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Allow markdownlint to run locally (#36398)

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Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>
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Harry Mellor
2026-03-09 03:05:24 +00:00
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parent fde4771bbd
commit a0f44bb616
47 changed files with 394 additions and 392 deletions
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docs/models/supported_models.md
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@@ -179,7 +179,7 @@ class MyConfig(PretrainedConfig):
Some model architectures are supported via vLLM plugins. These plugins extend vLLM's capabilities through the [plugin system](../design/plugin_system.md).
| Architecture | Models | Plugin Repository |
|--------------|--------|-------------------|
| ------------ | ------ | ----------------- |
| `BartForConditionalGeneration` | BART | [bart-plugin](https://github.com/vllm-project/bart-plugin) |
| `Florence2ForConditionalGeneration` | Florence-2 | [bart-plugin](https://github.com/vllm-project/bart-plugin) |
@@ -363,7 +363,7 @@ th {
</style>
| Architecture | Models | Example HF Models | [LoRA](../features/lora.md) | [PP](../serving/parallelism_scaling.md) |
|--------------|--------|-------------------|----------------------|---------------------------|
| ------------ | ------ | ----------------- | -------------------- | ------------------------- |
| `AfmoeForCausalLM` | Afmoe | TBA | ✅︎ | ✅︎ |
| `ApertusForCausalLM` | Apertus | `swiss-ai/Apertus-8B-2509`, `swiss-ai/Apertus-70B-Instruct-2509`, etc. | ✅︎ | ✅︎ |
| `AquilaForCausalLM` | Aquila, Aquila2 | `BAAI/Aquila-7B`, `BAAI/AquilaChat-7B`, etc. | ✅︎ | ✅︎ |
@@ -387,7 +387,7 @@ th {
| `Dots1ForCausalLM` | dots.llm1 | `rednote-hilab/dots.llm1.base`, `rednote-hilab/dots.llm1.inst`, etc. | | ✅︎ |
| `DotsOCRForCausalLM` | dots_ocr | `rednote-hilab/dots.ocr` | ✅︎ | ✅︎ |
| `Ernie4_5ForCausalLM` | 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. |✅︎| ✅︎ |
| `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. | ✅︎ | ✅︎ |
| `ExaoneMoEForCausalLM` | K-EXAONE | `LGAI-EXAONE/K-EXAONE-236B-A23B`, etc. | | |
| `Exaone4ForCausalLM` | EXAONE-4 | `LGAI-EXAONE/EXAONE-4.0-32B`, etc. | ✅︎ | ✅︎ |
@@ -427,18 +427,18 @@ th {
| `Jais2ForCausalLM` | Jais2 | `inceptionai/Jais-2-8B-Chat`, `inceptionai/Jais-2-70B-Chat`, etc. | | ✅︎ |
| `JambaForCausalLM` | Jamba | `ai21labs/AI21-Jamba-1.5-Large`, `ai21labs/AI21-Jamba-1.5-Mini`, `ai21labs/Jamba-v0.1`, etc. | ✅︎ | ✅︎ |
| `KimiLinearForCausalLM` | Kimi-Linear-48B-A3B-Base, Kimi-Linear-48B-A3B-Instruct | `moonshotai/Kimi-Linear-48B-A3B-Base`, `moonshotai/Kimi-Linear-48B-A3B-Instruct` | | ✅︎ |
| `Lfm2ForCausalLM` | LFM2 | `LiquidAI/LFM2-1.2B`, `LiquidAI/LFM2-700M`, `LiquidAI/LFM2-350M`, etc. | ✅︎ | ✅︎ |
| `Lfm2MoeForCausalLM` | LFM2MoE | `LiquidAI/LFM2-8B-A1B-preview`, etc. | ✅︎ | ✅︎ |
| `Lfm2ForCausalLM` | LFM2 | `LiquidAI/LFM2-1.2B`, `LiquidAI/LFM2-700M`, `LiquidAI/LFM2-350M`, etc. | ✅︎ | ✅︎ |
