embed-text-v1, nomic-ai/nomic-embed-text-v2-moe, Snowflake/snowflake-arctic-embed-m-long, etc.	︎	︎
LlamaModel, LlamaForCausalLM, MistralModel, etc.	Llama-based	intfloat/e5-mistral-7b-instruct, etc.	✅︎	✅︎	✅︎
Qwen2Model, Qwen2ForCausalLM	Qwen2-based	ssmits/Qwen2-7B-Instruct-embed-base (see note), Alibaba-NLP/gte-Qwen2-7B-instruct (see note), etc.	✅︎	✅︎	✅︎
Qwen3Model, Qwen3ForCausalLM	Qwen3-based	Qwen/Qwen3-Embedding-0.6B, etc.	✅︎	✅︎	✅︎
RobertaModel, RobertaForMaskedLM	RoBERTa-based	sentence-transformers/all-roberta-large-v1, etc.

Note

ssmits/Qwen2-7B-Instruct-embed-base has an improperly defined Sentence Transformers config. You need to manually set mean pooling by passing --override-pooler-config '{"pooling_type": "MEAN"}'.

Note

For Alibaba-NLP/gte-Qwen2-*, you need to enable --trust-remote-code for the correct tokenizer to be loaded. See relevant issue on HF Transformers.

Note

jinaai/jina-embeddings-v3 supports multiple tasks through LoRA, while vllm temporarily only supports text-matching tasks by merging LoRA weights.

Note

The second-generation GTE model (mGTE-TRM) is named NewModel. The name NewModel is too generic, you should set --hf-overrides '{"architectures": ["GteNewModel"]}' to specify the use of the GteNewModel architecture.

If your model is not in the above list, we will try to automatically convert the model using as_embedding_model. By default, the embeddings of the whole prompt are extracted from the normalized hidden state corresponding to the last token.

Reward Modeling

Specified using --task reward.

Architecture	Models	Example HF Models	LoRA	PP	V1
InternLM2ForRewardModel	InternLM2-based	internlm/internlm2-1_8b-reward, internlm/internlm2-7b-reward, etc.	✅︎	✅︎	✅︎
LlamaForCausalLM	Llama-based	peiyi9979/math-shepherd-mistral-7b-prm, etc.	✅︎	✅︎	✅︎
Qwen2ForRewardModel	Qwen2-based	Qwen/Qwen2.5-Math-RM-72B, etc.	✅︎	✅︎	✅︎
Qwen2ForProcessRewardModel	Qwen2-based	Qwen/Qwen2.5-Math-PRM-7B, etc.	✅︎	✅︎	✅︎

If your model is not in the above list, we will try to automatically convert the model using as_reward_model. By default, we return the hidden states of each token directly.

Important

For process-supervised reward models such as peiyi9979/math-shepherd-mistral-7b-prm, the pooling config should be set explicitly, e.g.: --override-pooler-config '{"pooling_type": "STEP", "step_tag_id": 123, "returned_token_ids": [456, 789]}'.

Classification

Specified using --task classify.

Architecture	Models	Example HF Models	LoRA	PP	V1
JambaForSequenceClassification	Jamba	ai21labs/Jamba-tiny-reward-dev, etc.	✅︎	✅︎
GPT2ForSequenceClassification	GPT2	nie3e/sentiment-polish-gpt2-small			✅︎

If your model is not in the above list, we will try to automatically convert the model using as_seq_cls_model. By default, the class probabilities are extracted from the softmaxed hidden state corresponding to the last token.

Sentence Pair Scoring

Specified using --task score.

Architecture	Models	Example HF Models	V1
BertForSequenceClassification	BERT-based	cross-encoder/ms-marco-MiniLM-L-6-v2, etc.
Qwen2ForSequenceClassification	Qwen2-based	mixedbread-ai/mxbai-rerank-base-v2 (see note), etc.	✅︎
Qwen3ForSequenceClassification	Qwen3-based	tomaarsen/Qwen3-Reranker-0.6B-seq-cls, Qwen/Qwen3-Reranker-0.6B (see note), etc.	✅︎
RobertaForSequenceClassification	RoBERTa-based	cross-encoder/quora-roberta-base, etc.
XLMRobertaForSequenceClassification	XLM-RoBERTa-based	BAAI/bge-reranker-v2-m3, etc.

