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[Doc] Update V1 status of various pooling models (#23189)

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Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>
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26
docs/models/supported_models.md
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@ -363,7 +363,7 @@ th {
| `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`. | ✅︎ | ✅︎ | |
| `GritLM` | GritLM | `parasail-ai/GritLM-7B-vllm`. | ✅︎ | ✅︎ | ✅︎ |
| `Grok1ModelForCausalLM` | Grok1 | `hpcai-tech/grok-1`. | ✅︎ | ✅︎ | ✅︎ |
| `HunYuanDenseV1ForCausalLM` | Hunyuan-7B-Instruct-0124 | `tencent/Hunyuan-7B-Instruct-0124` | ✅︎ | | ✅︎ |
| `HunYuanMoEV1ForCausalLM` | Hunyuan-80B-A13B | `tencent/Hunyuan-A13B-Instruct`, `tencent/Hunyuan-A13B-Pretrain`, `tencent/Hunyuan-A13B-Instruct-FP8`, etc. | ✅︎ | | ✅︎ |
@ -436,17 +436,17 @@ These models primarily support the [`LLM.embed`](./pooling_models.md#llmembed) A
| Architecture | Models | Example HF Models | [LoRA](../features/lora.md) | [PP](../serving/parallelism_scaling.md) | [V1](gh-issue:8779) |
|--------------|--------|-------------------|----------------------|---------------------------|---------------------|
| `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`<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. | | | |
| `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`<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. | | | |
| `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))
@ -476,7 +476,7 @@ These models primarily support the [`LLM.classify`](./pooling_models.md#llmclass
| Architecture | Models | Example HF Models | [LoRA](../features/lora.md) | [PP](../serving/parallelism_scaling.md) | [V1](gh-issue:8779) |
|--------------|--------|-------------------|----------------------|---------------------------|---------------------|
| `JambaForSequenceClassification` | Jamba | `ai21labs/Jamba-tiny-reward-dev`, etc. | ✅︎ | ✅︎ | |
| `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 | \* | \* | \* |
@ -493,12 +493,12 @@ These models primarily support the [`LLM.score`](./pooling_models.md#llmscore) A
| Architecture | Models | Example HF Models | [LoRA](../features/lora.md) | [PP](../serving/parallelism_scaling.md) | [V1](gh-issue:8779) |
|--------------|--------|-------------------|----------------------|---------------------------|---------------------|
| `BertForSequenceClassification` | BERT-based | `cross-encoder/ms-marco-MiniLM-L-6-v2`, etc. | | | |
| `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. | | | |
| `RobertaForSequenceClassification` | RoBERTa-based | `cross-encoder/quora-roberta-base`, etc. | | | ✅︎ |
| `XLMRobertaForSequenceClassification` | XLM-RoBERTa-based | `BAAI/bge-reranker-v2-m3`, etc. | | | ✅︎ |
| `*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))

9
tests/models/language/pooling/test_gritlm.py
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@ -14,6 +14,7 @@ from ....utils import RemoteOpenAIServer
MODEL_NAME = "parasail-ai/GritLM-7B-vllm"
MAX_MODEL_LEN = 4000
ATOL = 0.002
def _arr(arr):
@ -97,16 +98,16 @@ def get_test_data():
def validate_embed_output(q_rep: list[list[float]], d_rep: list[list[float]]):
cosine_sim_q0_d0 = 1 - cosine(q_rep[0], d_rep[0])
assert cosine_sim_q0_d0 == pytest.approx(0.609, abs=0.001)
assert cosine_sim_q0_d0 == pytest.approx(0.609, abs=ATOL)
cosine_sim_q0_d1 = 1 - cosine(q_rep[0], d_rep[1])
assert cosine_sim_q0_d1 == pytest.approx(0.101, abs=0.001)
assert cosine_sim_q0_d1 == pytest.approx(0.101, abs=ATOL)
cosine_sim_q1_d0 = 1 - cosine(q_rep[1], d_rep[0])
assert cosine_sim_q1_d0 == pytest.approx(0.120, abs=0.001)
assert cosine_sim_q1_d0 == pytest.approx(0.120, abs=ATOL)
cosine_sim_q1_d1 = 1 - cosine(q_rep[1], d_rep[1])
assert cosine_sim_q1_d1 == pytest.approx(0.534, abs=0.001)
assert cosine_sim_q1_d1 == pytest.approx(0.534, abs=ATOL)
def test_gritlm_offline_embedding(vllm_runner):

6
vllm/model_executor/models/gritlm.py
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@ -20,7 +20,7 @@ from vllm.sequence import PoolerOutput
from vllm.tasks import PoolingTask
from vllm.transformers_utils.tokenizer import cached_tokenizer_from_config
from .interfaces import SupportsV0Only
from .interfaces import default_pooling_type
logger = init_logger(__name__)
@ -215,7 +215,8 @@ class GritLMPooler(Pooler):
return build_output(pooled_data)
class GritLM(LlamaForCausalLM, SupportsV0Only):
@default_pooling_type("MEAN")
class GritLM(LlamaForCausalLM):
"""This class implements the embedding model for parasail-ai/GritLM-7B-vllm.
The class inherits from LlamaForCausalLM and provides a custom pooling
@ -241,7 +242,6 @@ class GritLM(LlamaForCausalLM, SupportsV0Only):
prefix: str = "",
**kwargs,
) -> None:
# Use full attention for pooling (this is why V1 is not supported yet)
if vllm_config.model_config.runner_type == "pooling":
hf_config = vllm_config.model_config.hf_config
hf_config.is_causal = False

11
vllm/model_executor/models/interfaces.py
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@ -3,7 +3,7 @@
from collections.abc import Iterable, Mapping, MutableSequence
from typing import (TYPE_CHECKING, ClassVar, Literal, Optional, Protocol,
Union, overload, runtime_checkable)
TypeVar, Union, overload, runtime_checkable)
import numpy as np
import torch
@ -641,11 +641,14 @@ def supports_cross_encoding(
return is_pooling_model(model) and _supports_cross_encoding(model)
def default_pooling_type(pooling_type: str) -> object:
_T = TypeVar("_T", bound=type[torch.nn.Module])
def default_pooling_type(pooling_type: str):
"""Set default_pooling_type decorator. """
def func(model: object):
model.default_pooling_type = pooling_type
def func(model: _T) -> _T:
model.default_pooling_type = pooling_type # type: ignore
return model
return func