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[Doc] Update V1 status for decoder-only embedding models (#19952)
Signed-off-by: Isotr0py <2037008807@qq.com>
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@ -407,15 +407,15 @@ Specified using `--task embed`.
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| Architecture | Models | Example HF Models | [LoRA][lora-adapter] | [PP][distributed-serving] | [V1](gh-issue:8779) |
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|--------------------------------------------------------|---------------------|---------------------------------------------------------------------------------------------------------------------|----------------------|---------------------------|-----------------------|
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| `BertModel` | BERT-based | `BAAI/bge-base-en-v1.5`, `Snowflake/snowflake-arctic-embed-xs`, etc. | | | |
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| `Gemma2Model` | Gemma 2-based | `BAAI/bge-multilingual-gemma2`, etc. | ✅︎ | | |
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| `Gemma2Model` | Gemma 2-based | `BAAI/bge-multilingual-gemma2`, etc. | ✅︎ | | ✅︎ |
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| `GritLM` | GritLM | `parasail-ai/GritLM-7B-vllm`. | ✅︎ | ✅︎ | |
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| `GteModel` | Arctic-Embed-2.0-M | `Snowflake/snowflake-arctic-embed-m-v2.0`. | ︎ | | |
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| `GteNewModel` | mGTE-TRM (see note) | `Alibaba-NLP/gte-multilingual-base`, etc. | ︎ | ︎ | |
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| `ModernBertModel` | ModernBERT-based | `Alibaba-NLP/gte-modernbert-base`, etc. | ︎ | ︎ | |
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| `NomicBertModel` | Nomic BERT | `nomic-ai/nomic-embed-text-v1`, `nomic-ai/nomic-embed-text-v2-moe`, `Snowflake/snowflake-arctic-embed-m-long`, etc. | ︎ | ︎ | |
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| `LlamaModel`, `LlamaForCausalLM`, `MistralModel`, etc. | Llama-based | `intfloat/e5-mistral-7b-instruct`, etc. | ✅︎ | ✅︎ | |
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| `Qwen2Model`, `Qwen2ForCausalLM` | Qwen2-based | `ssmits/Qwen2-7B-Instruct-embed-base` (see note), `Alibaba-NLP/gte-Qwen2-7B-instruct` (see note), etc. | ✅︎ | ✅︎ | |
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| `Qwen3Model`, `Qwen3ForCausalLM` | Qwen3-based | `Qwen/Qwen3-Embedding-0.6B`, etc. | ✅︎ | ✅︎ | |
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| `LlamaModel`, `LlamaForCausalLM`, `MistralModel`, etc. | Llama-based | `intfloat/e5-mistral-7b-instruct`, etc. | ✅︎ | ✅︎ | ✅︎ |
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| `Qwen2Model`, `Qwen2ForCausalLM` | Qwen2-based | `ssmits/Qwen2-7B-Instruct-embed-base` (see note), `Alibaba-NLP/gte-Qwen2-7B-instruct` (see note), etc. | ✅︎ | ✅︎ | ✅︎ |
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| `Qwen3Model`, `Qwen3ForCausalLM` | Qwen3-based | `Qwen/Qwen3-Embedding-0.6B`, etc. | ✅︎ | ✅︎ | ✅︎ |
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| `RobertaModel`, `RobertaForMaskedLM` | RoBERTa-based | `sentence-transformers/all-roberta-large-v1`, etc. | | | |
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!!! note
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@ -442,9 +442,10 @@ Specified using `--task reward`.
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| Architecture | Models | Example HF Models | [LoRA][lora-adapter] | [PP][distributed-serving] | [V1](gh-issue:8779) |
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|---------------------------|-----------------|------------------------------------------------------------------------|------------------------|-----------------------------|-----------------------|
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| `InternLM2ForRewardModel` | InternLM2-based | `internlm/internlm2-1_8b-reward`, `internlm/internlm2-7b-reward`, etc. | ✅︎ | ✅︎ | |
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| `LlamaForCausalLM` | Llama-based | `peiyi9979/math-shepherd-mistral-7b-prm`, etc. | ✅︎ | ✅︎ | |
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| `Qwen2ForRewardModel` | Qwen2-based | `Qwen/Qwen2.5-Math-RM-72B`, etc. | ✅︎ | ✅︎ | |
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| `InternLM2ForRewardModel` | InternLM2-based | `internlm/internlm2-1_8b-reward`, `internlm/internlm2-7b-reward`, etc. | ✅︎ | ✅︎ | ✅︎ |
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| `LlamaForCausalLM` | Llama-based | `peiyi9979/math-shepherd-mistral-7b-prm`, etc. | ✅︎ | ✅︎ | ✅︎ |
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| `Qwen2ForRewardModel` | Qwen2-based | `Qwen/Qwen2.5-Math-RM-72B`, etc. | ✅︎ | ✅︎ | ✅︎ |
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| `Qwen2ForProcessRewardModel` | Qwen2-based | `Qwen/Qwen2.5-Math-PRM-7B`, etc. | ✅︎ | ✅︎ | ✅︎ |
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If your model is not in the above list, we will try to automatically convert the model using
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[as_reward_model][vllm.model_executor.models.adapters.as_reward_model]. By default, we return the hidden states of each token directly.
