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[Deprecation][2/N] Replace --task with --runner and --convert (#21470)

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Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>
Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>
Co-authored-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>
This commit is contained in:
Cyrus Leung
2025-07-28 10:42:40 +08:00
committed by GitHub
parent 8f605ee309
commit 86ae693f20
94 changed files with 1117 additions and 1083 deletions
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101
docs/models/supported_models.md
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@@ -1,7 +1,6 @@
# Supported Models
vLLM supports [generative](./generative_models.md) and [pooling](./pooling_models.md) 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.
@@ -24,7 +23,7 @@ To check if the modeling backend is Transformers, you can simply do this:
```python
from vllm import LLM
llm = LLM(model=..., task="generate") # Name or path of your model
llm = LLM(model=...) # Name or path of your model
llm.apply_model(lambda model: print(type(model)))
```
@@ -158,13 +157,13 @@ The [Transformers backend][transformers-backend] enables you to run models direc
```python
from vllm import LLM
# For generative models (task=generate) only
llm = LLM(model=..., task="generate") # Name or path of your model
# For generative models (runner=generate) only
llm = LLM(model=..., runner="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
# For pooling models (runner=pooling) only
llm = LLM(model=..., runner="pooling") # Name or path of your model
output = llm.encode("Hello, my name is")
print(output)
```
@@ -281,13 +280,13 @@ And use with `trust_remote_code=True`.
```python
from vllm import LLM
llm = LLM(model=..., revision=..., task=..., trust_remote_code=True)
llm = LLM(model=..., revision=..., runner=..., trust_remote_code=True)
# For generative models (task=generate) only
# For generative models (runner=generate) only
output = llm.generate("Hello, my name is")
print(output)
# For pooling models (task={embed,classify,reward,score}) only
# For pooling models (runner=pooling) only
output = llm.encode("Hello, my name is")
print(output)
```
@@ -312,8 +311,6 @@ See [this page](generative_models.md) for more information on how to use generat
#### Text Generation
Specified using `--task generate`.
<style>
th {
white-space: nowrap;
@@ -420,25 +417,27 @@ See [this page](./pooling_models.md) for more information on how to use pooling
!!! 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.
you should explicitly specify `--runner pooling` 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](../features/lora.md) | [PP](../serving/distributed_serving.md) | [V1](gh-issue:8779) |
|--------------|--------|-------------------|----------------------|---------------------------|---------------------|
| `BertModel` | BERT-based | `BAAI/bge-base-en-v1.5`, `Snowflake/snowflake-arctic-embed-xs`, etc. | | | |
| `Gemma2Model` | Gemma 2-based | `BAAI/bge-multilingual-gemma2`, etc. | ✅︎ | | ✅︎ |
| `BertModel`<sup>C</sup> | BERT-based | `BAAI/bge-base-en-v1.5`, `Snowflake/snowflake-arctic-embed-xs`, etc. | | | |
| `Gemma2Model`<sup>C</sup> | 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. | ✅︎ | ✅︎ | ✅︎ |
| `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. | | | |
| `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. | ✅︎ | ✅︎ | ✅︎ |
| `Qwen3Model`<sup>C</sup>, `Qwen3ForCausalLM`<sup>C</sup> | Qwen3-based | `Qwen/Qwen3-Embedding-0.6B`, etc. | ✅︎ | ✅︎ | ✅︎ |
| `RobertaModel`, `RobertaForMaskedLM` | RoBERTa-based | `sentence-transformers/all-roberta-large-v1`, etc. | | | |
| `*Model`<sup>C</sup>, `*ForCausalLM`<sup>C</sup>, etc. | Generative models | N/A | \* | \* | \* |
<sup>C</sup> Automatically converted into an embedding model via `--convert embed`. ([details](./pooling_models.md#model-conversion))
\* Feature support is the same as that of the original model.
!!! note
`ssmits/Qwen2-7B-Instruct-embed-base` has an improperly defined Sentence Transformers config.
@@ -460,14 +459,16 @@ of the whole prompt are extracted from the normalized hidden state corresponding
#### Reward Modeling
Specified using `--task reward`.
| Architecture | Models | Example HF Models | [LoRA](../features/lora.md) | [PP](../serving/distributed_serving.md) | [V1](gh-issue:8779) |
|--------------|--------|-------------------|----------------------|---------------------------|---------------------|
| `InternLM2ForRewardModel` | InternLM2-based | `internlm/internlm2-1_8b-reward`, `internlm/internlm2-7b-reward`, etc. | ✅︎ | ✅︎ | ✅︎ |
| `LlamaForCausalLM` | Llama-based | `peiyi9979/math-shepherd-mistral-7b-prm`, etc. | ✅︎ | ✅︎ | ✅︎ |
| `LlamaForCausalLM`<sup>C</sup> | 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. | ✅︎ | ✅︎ | ✅︎ |
| `*Model`<sup>C</sup>, `*ForCausalLM`<sup>C</sup>, etc. | Generative models | N/A | \* | \* | \* |
<sup>C</sup> Automatically converted into a reward model via `--convert reward`. ([details](./pooling_models.md#model-conversion))
\* Feature support is the same as that of the original model.