| `Lfm2MoeForCausalLM` | LFM2MoE | `LiquidAI/LFM2-8B-A1B-preview`, 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. | ✅︎ | ✅︎ |
| `LongcatFlashForCausalLM` | LongCat-Flash | `meituan-longcat/LongCat-Flash-Chat`, `meituan-longcat/LongCat-Flash-Chat-FP8` | ✅︎ | ✅︎ |
| `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. | | ✅︎ |
| `MiMoForCausalLM` | MiMo | `XiaomiMiMo/MiMo-7B-RL`, etc. | ✅︎ | ✅︎ |
| `MiMoV2FlashForCausalLM` | MiMoV2Flash | `XiaomiMiMo/MiMo-V2-Flash`, etc. | ︎| ✅︎ |
| `MiMoV2FlashForCausalLM` | MiMoV2Flash | `XiaomiMiMo/MiMo-V2-Flash`, 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. | ✅︎ | ✅︎ |
| `MiniMaxForCausalLM` | MiniMax-Text | `MiniMaxAI/MiniMax-Text-01-hf`, etc. | | |
| `MiniMaxM2ForCausalLM` | MiniMax-M2, MiniMax-M2.1 |`MiniMaxAI/MiniMax-M2`, etc. | ✅︎ | ✅︎ |
| `MiniMaxM2ForCausalLM` | MiniMax-M2, MiniMax-M2.1 | `MiniMaxAI/MiniMax-M2`, etc. | ✅︎ | ✅︎ |
| `MistralForCausalLM` | Ministral-3, Mistral, Mistral-Instruct | `mistralai/Ministral-3-3B-Instruct-2512`, `mistralai/Mistral-7B-v0.1`, `mistralai/Mistral-7B-Instruct-v0.1`, etc. | ✅︎ | ✅︎ |
| `MistralLarge3ForCausalLM` | Mistral-Large-3-675B-Base-2512, Mistral-Large-3-675B-Instruct-2512 | `mistralai/Mistral-Large-3-675B-Base-2512`, `mistralai/Mistral-Large-3-675B-Instruct-2512`, 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. | ✅︎ | ✅︎ |
@@ -453,9 +453,9 @@ th {
| `OPTForCausalLM` | OPT, OPT-IML | `facebook/opt-66b`, `facebook/opt-iml-max-30b`, etc. | ✅︎ | ✅︎ |
| `OrionForCausalLM` | Orion | `OrionStarAI/Orion-14B-Base`, `OrionStarAI/Orion-14B-Chat`, etc. | | ✅︎ |
| `OuroForCausalLM` | ouro | `ByteDance/Ouro-1.4B`, `ByteDance/Ouro-2.6B`, etc. | ✅︎ | |
| `PanguEmbeddedForCausalLM` |openPangu-Embedded-7B | `FreedomIntelligence/openPangu-Embedded-7B-V1.1` | ✅︎ | ✅︎ |
| `PanguProMoEV2ForCausalLM` |openpangu-pro-moe-v2 | | ✅︎ | ✅︎ |
| `PanguUltraMoEForCausalLM` |openpangu-ultra-moe-718b-model | `FreedomIntelligence/openPangu-Ultra-MoE-718B-V1.1` | ✅︎ | ✅︎ |
| `PanguEmbeddedForCausalLM` | openPangu-Embedded-7B | `FreedomIntelligence/openPangu-Embedded-7B-V1.1` | ✅︎ | ✅︎ |
| `PanguProMoEV2ForCausalLM` | openpangu-pro-moe-v2 | | ✅︎ | ✅︎ |
| `PanguUltraMoEForCausalLM` | openpangu-ultra-moe-718b-model | `FreedomIntelligence/openPangu-Ultra-MoE-718B-V1.1` | ✅︎ | ✅︎ |
| `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. | ✅︎ | ✅︎ |
| `PhiMoEForCausalLM` | Phi-3.5-MoE | `microsoft/Phi-3.5-MoE-instruct`, etc. | ✅︎ | ✅︎ |
@@ -477,7 +477,7 @@ th {
| `StableLMEpochForCausalLM` | StableLM Epoch | `stabilityai/stablelm-zephyr-3b`, etc. | | ✅︎ |
| `Starcoder2ForCausalLM` | Starcoder2 | `bigcode/starcoder2-3b`, `bigcode/starcoder2-7b`, `bigcode/starcoder2-15b`, etc. | | ✅︎ |
| `Step1ForCausalLM` | Step-Audio | `stepfun-ai/Step-Audio-EditX`, etc. | ✅︎ | ✅︎ |
| `Step3p5ForCausalLM` | Step-3.5-flash | `stepfun-ai/Step-3.5-Flash`, etc. | | ✅︎ |
| `Step3p5ForCausalLM` | Step-3.5-flash | `stepfun-ai/Step-3.5-Flash`, etc. | | ✅︎ |