Note

Load the official original mxbai-rerank-v2 by using the following command.

vllm serve mixedbread-ai/mxbai-rerank-base-v2 --hf_overrides '{"architectures": ["Qwen2ForSequenceClassification"],"classifier_from_token": ["0", "1"], "method": "from_2_way_softmax"}'

Note

Load the official original Qwen3 Reranker by using the following command. More information can be found at: examples/offline_inference/qwen3_reranker.py.

vllm serve Qwen/Qwen3-Reranker-0.6B --hf_overrides '{"architectures": ["Qwen3ForSequenceClassification"],"classifier_from_token": ["no", "yes"],"is_original_qwen3_reranker": true}'

List of Multimodal Language Models

The following modalities are supported depending on the model:

• Text
• Image
• Video
• Audio

Any combination of modalities joined by + are supported.

• e.g.: T + I means that the model supports text-only, image-only, and text-with-image inputs.

On the other hand, modalities separated by / are mutually exclusive.

• e.g.: T / I means that the model supports text-only and image-only inputs, but not text-with-image inputs.

See this page on how to pass multi-modal inputs to the model.

Important

To enable multiple multi-modal items per text prompt in vLLM V0, you have to set limit_mm_per_prompt (offline inference) or --limit-mm-per-prompt (online serving). For example, to enable passing up to 4 images per text prompt:

Offline inference:

from vllm import LLM

llm = LLM(
    model="Qwen/Qwen2-VL-7B-Instruct",
    limit_mm_per_prompt={"image": 4},
)

Online serving:

vllm serve Qwen/Qwen2-VL-7B-Instruct --limit-mm-per-prompt '{"image":4}'

This is no longer required if you are using vLLM V1.

Note

vLLM currently only supports adding LoRA to the language backbone of multimodal models.

Generative Models

See this page for more information on how to use generative models.

Text Generation

Specified using --task generate.

