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Unverified Commit 5111642a authored by Isotr0py's avatar Isotr0py Committed by GitHub
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[Doc] Update V1 status for decoder-only embedding models (#19952) 


Signed-off-by: default avatarIsotr0py <2037008807@qq.com>
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docs/models/supported_models.md
View file @ 5111642a
......@@ -407,15 +407,15 @@ Specified using `--task embed`.
| Architecture | Models | Example HF Models | [LoRA][lora-adapter] | [PP][distributed-serving] | [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. | ✅︎ | | |
| `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. | ✅︎ | ✅︎ | |
| `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
......@@ -442,9 +442,10 @@ Specified using `--task reward`.
| Architecture | Models | Example HF Models | [LoRA][lora-adapter] | [PP][distributed-serving] | [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. | ✅︎ | ✅︎ | |
| `Qwen2ForRewardModel` | Qwen2-based | `Qwen/Qwen2.5-Math-RM-72B`, etc. | ✅︎ | ✅︎ | |
| `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][vllm.model_executor.models.adapters.as_reward_model]. By default, we return the hidden states of each token directly.
......@@ -460,7 +461,7 @@ Specified using `--task classify`.
| Architecture | Models | Example HF Models | [LoRA][lora-adapter] | [PP][distributed-serving] | [V1](gh-issue:8779) |
|----------------------------------|----------|----------------------------------------|------------------------|-----------------------------|-----------------------|
| `JambaForSequenceClassification` | Jamba | `ai21labs/Jamba-tiny-reward-dev`, etc. | ✅︎ | ✅︎ | |
| `GPT2ForSequenceClassification` | GPT2 | `nie3e/sentiment-polish-gpt2-small` | | | |
| `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_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.
......@@ -471,7 +472,7 @@ Specified using `--task score`.
| Architecture | Models | Example HF Models | [V1](gh-issue:8779) |
|---------------------------------------|-------------------|--------------------------------------------------------------------------------------|-----------------------|
| `BertForSequenceClassification` | BERT-based | `cross-encoder/ms-marco-MiniLM-L-6-v2`, etc. | |
| `Qwen3ForSequenceClassification` | Qwen3-based | `tomaarsen/Qwen3-Reranker-0.6B-seq-cls`, `Qwen/Qwen3-Reranker-0.6B` (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. | |
......
vllm/model_executor/models/qwen2_rm.py
View file @ 5111642a
......@@ -19,24 +19,12 @@ from vllm.model_executor.layers.pooler import Pooler, PoolingType, SimplePooler
from vllm.model_executor.pooling_metadata import PoolingMetadata
from vllm.sequence import IntermediateTensors, PoolerOutput
from .interfaces import SupportsLoRA, SupportsPP, SupportsV0Only
from .interfaces import SupportsLoRA, SupportsPP
from .qwen2 import Qwen2Model
from .utils import AutoWeightsLoader, maybe_prefix
class ReLU(nn.Module):
def __init__(self):
super().__init__()
self.activation = nn.ReLU()
def forward(self, input):
input, _ = input
return self.activation(input)
class Qwen2RewardBaseModel(nn.Module, SupportsLoRA, SupportsPP,
SupportsV0Only):
class Qwen2RewardBaseModel(nn.Module, SupportsLoRA, SupportsPP):
packed_modules_mapping = {
"qkv_proj": [
"q_proj",
......@@ -65,11 +53,13 @@ class Qwen2RewardBaseModel(nn.Module, SupportsLoRA, SupportsPP,
self.score = nn.Sequential(
ColumnParallelLinear(config.hidden_size,
config.hidden_size,
quant_config=quant_config),
ReLU(),
quant_config=quant_config,
return_bias=False),
nn.ReLU(),
RowParallelLinear(config.hidden_size,
config.num_labels,
quant_config=quant_config),
quant_config=quant_config,
return_bias=False),
)
self._pooler: SimplePooler
self.make_empty_intermediate_tensors = (
......@@ -87,7 +77,7 @@ class Qwen2RewardBaseModel(nn.Module, SupportsLoRA, SupportsPP,
) -> Union[torch.Tensor, IntermediateTensors]:
hidden_states = self.model(input_ids, positions, intermediate_tensors,
inputs_embeds)
logits, _ = self.score(hidden_states)
logits = self.score(hidden_states)
return logits
def pooler(
......
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