| `TeleChatForCausalLM` | TeleChat | `chuhac/TeleChat2-35B`, 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. | ✅︎ | ✅︎ |
@@ -492,7 +492,7 @@ th {
Some models are supported only via the [Transformers modeling backend](#transformers). The purpose of the table below is to acknowledge models which we officially support in this way. The logs will say that the Transformers modeling backend is being used, and you will see no warning that this is fallback behaviour. This means that, if you have issues with any of the models listed below, please [make an issue](https://github.com/vllm-project/vllm/issues/new/choose) and we'll do our best to fix it!
| Architecture | Models | Example HF Models | [LoRA](../features/lora.md) | [PP](../serving/parallelism_scaling.md) |
|--------------|--------|-------------------|----------------------|---------------------------|
| ------------ | ------ | ----------------- | -------------------- | ------------------------- |
| `SmolLM3ForCausalLM` | SmolLM3 | `HuggingFaceTB/SmolLM3-3B` | ✅︎ | ✅︎ |
!!! note
@@ -511,16 +511,16 @@ See [this page](./pooling_models.md) for more information on how to use pooling
These models primarily support the [`LLM.embed`](./pooling_models.md#llmembed) API.
| Architecture | Models | Example HF Models | [LoRA](../features/lora.md) | [PP](../serving/parallelism_scaling.md) |
|--------------|--------|-------------------|----------------------|---------------------------|
| ------------ | ------ | ----------------- | -------------------- | ------------------------- |
| `BertModel`<sup>C</sup> | BERT-based | `BAAI/bge-base-en-v1.5`, `Snowflake/snowflake-arctic-embed-xs`, etc. | | |
| `BertSpladeSparseEmbeddingModel` | SPLADE | `naver/splade-v3` | | |
| `Gemma2Model`<sup>C</sup> | Gemma 2-based | `BAAI/bge-multilingual-gemma2`, etc. | ✅︎ | ✅︎ |
| `Gemma3TextModel`<sup>C</sup> | Gemma 3-based | `google/embeddinggemma-300m`, etc. | ✅︎ | ✅︎ |
| `GritLM` | GritLM | `parasail-ai/GritLM-7B-vllm`. | ✅︎ | ✅︎ |
| `GteModel`<sup>C</sup> | Arctic-Embed-2.0-M | `Snowflake/snowflake-arctic-embed-m-v2.0`. | | |
| `GteNewModel`<sup>C</sup> | mGTE-TRM (see note) | `Alibaba-NLP/gte-multilingual-base`, etc. | | |
| `ModernBertModel`<sup>C</sup> | ModernBERT-based | `Alibaba-NLP/gte-modernbert-base`, etc. | | |
| `NomicBertModel`<sup>C</sup> | Nomic BERT | `nomic-ai/nomic-embed-text-v1`, `nomic-ai/nomic-embed-text-v2-moe`, `Snowflake/snowflake-arctic-embed-m-long`, etc. | | |
| `GteModel`<sup>C</sup> | Arctic-Embed-2.0-M | `Snowflake/snowflake-arctic-embed-m-v2.0`. | | |
| `GteNewModel`<sup>C</sup> | mGTE-TRM (see note) | `Alibaba-NLP/gte-multilingual-base`, etc. | | |
| `ModernBertModel`<sup>C</sup> | ModernBERT-based | `Alibaba-NLP/gte-modernbert-base`, etc. | | |
| `NomicBertModel`<sup>C</sup> | Nomic BERT | `nomic-ai/nomic-embed-text-v1`, `nomic-ai/nomic-embed-text-v2-moe`, `Snowflake/snowflake-arctic-embed-m-long`, etc. | | |
| `LlamaBidirectionalModel`<sup>C</sup> | Llama-based with bidirectional attention | `nvidia/llama-nemotron-embed-1b-v2`, etc. | ✅︎ | ✅︎ |