Architecture	Models	Inputs	Example HF Models	LoRA	PP	V1
AriaForConditionalGeneration	Aria	T + I+	rhymes-ai/Aria			✅︎
AyaVisionForConditionalGeneration	Aya Vision	T + I+	CohereForAI/aya-vision-8b, CohereForAI/aya-vision-32b, etc.		✅︎	✅︎
Blip2ForConditionalGeneration	BLIP-2	T + IE	Salesforce/blip2-opt-2.7b, Salesforce/blip2-opt-6.7b, etc.		✅︎	✅︎
ChameleonForConditionalGeneration	Chameleon	T + I	facebook/chameleon-7b etc.		✅︎	✅︎
DeepseekVLV2ForCausalLM^	DeepSeek-VL2	T + I+	deepseek-ai/deepseek-vl2-tiny, deepseek-ai/deepseek-vl2-small, deepseek-ai/deepseek-vl2 etc.		✅︎	✅︎
Florence2ForConditionalGeneration	Florence-2	T + I	microsoft/Florence-2-base, microsoft/Florence-2-large etc.
FuyuForCausalLM	Fuyu	T + I	adept/fuyu-8b etc.		✅︎	✅︎
Gemma3ForConditionalGeneration	Gemma 3	T + I+	google/gemma-3-4b-it, google/gemma-3-27b-it, etc.	✅︎	✅︎	⚠️
GLM4VForCausalLM^	GLM-4V	T + I	THUDM/glm-4v-9b, THUDM/cogagent-9b-20241220 etc.	✅︎	✅︎	✅︎
Glm4vForConditionalGeneration	GLM-4.1V-Thinking	T + IE+ + VE+	THUDM/GLM-4.1V-9B-Thinkg, etc.	✅︎	✅︎	✅︎
GraniteSpeechForConditionalGeneration	Granite Speech	T + A	ibm-granite/granite-speech-3.3-8b	✅︎	✅︎	✅︎
H2OVLChatModel	H2OVL	T + IE+	h2oai/h2ovl-mississippi-800m, h2oai/h2ovl-mississippi-2b, etc.		✅︎	✅︎*
Idefics3ForConditionalGeneration	Idefics3	T + I	HuggingFaceM4/Idefics3-8B-Llama3 etc.	✅︎		✅︎
InternVLChatModel	InternVL 3.0, InternVideo 2.5, InternVL 2.5, Mono-InternVL, InternVL 2.0	T + IE+ + (VE+)	OpenGVLab/InternVL3-9B, OpenGVLab/InternVideo2_5_Chat_8B, OpenGVLab/InternVL2_5-4B, OpenGVLab/Mono-InternVL-2B, OpenGVLab/InternVL2-4B, etc.	✅︎	✅︎	✅︎
KeyeForConditionalGeneration	Keye-VL-8B-Preview	T + IE+ + VE+	Kwai-Keye/Keye-VL-8B-Preview			✅︎
KimiVLForConditionalGeneration	Kimi-VL-A3B-Instruct, Kimi-VL-A3B-Thinking	T + I+	moonshotai/Kimi-VL-A3B-Instruct, moonshotai/Kimi-VL-A3B-Thinking			✅︎
Llama4ForConditionalGeneration	Llama 4	T + I+	meta-llama/Llama-4-Scout-17B-16E-Instruct, meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8, meta-llama/Llama-4-Maverick-17B-128E-Instruct, etc.		✅︎	✅︎
LlavaForConditionalGeneration	LLaVA-1.5	T + IE+	llava-hf/llava-1.5-7b-hf, TIGER-Lab/Mantis-8B-siglip-llama3 (see note), etc.		✅︎	✅︎
LlavaNextForConditionalGeneration	LLaVA-NeXT	T + IE+	llava-hf/llava-v1.6-mistral-7b-hf, llava-hf/llava-v1.6-vicuna-7b-hf, etc.		✅︎	✅︎
LlavaNextVideoForConditionalGeneration	LLaVA-NeXT-Video	T + V	llava-hf/LLaVA-NeXT-Video-7B-hf, etc.		✅︎	✅︎
LlavaOnevisionForConditionalGeneration	LLaVA-Onevision	T + I+ + V+	llava-hf/llava-onevision-qwen2-7b-ov-hf, llava-hf/llava-onevision-qwen2-0.5b-ov-hf, etc.		✅︎	✅︎
MiniCPMO	MiniCPM-O	T + IE+ + VE+ + AE+	openbmb/MiniCPM-o-2_6, etc.	✅︎	✅︎	✅︎
MiniCPMV	MiniCPM-V	T + IE+ + VE+	openbmb/MiniCPM-V-2 (see note), openbmb/MiniCPM-Llama3-V-2_5, openbmb/MiniCPM-V-2_6, etc.	✅︎		✅︎
MiniMaxVL01ForConditionalGeneration	MiniMax-VL	T + IE+	MiniMaxAI/MiniMax-VL-01, etc.		✅︎	✅︎
Mistral3ForConditionalGeneration	Mistral3	T + I+	mistralai/Mistral-Small-3.1-24B-Instruct-2503, etc.	✅︎	✅︎	✅︎
MllamaForConditionalGeneration	Llama 3.2	T + I+	meta-llama/Llama-3.2-90B-Vision-Instruct, meta-llama/Llama-3.2-11B-Vision, etc.
MolmoForCausalLM	Molmo	T + I+	allenai/Molmo-7B-D-0924, allenai/Molmo-7B-O-0924, etc.	✅︎	✅︎	✅︎
NVLM_D_Model	NVLM-D 1.0	T + I+	nvidia/NVLM-D-72B, etc.		✅︎	✅︎
Ovis	Ovis2, Ovis1.6	T + I+	AIDC-AI/Ovis2-1B, AIDC-AI/Ovis1.6-Llama3.2-3B, etc.		✅︎	✅︎
PaliGemmaForConditionalGeneration	PaliGemma, PaliGemma 2	T + IE	google/paligemma-3b-pt-224, google/paligemma-3b-mix-224, google/paligemma2-3b-ft-docci-448, etc.		✅︎	⚠️
Phi3VForCausalLM	Phi-3-Vision, Phi-3.5-Vision	T + IE+	microsoft/Phi-3-vision-128k-instruct, microsoft/Phi-3.5-vision-instruct, etc.		✅︎	✅︎
Phi4MMForCausalLM	Phi-4-multimodal	T + I+ / T + A+ / I+ + A+	microsoft/Phi-4-multimodal-instruct, etc.	✅︎	✅︎	✅︎
PixtralForConditionalGeneration	Pixtral	T + I+	mistralai/Mistral-Small-3.1-24B-Instruct-2503, mistral-community/pixtral-12b, etc.		✅︎	✅︎
QwenVLForConditionalGeneration^	Qwen-VL	T + IE+	Qwen/Qwen-VL, Qwen/Qwen-VL-Chat, etc.	✅︎	✅︎	✅︎
Qwen2AudioForConditionalGeneration	Qwen2-Audio	T + A+	Qwen/Qwen2-Audio-7B-Instruct		✅︎	✅︎
Qwen2VLForConditionalGeneration	QVQ, Qwen2-VL	T + IE+ + VE+	Qwen/QVQ-72B-Preview, Qwen/Qwen2-VL-7B-Instruct, Qwen/Qwen2-VL-72B-Instruct, etc.	✅︎	✅︎	✅︎
Qwen2_5_VLForConditionalGeneration	Qwen2.5-VL	T + IE+ + VE+	Qwen/Qwen2.5-VL-3B-Instruct, Qwen/Qwen2.5-VL-72B-Instruct, etc.	✅︎	✅︎	✅︎
Qwen2_5OmniThinkerForConditionalGeneration	Qwen2.5-Omni	T + IE+ + VE+ + A+	Qwen/Qwen2.5-Omni-7B		✅︎	✅︎*
SkyworkR1VChatModel	Skywork-R1V-38B	T + I	Skywork/Skywork-R1V-38B		✅︎	✅︎
SmolVLMForConditionalGeneration	SmolVLM2	T + I	SmolVLM2-2.2B-Instruct	✅︎		✅︎
TarsierForConditionalGeneration	Tarsier	T + IE+	omni-search/Tarsier-7b,omni-search/Tarsier-34b		✅︎	✅︎
Tarsier2ForConditionalGeneration^	Tarsier2	T + IE+ + VE+	omni-research/Tarsier2-Recap-7b,omni-research/Tarsier2-7b-0115		✅︎	✅︎