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@ -460,7 +461,7 @@ Specified using `--task classify`.
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| Architecture | Models | Example HF Models | [LoRA][lora-adapter] | [PP][distributed-serving] | [V1](gh-issue:8779) |
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|----------------------------------|----------|----------------------------------------|------------------------|-----------------------------|-----------------------|
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| `JambaForSequenceClassification` | Jamba | `ai21labs/Jamba-tiny-reward-dev`, etc. | ✅︎ | ✅︎ | |
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| `GPT2ForSequenceClassification` | GPT2 | `nie3e/sentiment-polish-gpt2-small` | | | |
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| `GPT2ForSequenceClassification` | GPT2 | `nie3e/sentiment-polish-gpt2-small` | | | ✅︎ |
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If your model is not in the above list, we will try to automatically convert the model using
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[as_classification_model][vllm.model_executor.models.adapters.as_classification_model]. By default, the class probabilities are extracted from the softmaxed hidden state corresponding to the last token.
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@ -471,7 +472,7 @@ Specified using `--task score`.
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| Architecture | Models | Example HF Models | [V1](gh-issue:8779) |
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|---------------------------------------|-------------------|--------------------------------------------------------------------------------------|-----------------------|
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| `BertForSequenceClassification` | BERT-based | `cross-encoder/ms-marco-MiniLM-L-6-v2`, etc. | |
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| `Qwen3ForSequenceClassification` | Qwen3-based | `tomaarsen/Qwen3-Reranker-0.6B-seq-cls`, `Qwen/Qwen3-Reranker-0.6B` (see note), etc. | |
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| `Qwen3ForSequenceClassification` | Qwen3-based | `tomaarsen/Qwen3-Reranker-0.6B-seq-cls`, `Qwen/Qwen3-Reranker-0.6B` (see note), etc. | ✅︎ |
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| `RobertaForSequenceClassification` | RoBERTa-based | `cross-encoder/quora-roberta-base`, etc. | |
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| `XLMRobertaForSequenceClassification` | XLM-RoBERTa-based | `BAAI/bge-reranker-v2-m3`, etc. | |
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@ -19,24 +19,12 @@ from vllm.model_executor.layers.pooler import Pooler, PoolingType, SimplePooler
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from vllm.model_executor.pooling_metadata import PoolingMetadata
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from vllm.sequence import IntermediateTensors, PoolerOutput
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from .interfaces import SupportsLoRA, SupportsPP, SupportsV0Only
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from .interfaces import SupportsLoRA, SupportsPP
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from .qwen2 import Qwen2Model
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from .utils import AutoWeightsLoader, maybe_prefix
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class ReLU(nn.Module):
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def __init__(self):
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super().__init__()
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self.activation = nn.ReLU()
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def forward(self, input):
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input, _ = input
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return self.activation(input)
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class Qwen2RewardBaseModel(nn.Module, SupportsLoRA, SupportsPP,
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SupportsV0Only):
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class Qwen2RewardBaseModel(nn.Module, SupportsLoRA, SupportsPP):
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packed_modules_mapping = {
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"qkv_proj": [
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"q_proj",
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@ -65,11 +53,13 @@ class Qwen2RewardBaseModel(nn.Module, SupportsLoRA, SupportsPP,
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self.score = nn.Sequential(
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ColumnParallelLinear(config.hidden_size,
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config.hidden_size,
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quant_config=quant_config),
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ReLU(),
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quant_config=quant_config,
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return_bias=False),
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nn.ReLU(),
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RowParallelLinear(config.hidden_size,
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config.num_labels,
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quant_config=quant_config),
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quant_config=quant_config,
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return_bias=False),
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)
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self._pooler: SimplePooler
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self.make_empty_intermediate_tensors = (
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@ -87,7 +77,7 @@ class Qwen2RewardBaseModel(nn.Module, SupportsLoRA, SupportsPP,
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) -> Union[torch.Tensor, IntermediateTensors]:
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hidden_states = self.model(input_ids, positions, intermediate_tensors,
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inputs_embeds)
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logits, _ = self.score(hidden_states)
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logits = self.score(hidden_states)
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return logits
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def pooler(
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