| `LlamaModel`<sup>C</sup>, `LlamaForCausalLM`<sup>C</sup>, `MistralModel`<sup>C</sup>, etc. | Llama-based | `intfloat/e5-mistral-7b-instruct`, etc. | ✅︎ | ✅︎ |
| `Qwen2Model`<sup>C</sup>, `Qwen2ForCausalLM`<sup>C</sup> | Qwen2-based | `ssmits/Qwen2-7B-Instruct-embed-base` (see note), `Alibaba-NLP/gte-Qwen2-7B-instruct` (see note), etc. | ✅︎ | ✅︎ |
@@ -555,7 +555,7 @@ of the whole prompt are extracted from the normalized hidden state corresponding
These models primarily support the [`LLM.classify`](./pooling_models.md#llmclassify) API.
| Architecture | Models | Example HF Models | [LoRA](../features/lora.md) | [PP](../serving/parallelism_scaling.md) |
|--------------|--------|-------------------|----------------------|---------------------------|
| ------------ | ------ | ----------------- | -------------------- | ------------------------- |
| `JambaForSequenceClassification` | Jamba | `ai21labs/Jamba-tiny-reward-dev`, etc. | ✅︎ | ✅︎ |
| `GPT2ForSequenceClassification` | GPT2 | `nie3e/sentiment-polish-gpt2-small` | | |
| `*Model`<sup>C</sup>, `*ForCausalLM`<sup>C</sup>, etc. | Generative models | N/A | \* | \* |
@@ -572,7 +572,7 @@ Cross-encoder and reranker models are a subset of classification models that acc
These models primarily support the [`LLM.score`](./pooling_models.md#llmscore) API.
| Architecture | Models | Example HF Models | Score template (see note) | [LoRA](../features/lora.md) | [PP](../serving/parallelism_scaling.md) |
|--------------|--------|-------------------|---------------------------|-----------------------------|-----------------------------------------|
| ------------ | ------ | ----------------- | ------------------------- | --------------------------- | --------------------------------------- |
| `BertForSequenceClassification` | BERT-based | `cross-encoder/ms-marco-MiniLM-L-6-v2`, etc. | N/A | | |
| `GemmaForSequenceClassification` | Gemma-based | `BAAI/bge-reranker-v2-gemma`(see note), etc. | [bge-reranker-v2-gemma.jinja](../../examples/pooling/score/template/bge-reranker-v2-gemma.jinja) | ✅︎ | ✅︎ |
| `GteNewForSequenceClassification` | mGTE-TRM (see note) | `Alibaba-NLP/gte-multilingual-reranker-base`, etc. | N/A | | |
@@ -622,7 +622,7 @@ These models primarily support the [`LLM.score`](./pooling_models.md#llmscore) A
These models primarily support the [`LLM.reward`](./pooling_models.md#llmreward) API.
| Architecture | Models | Example HF Models | [LoRA](../features/lora.md) | [PP](../serving/parallelism_scaling.md) |
|--------------|--------|-------------------|----------------------|---------------------------|
| ------------ | ------ | ----------------- | -------------------- | ------------------------- |
| `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. | ✅︎ | ✅︎ |
@@ -637,9 +637,9 @@ These models primarily support the [`LLM.reward`](./pooling_models.md#llmreward)
These models primarily support the [`LLM.encode`](./pooling_models.md#llmencode) API.