If your model is not in the above list, we will try to automatically convert the model using
[as_reward_model][vllm.model_executor.models.adapters.as_reward_model]. By default, we return the hidden states of each token directly.
@@ -478,28 +479,31 @@ If your model is not in the above list, we will try to automatically convert the
#### Classification
Specified using `--task classify`.
| Architecture | Models | Example HF Models | [LoRA](../features/lora.md) | [PP](../serving/distributed_serving.md) | [V1](gh-issue:8779) |
|--------------|--------|-------------------|----------------------|---------------------------|---------------------|
| `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 | \* | \* | \* |
<sup>C</sup> Automatically converted into a classification model via `--convert classify`. ([details](./pooling_models.md#model-conversion))
\* Feature support is the same as that of the original model.
If your model is not in the above list, we will try to automatically convert the model using
[as_seq_cls_model][vllm.model_executor.models.adapters.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 | [LoRA](../features/lora.md) | [PP](../serving/distributed_serving.md) | [V1](gh-issue:8779) |
|--------------|--------|-------------------|----------------------|---------------------------|---------------------|
| `BertForSequenceClassification` | BERT-based | `cross-encoder/ms-marco-MiniLM-L-6-v2`, etc. | | | |
| `GemmaForSequenceClassification` | Gemma-based | `BAAI/bge-reranker-v2-gemma` (see note), 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. | | | |
| Architecture | Models | Example HF Models | [V1](gh-issue:8779) |
|--------------|--------|-------------------|---------------------|
| `BertForSequenceClassification` | BERT-based | `cross-encoder/ms-marco-MiniLM-L-6-v2`, etc. | |
| `GemmaForSequenceClassification` | Gemma-based | `BAAI/bge-reranker-v2-gemma` (see note), 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. | |
<sup>C</sup> Automatically converted into a classification model via `--convert classify`. ([details](./pooling_models.md#model-conversion))
\* Feature support is the same as that of the original model.
!!! note
Load the official original `BAAI/bge-reranker-v2-gemma` by using the following command.
@@ -575,8 +579,6 @@ See [this page](generative_models.md) for more information on how to use generat
#### Text Generation
Specified using `--task generate`.
| Architecture | Models | Inputs | Example HF Models | [LoRA](../features/lora.md) | [PP](../serving/distributed_serving.md) | [V1](gh-issue:8779) |
|--------------|--------|--------|-------------------|----------------------|---------------------------|---------------------|
| `AriaForConditionalGeneration` | Aria | T + I<sup>+</sup> | `rhymes-ai/Aria` | | | ✅︎ |
@@ -705,8 +707,6 @@ Some models are supported only via the [Transformers backend](#transformers). Th
#### Transcription
Specified using `--task transcription`.
Speech2Text models trained specifically for Automatic Speech Recognition.
| Architecture | Models | Example HF Models | [LoRA](../features/lora.md) | [PP](../serving/distributed_serving.md) | [V1](gh-issue:8779) |
@@ -719,14 +719,10 @@ See [this page](./pooling_models.md) for more information on how to use pooling
!!! 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.
you should explicitly specify `--runner pooling` 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.
@@ -734,19 +730,24 @@ The following table lists those that are tested in vLLM.
| Architecture | Models | Inputs | Example HF Models | [LoRA](../features/lora.md) | [PP](../serving/distributed_serving.md) | [V1](gh-issue:8779) |
|--------------|--------|--------|-------------------|----------------------|---------------------------|---------------------|
| `LlavaNextForConditionalGeneration` | LLaVA-NeXT-based | T / I | `royokong/e5-v` | | | |
| `Phi3VForCausalLM` | Phi-3-Vision-based | T + I | `TIGER-Lab/VLM2Vec-Full` | 🚧 | ✅︎ | |
| `LlavaNextForConditionalGeneration`<sup>C</sup> | LLaVA-NeXT-based | T / I | `royokong/e5-v` | | | |
| `Phi3VForCausalLM`<sup>C</sup> | Phi-3-Vision-based | T + I | `TIGER-Lab/VLM2Vec-Full` | 🚧 | ✅︎ | |
| `*ForConditionalGeneration`<sup>C</sup>, `*ForCausalLM`<sup>C</sup>, etc. | Generative models | \* | N/A | \* | \* | \* |
<sup>C</sup> Automatically converted into an embedding model via `--convert embed`. ([details](./pooling_models.md#model-conversion))
\* Feature support is the same as that of the original model.
---
#### Scoring
Specified using `--task score`.
| Architecture | Models | Inputs | Example HF Models | [LoRA][lora-adapter] | [PP][distributed-serving] | [V1](gh-issue:8779) |
|-------------------------------------|--------------------|----------|--------------------------|------------------------|-----------------------------|-----------------------|
| `JinaVLForSequenceClassification` | JinaVL-based | T + I<sup>E+</sup> | `jinaai/jina-reranker-m0`, etc. | | | ✅︎ |
<sup>C</sup> Automatically converted into a classification model via `--convert classify`. ([details](./pooling_models.md#model-conversion))
\* Feature support is the same as that of the original model.
## 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: