[Doc] Update pooling model docs (#22186)

Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>

docs/models/pooling_models.md

@@ -120,7 +120,7 @@ A code example can be found here: <gh-file:examples/offline_inference/basic/clas
 ### `LLM.score`
 
 The [score][vllm.LLM.score] method outputs similarity scores between sentence pairs.
-It is designed for embedding models and cross encoder models. Embedding models use cosine similarity, and [cross-encoder models](https://www.sbert.net/examples/applications/cross-encoder/README.html) serve as rerankers between candidate query-document pairs in RAG systems.
+It is designed for embedding models and cross-encoder models. Embedding models use cosine similarity, and [cross-encoder models](https://www.sbert.net/examples/applications/cross-encoder/README.html) serve as rerankers between candidate query-document pairs in RAG systems.
 
 !!! note
     vLLM can only perform the model inference component (e.g. embedding, reranking) of RAG.

docs/models/supported_models.md

@@ -311,6 +311,8 @@ See [this page](generative_models.md) for more information on how to use generat
 
 #### Text Generation
 
+These models primarily accept the [`LLM.generate`](./generative_models.md#llmgenerate) API. Chat/Instruct models additionally support the [`LLM.chat`](./generative_models.md#llmchat) API.
+
 <style>
 th {
   white-space: nowrap;
@@ -419,7 +421,9 @@ See [this page](./pooling_models.md) for more information on how to use pooling
     Since some model architectures support both generative and pooling tasks,
     you should explicitly specify `--runner pooling` to ensure that the model is used in pooling mode instead of generative mode.
 
-#### Text Embedding
+#### Embedding
+
+These models primarily support the [`LLM.embed`](./pooling_models.md#llmembed) API.
 
 | Architecture | Models | Example HF Models | [LoRA](../features/lora.md) | [PP](../serving/distributed_serving.md) | [V1](gh-issue:8779) |
 |--------------|--------|-------------------|----------------------|---------------------------|---------------------|
@@ -457,28 +461,10 @@ If your model is not in the above list, we will try to automatically convert the
 [as_embedding_model][vllm.model_executor.models.adapters.as_embedding_model]. By default, the embeddings
 of the whole prompt are extracted from the normalized hidden state corresponding to the last token.
 
-#### Reward Modeling
-
-| 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`<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.
-
-!!! important
-    For process-supervised reward models such as `peiyi9979/math-shepherd-mistral-7b-prm`, the pooling config should be set explicitly,
-    e.g.: `--override-pooler-config '{"pooling_type": "STEP", "step_tag_id": 123, "returned_token_ids": [456, 789]}'`.
-
 #### Classification
 
+These models primarily support the [`LLM.classify`](./pooling_models.md#llmclassify) API.
+
 | 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. | ✅︎ | ✅︎ | |
@@ -491,7 +477,10 @@ If your model is not in the above list, we will try to automatically convert the
 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
+#### Cross-encoder / Reranker
+
+Cross-encoder and reranker models are a subset of classification models that accept two prompts as input.
+These models primarily support the [`LLM.score`](./pooling_models.md#llmscore) API.
 
 | Architecture | Models | Example HF Models | [LoRA](../features/lora.md) | [PP](../serving/distributed_serving.md) | [V1](gh-issue:8779) |
 |--------------|--------|-------------------|----------------------|---------------------------|---------------------|
@@ -501,6 +490,7 @@ If your model is not in the above list, we will try to automatically convert the
 | `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. | | | |
+| `*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.
@@ -526,6 +516,28 @@ If your model is not in the above list, we will try to automatically convert the
     vllm serve Qwen/Qwen3-Reranker-0.6B --hf_overrides '{"architectures": ["Qwen3ForSequenceClassification"],"classifier_from_token": ["no", "yes"],"is_original_qwen3_reranker": true}'
     ```
 
+#### Reward Modeling
+
+These models primarily support the [`LLM.reward`](./pooling_models.md#llmreward) API.
+
+| 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`<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.
+
+!!! important
+    For process-supervised reward models such as `peiyi9979/math-shepherd-mistral-7b-prm`, the pooling config should be set explicitly,
+    e.g.: `--override-pooler-config '{"pooling_type": "STEP", "step_tag_id": 123, "returned_token_ids": [456, 789]}'`.
+
 [](){ #supported-mm-models }
 
 ## List of Multimodal Language Models
@@ -579,6 +591,8 @@ See [this page](generative_models.md) for more information on how to use generat
 
 #### Text Generation
 
+These models primarily accept the [`LLM.generate`](./generative_models.md#llmgenerate) API. Chat/Instruct models additionally support the [`LLM.chat`](./generative_models.md#llmchat) API.
+
 | 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` | | | ✅︎ |
@@ -720,11 +734,9 @@ Speech2Text models trained specifically for Automatic Speech Recognition.
 
 See [this page](./pooling_models.md) for more information on how to use pooling models.
 
-!!! important
-    Since some model architectures support both generative and pooling tasks,
-    you should explicitly specify `--runner pooling` to ensure that the model is used in pooling mode instead of generative mode.
+#### Embedding
 
-#### Text Embedding
+These models primarily support the [`LLM.embed`](./pooling_models.md#llmembed) API.
 
 !!! note
     To get the best results, you should use pooling models that are specifically trained as such.
@@ -742,7 +754,10 @@ The following table lists those that are tested in vLLM.
 
 ---
 
-#### Scoring
+#### Cross-encoder / Reranker
+
+Cross-encoder and reranker models are a subset of classification models that accept two prompts as input.
+These models primarily support the [`LLM.score`](./pooling_models.md#llmscore) API.
 
 | Architecture                        | Models             | Inputs   | Example HF Models        | [LoRA][lora-adapter]   | [PP][distributed-serving]   | [V1](gh-issue:8779)   |
 |-------------------------------------|--------------------|----------|--------------------------|------------------------|-----------------------------|-----------------------|