| Architecture | Models | Example HF Models | [LoRA](../features/lora.md) | [PP](../serving/parallelism_scaling.md) |
|--------------|--------|-------------------|-----------------------------|-----------------------------------------|
| `BertForTokenClassification` | bert-based | `boltuix/NeuroBERT-NER` (see note), etc. | | |
| `ModernBertForTokenClassification` | ModernBERT-based | `disham993/electrical-ner-ModernBERT-base` | | |
| ------------ | ------ | ----------------- | --------------------------- | --------------------------------------- |
| `BertForTokenClassification` | bert-based | `boltuix/NeuroBERT-NER` (see note), etc. | | |
| `ModernBertForTokenClassification` | ModernBERT-based | `disham993/electrical-ner-ModernBERT-base` | | |
!!! note
Named Entity Recognition (NER) usage, please refer to [examples/pooling/token_classify/ner_offline.py](../../examples/pooling/token_classify/ner_offline.py), [examples/pooling/token_classify/ner_online.py](../../examples/pooling/token_classify/ner_online.py).
@@ -678,7 +678,7 @@ See [this page](generative_models.md) for more information on how to use generat
These models primarily accept the [`LLM.generate`](./generative_models.md#llmgenerate) API. Chat/Instruct models additionally support the [`LLM.chat`](./generative_models.md#llmchat) API.
| Architecture | Models | Inputs | Example HF Models | [LoRA](../features/lora.md) | [PP](../serving/parallelism_scaling.md) |
|--------------|--------|--------|-------------------|----------------------|---------------------------|
| ------------ | ------ | ------ | ----------------- | -------------------- | ------------------------- |
| `AriaForConditionalGeneration` | Aria | T + I<sup>+</sup> | `rhymes-ai/Aria` | | |
| `AudioFlamingo3ForConditionalGeneration` | AudioFlamingo3 | T + A | `nvidia/audio-flamingo-3-hf`, `nvidia/music-flamingo-2601-hf` | ✅︎ | ✅︎ |
| `AyaVisionForConditionalGeneration` | Aya Vision | T + I<sup>+</sup> | `CohereLabs/aya-vision-8b`, `CohereLabs/aya-vision-32b`, etc. | | ✅︎ |
@@ -698,7 +698,7 @@ These models primarily accept the [`LLM.generate`](./generative_models.md#llmgen
| `GLM4VForCausalLM`<sup>^</sup> | GLM-4V | T + I | `zai-org/glm-4v-9b`, `zai-org/cogagent-9b-20241220`, etc. | ✅︎ | ✅︎ |
| `Glm4vForConditionalGeneration` | GLM-4.1V-Thinking | T + I<sup>E+</sup> + V<sup>E+</sup> | `zai-org/GLM-4.1V-9B-Thinking`, etc. | ✅︎ | ✅︎ |
| `Glm4vMoeForConditionalGeneration` | GLM-4.5V | T + I<sup>E+</sup> + V<sup>E+</sup> | `zai-org/GLM-4.5V`, etc. | ✅︎ | ✅︎ |
| `GlmOcrForConditionalGeneration` | GLM-OCR | T + I<sup>E+</sup> | `zai-org/GLM-OCR`, etc. | ✅︎ | ✅︎ |
| `GlmOcrForConditionalGeneration` | GLM-OCR | T + I<sup>E+</sup> | `zai-org/GLM-OCR`, etc. | ✅︎ | ✅︎ |
| `GraniteSpeechForConditionalGeneration` | Granite Speech | T + A | `ibm-granite/granite-speech-3.3-8b` | ✅︎ | ✅︎ |
| `HCXVisionForCausalLM` | HyperCLOVAX-SEED-Vision-Instruct-3B | T + I<sup>+</sup> + V<sup>+</sup> | `naver-hyperclovax/HyperCLOVAX-SEED-Vision-Instruct-3B` | | |
