[Doc] Show default pooling method in a table (#11904)

Signed-off-by: DarkLight1337 <[email protected]>

docs/source/models/generative_models.md

@@ -8,14 +8,14 @@ In vLLM, generative models implement the {class}`~vllm.model_executor.models.Vll
 Based on the final hidden states of the input, these models output log probabilities of the tokens to generate,
 which are then passed through {class}`~vllm.model_executor.layers.Sampler` to obtain the final text.
 
+For generative models, the only supported `--task` option is `"generate"`.
+Usually, this is automatically inferred so you don't have to specify it.
+
 ## Offline Inference
 
 The {class}`~vllm.LLM` class provides various methods for offline inference.
 See [Engine Arguments](#engine-args) for a list of options when initializing the model.
 
-For generative models, the only supported {code}`task` option is {code}`"generate"`.
-Usually, this is automatically inferred so you don't have to specify it.
-
 ### `LLM.generate`
 
 The {class}`~vllm.LLM.generate` method is available to all generative models in vLLM.
@@ -33,7 +33,7 @@ for output in outputs:
 ```
 
 You can optionally control the language generation by passing {class}`~vllm.SamplingParams`.
-For example, you can use greedy sampling by setting {code}`temperature=0`:
+For example, you can use greedy sampling by setting `temperature=0`:
 
 ```python
 llm = LLM(model="facebook/opt-125m")

docs/source/models/pooling_models.md

@@ -14,30 +14,53 @@ As shown in the [Compatibility Matrix](#compatibility-matrix), most vLLM feature
 pooling models as they only work on the generation or decode stage, so performance may not improve as much.
 ```
 
-## Offline Inference
-
-The {class}`~vllm.LLM` class provides various methods for offline inference.
-See [Engine Arguments](#engine-args) for a list of options when initializing the model.
-
-For pooling models, we support the following {code}`task` options:
-
-- Embedding ({code}`"embed"` / {code}`"embedding"`)
-- Classification ({code}`"classify"`)
-- Sentence Pair Scoring ({code}`"score"`)
-- Reward Modeling ({code}`"reward"`)
+For pooling models, we support the following `--task` options.
+The selected option sets the default pooler used to extract the final hidden states:
+
+```{list-table}
+:widths: 50 25 25 25
+:header-rows: 1
+
+* - Task
+  - Pooling Type
+  - Normalization
+  - Softmax
+* - Embedding (`embed`)
+  - `LAST`
+  - ✅︎
+  - ✗
+* - Classification (`classify`)
+  - `LAST`
+  - ✗
+  - ✅︎
+* - Sentence Pair Scoring (`score`)
+  - \*
+  - \*
+  - \*
+* - Reward Modeling (`reward`)
+  - `ALL`
+  - ✗
+  - ✗
+```
 
-The selected task determines the default {class}`~vllm.model_executor.layers.Pooler` that is used:
+\*The default pooler is always defined by the model.
 
-- Embedding: Extract only the hidden states corresponding to the last token, and apply normalization.
-- Classification: Extract only the hidden states corresponding to the last token, and apply softmax.
-- Sentence Pair Scoring: Extract only the hidden states corresponding to the last token, and apply softmax.
-- Reward Modeling: Extract all of the hidden states and return them directly.
+```{note}
+If the model's implementation in vLLM defines its own pooler, the default pooler is set to that instead of the one specified in this table.
+```
 
 When loading [Sentence Transformers](https://huggingface.co/sentence-transformers) models,
-we attempt to override the default pooler based on its Sentence Transformers configuration file ({code}`modules.json`).
+we attempt to override the default pooler based on its Sentence Transformers configuration file (`modules.json`).
 
-You can customize the model's pooling method via the {code}`override_pooler_config` option,
+```{tip}
+You can customize the model's pooling method via the `--override-pooler-config` option,
 which takes priority over both the model's and Sentence Transformers's defaults.
+```
+
+## Offline Inference
+
+The {class}`~vllm.LLM` class provides various methods for offline inference.
+See [Engine Arguments](#engine-args) for a list of options when initializing the model.
 
 ### `LLM.encode`