^{^} You need to set the architecture name via --hf-overrides to match the one in vLLM.
    • For example, to use DeepSeek-VL2 series models:
      --hf-overrides '{"architectures": ["DeepseekVLV2ForCausalLM"]}'
^E Pre-computed embeddings can be inputted for this modality.
⁺ Multiple items can be inputted per text prompt for this modality.

Warning

Both V0 and V1 support Gemma3ForConditionalGeneration for text-only inputs. However, there are differences in how they handle text + image inputs:

V0 correctly implements the model's attention pattern: - Uses bidirectional attention between the image tokens corresponding to the same image - Uses causal attention for other tokens - Implemented via (naive) PyTorch SDPA with masking tensors - Note: May use significant memory for long prompts with image

V1 currently uses a simplified attention pattern: - Uses causal attention for all tokens, including image tokens - Generates reasonable outputs but does not match the original model's attention for text + image inputs, especially when {"do_pan_and_scan": true} - Will be updated in the future to support the correct behavior

This limitation exists because the model's mixed attention pattern (bidirectional for images, causal otherwise) is not yet supported by vLLM's attention backends.

Note

Only InternVLChatModel with Qwen2.5 text backbone (OpenGVLab/InternVL3-2B, OpenGVLab/InternVL2.5-1B etc) has video inputs support currently.

Note

h2oai/h2ovl-mississippi-2b will be available in V1 once we support head size 80.

Note

To use TIGER-Lab/Mantis-8B-siglip-llama3, you have to pass --hf_overrides '{"architectures": ["MantisForConditionalGeneration"]}' when running vLLM.

Warning

The output quality of AllenAI/Molmo-7B-D-0924 (especially in object localization tasks) has deteriorated in recent updates.

For the best results, we recommend using the following dependency versions (tested on A10 and L40):

Dependency

# Core vLLM-compatible dependencies with Molmo accuracy setup (tested on L40)
torch==2.5.1
torchvision==0.20.1
transformers==4.48.1
tokenizers==0.21.0
tiktoken==0.7.0
vllm==0.7.0

# Optional but recommended for improved performance and stability
triton==3.1.0
xformers==0.0.28.post3
uvloop==0.21.0
protobuf==5.29.3
openai==1.60.2
opencv-python-headless==4.11.0.86
pillow==10.4.0

# Installed FlashAttention (for float16 only)
flash-attn>=2.5.6  # Not used in float32, but should be documented

Note: Make sure you understand the security implications of using outdated packages.

Note

The official openbmb/MiniCPM-V-2 doesn't work yet, so we need to use a fork (HwwwH/MiniCPM-V-2) for now. For more details, please see: Pull Request #4087

Warning

Our PaliGemma implementations have the same problem as Gemma 3 (see above) for both V0 and V1.