| `H2OVLChatModel` | H2OVL | T + I<sup>E+</sup> | `h2oai/h2ovl-mississippi-800m`, `h2oai/h2ovl-mississippi-2b`, etc. | | ✅︎ |
@@ -714,7 +714,7 @@ These models primarily accept the [`LLM.generate`](./generative_models.md#llmgen
| `KeyeVL1_5ForConditionalGeneration` | Keye-VL-1_5-8B | T + I<sup>E+</sup> + V<sup>E+</sup> | `Kwai-Keye/Keye-VL-1_5-8B` | ✅︎ | ✅︎ |
| `KimiVLForConditionalGeneration` | Kimi-VL-A3B-Instruct, Kimi-VL-A3B-Thinking | T + I<sup>+</sup> | `moonshotai/Kimi-VL-A3B-Instruct`, `moonshotai/Kimi-VL-A3B-Thinking` | | ✅︎ |
| `KimiK25ForConditionalGeneration` | Kimi-K2.5 | T + I<sup>+</sup> | `moonshotai/Kimi-K2.5` | | ✅︎ |
| `LightOnOCRForConditionalGeneration` | LightOnOCR-1B | T + I<sup>+</sup> | `lightonai/LightOnOCR-1B`, etc | ✅︎ | ✅︎ |
| `LightOnOCRForConditionalGeneration` | LightOnOCR-1B | T + I<sup>+</sup> | `lightonai/LightOnOCR-1B`, etc | ✅︎ | ✅︎ |
| `Lfm2VlForConditionalGeneration` | LFM2-VL | T + I<sup>+</sup> | `LiquidAI/LFM2-VL-450M`, `LiquidAI/LFM2-VL-3B`, `LiquidAI/LFM2-VL-8B-A1B`, etc. | ✅︎ | ✅︎ |
| `Llama4ForConditionalGeneration` | Llama 4 | T + I<sup>+</sup> | `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. | ✅︎ | ✅︎ |
| `Llama_Nemotron_Nano_VL` | Llama Nemotron Nano VL | T + I<sup>E+</sup> | `nvidia/Llama-3.1-Nemotron-Nano-VL-8B-V1` | ✅︎ | ✅︎ |
@@ -731,7 +731,7 @@ These models primarily accept the [`LLM.generate`](./generative_models.md#llmgen
| `Molmo2ForConditionalGeneration` | Molmo2 | T + I<sup>+</sup> / V | `allenai/Molmo2-4B`, `allenai/Molmo2-8B`, `allenai/Molmo2-O-7B` | ✅︎ | ✅︎ |
| `NVLM_D_Model` | NVLM-D 1.0 | T + I<sup>+</sup> | `nvidia/NVLM-D-72B`, etc. | | ✅︎ |
| `OpenCUAForConditionalGeneration` | OpenCUA-7B | T + I<sup>E+</sup> | `xlangai/OpenCUA-7B` | ✅︎ | ✅︎ |
| `OpenPanguVLForConditionalGeneration` | openpangu-VL | T + I<sup>E+</sup> + V<sup>E+</sup> |`FreedomIntelligence/openPangu-VL-7B` | ✅︎ | ✅︎ |
| `OpenPanguVLForConditionalGeneration` | openpangu-VL | T + I<sup>E+</sup> + V<sup>E+</sup> | `FreedomIntelligence/openPangu-VL-7B` | ✅︎ | ✅︎ |
| `Ovis` | Ovis2, Ovis1.6 | T + I<sup>+</sup> | `AIDC-AI/Ovis2-1B`, `AIDC-AI/Ovis1.6-Llama3.2-3B`, etc. | | ✅︎ |
| `Ovis2_5` | Ovis2.5 | T + I<sup>+</sup> + V | `AIDC-AI/Ovis2.5-9B`, etc. | | |
| `Ovis2_6ForCausalLM` | Ovis2.6 | T + I<sup>+</sup> + V | `AIDC-AI/Ovis2.6-2B`, etc. | | |
@@ -764,7 +764,7 @@ These models primarily accept the [`LLM.generate`](./generative_models.md#llmgen
Some models are supported only via the [Transformers modeling backend](#transformers). The purpose of the table below is to acknowledge models which we officially support in this way. The logs will say that the Transformers modeling backend is being used, and you will see no warning that this is fallback behaviour. This means that, if you have issues with any of the models listed below, please [make an issue](https://github.com/vllm-project/vllm/issues/new/choose) and we'll do our best to fix it!
