diff --git a/docs/models/supported_models.md b/docs/models/supported_models.md
index 1d165fa6f16bd..7908e42387100 100644
--- a/docs/models/supported_models.md
+++ b/docs/models/supported_models.md
@@ -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))  
diff --git a/tests/models/language/pooling/test_gritlm.py b/tests/models/language/pooling/test_gritlm.py
index d21987571cbaa..17a55d916b1ff 100644
--- a/tests/models/language/pooling/test_gritlm.py
+++ b/tests/models/language/pooling/test_gritlm.py
@@ -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):
diff --git a/vllm/model_executor/models/gritlm.py b/vllm/model_executor/models/gritlm.py
index 9e7490e3c4f07..3f6790269ae62 100644
--- a/vllm/model_executor/models/gritlm.py
+++ b/vllm/model_executor/models/gritlm.py
@@ -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
diff --git a/vllm/model_executor/models/interfaces.py b/vllm/model_executor/models/interfaces.py
index c425488f834b5..9415e67924e74 100644
--- a/vllm/model_executor/models/interfaces.py
+++ b/vllm/model_executor/models/interfaces.py
@@ -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