Note

To use Qwen2.5-Omni, you have to install Hugging Face Transformers library from source via pip install git+https://github.com/huggingface/transformers.git.

Read audio from video pre-processing is currently supported on V0 (but not V1), because overlapping modalities is not yet supported in V1. --mm-processor-kwargs '{"use_audio_in_video": true}'.

Transcription

Specified using --task transcription.

Speech2Text models trained specifically for Automatic Speech Recognition.

Architecture	Models	Example HF Models	LoRA	PP	V1
WhisperForConditionalGeneration	Whisper	openai/whisper-small, openai/whisper-large-v3-turbo, etc.

Pooling Models

See this page for more information on how to use pooling models.

Important

Since some model architectures support both generative and pooling tasks, you should explicitly specify the task type to ensure that the model is used in pooling mode instead of generative mode.

Text Embedding

Specified using --task embed.

Any text generation model can be converted into an embedding model by passing --task embed.

Note

To get the best results, you should use pooling models that are specifically trained as such.

The following table lists those that are tested in vLLM.

Architecture	Models	Inputs	Example HF Models	LoRA	PP	V1
LlavaNextForConditionalGeneration	LLaVA-NeXT-based	T / I	royokong/e5-v
Phi3VForCausalLM	Phi-3-Vision-based	T + I	TIGER-Lab/VLM2Vec-Full	🚧	✅︎

---

Model Support Policy

At vLLM, we are committed to facilitating the integration and support of third-party models within our ecosystem. Our approach is designed to balance the need for robustness and the practical limitations of supporting a wide range of models. Here’s how we manage third-party model support:

1. Community-Driven Support: We encourage community contributions for adding new models. When a user requests support for a new model, we welcome pull requests (PRs) from the community. These contributions are evaluated primarily on the sensibility of the output they generate, rather than strict consistency with existing implementations such as those in transformers. Call for contribution: PRs coming directly from model vendors are greatly appreciated!

2. Best-Effort Consistency: While we aim to maintain a level of consistency between the models implemented in vLLM and other frameworks like transformers, complete alignment is not always feasible. Factors like acceleration techniques and the use of low-precision computations can introduce discrepancies. Our commitment is to ensure that the implemented models are functional and produce sensible results.

   Tip

   When comparing the output of model.generate from Hugging Face Transformers with the output of llm.generate from vLLM, note that the former reads the model's generation config file (i.e., generation_config.json) and applies the default parameters for generation, while the latter only uses the parameters passed to the function. Ensure all sampling parameters are identical when comparing outputs.

3. Issue Resolution and Model Updates: Users are encouraged to report any bugs or issues they encounter with third-party models. Proposed fixes should be submitted via PRs, with a clear explanation of the problem and the rationale behind the proposed solution. If a fix for one model impacts another, we rely on the community to highlight and address these cross-model dependencies. Note: for bugfix PRs, it is good etiquette to inform the original author to seek their feedback.

4. Monitoring and Updates: Users interested in specific models should monitor the commit history for those models (e.g., by tracking changes in the main/vllm/model_executor/models directory). This proactive approach helps users stay informed about updates and changes that may affect the models they use.

5. Selective Focus: Our resources are primarily directed towards models with significant user interest and impact. Models that are less frequently used may receive less attention, and we rely on the community to play a more active role in their upkeep and improvement.

Through this approach, vLLM fosters a collaborative environment where both the core development team and the broader community contribute to the robustness and diversity of the third-party models supported in our ecosystem.

Note that, as an inference engine, vLLM does not introduce new models. Therefore, all models supported by vLLM are third-party models in this regard.

We have the following levels of testing for models:

1. Strict Consistency: We compare the output of the model with the output of the model in the HuggingFace Transformers library under greedy decoding. This is the most stringent test. Please refer to models tests for the models that have passed this test.
2. Output Sensibility: We check if the output of the model is sensible and coherent, by measuring the perplexity of the output and checking for any obvious errors. This is a less stringent test.
3. Runtime Functionality: We check if the model can be loaded and run without errors. This is the least stringent test. Please refer to functionality tests and examples for the models that have passed this test.
4. Community Feedback: We rely on the community to provide feedback on the models. If a model is broken or not working as expected, we encourage users to raise issues to report it or open pull requests to fix it. The rest of the models fall under this category.