| Architecture | Models | Inputs | Example HF Models | [LoRA](../features/lora.md) | [PP](../serving/parallelism_scaling.md) |
|--------------|--------|--------|-------------------|-----------------------------|-----------------------------------------|
| ------------ | ------ | ------ | ----------------- | --------------------------- | --------------------------------------- |
| `Emu3ForConditionalGeneration` | Emu3 | T + I | `BAAI/Emu3-Chat-hf` | ✅︎ | ✅︎ |
<sup>^</sup> You need to set the architecture name via `--hf-overrides` to match the one in vLLM.</br>
@@ -795,7 +795,7 @@ Some models are supported only via the [Transformers modeling backend](#transfor
Speech2Text models trained specifically for Automatic Speech Recognition.
| Architecture | Models | Example HF Models | [LoRA](../features/lora.md) | [PP](../serving/parallelism_scaling.md) |
|--------------|--------|-------------------|----------------------|---------------------------|
| ------------ | ------ | ----------------- | -------------------- | ------------------------- |
| `FireRedASR2ForConditionalGeneration` | FireRedASR2 | `allendou/FireRedASR2-LLM-vllm`, etc. | | |
| `FunASRForConditionalGeneration` | FunASR | `allendou/Fun-ASR-Nano-2512-vllm`, etc. | | |
| `Gemma3nForConditionalGeneration` | Gemma3n | `google/gemma-3n-E2B-it`, `google/gemma-3n-E4B-it`, etc. | | |
@@ -823,7 +823,7 @@ These models primarily support the [`LLM.embed`](./pooling_models.md#llmembed) A
The following table lists those that are tested in vLLM.
| Architecture | Models | Inputs | Example HF Models | [LoRA](../features/lora.md) | [PP](../serving/parallelism_scaling.md) |
|--------------|--------|--------|-------------------|----------------------|---------------------------|
| ------------ | ------ | ------ | ----------------- | -------------------- | ------------------------- |
| `CLIPModel` | CLIP | T / I | `openai/clip-vit-base-patch32`, `openai/clip-vit-large-patch14`, etc. | | |
| `ColModernVBertForRetrieval` | ColModernVBERT | T / I | `ModernVBERT/colmodernvbert-merged` | | |
| `LlamaNemotronVLModel` | Llama Nemotron Embedding + SigLIP | T + I | `nvidia/llama-nemotron-embed-vl-1b-v2` | | |
@@ -844,7 +844,7 @@ Cross-encoder and reranker models are a subset of classification models that acc
These models primarily support the [`LLM.score`](./pooling_models.md#llmscore) API.
| Architecture | Models | Inputs | Example HF Models | [LoRA](../features/lora.md) | [PP](../serving/parallelism_scaling.md) |
|--------------|--------|--------|-------------------|----------------------|---------------------------|
| ------------ | ------ | ------ | ----------------- | -------------------- | ------------------------- |
| `JinaVLForSequenceClassification` | JinaVL-based | T + I<sup>E+</sup> | `jinaai/jina-reranker-m0`, etc. | ✅︎ | ✅︎ |
| `LlamaNemotronVLForSequenceClassification` | Llama Nemotron Reranker + SigLIP | T + I<sup>E+</sup> | `nvidia/llama-nemotron-rerank-vl-1b-v2` | | |
| `Qwen3VLForSequenceClassification` | Qwen3-VL-Reranker | T + I<sup>E+</sup> + V<sup>E+</sup> | `Qwen/Qwen3-VL-Reranker-2B`(see note), etc. | ✅︎ | ✅︎ |