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Make right sidebar more readable in "Supported Models" (#17723)

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Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>
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docs/source/models/supported_models.md
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@ -239,7 +239,9 @@ print(output)
See [this page](#generative-models) for more information on how to use generative models.
#### Text Generation (`--task generate`)
#### Text Generation
Specified using `--task generate`.
:::{list-table}
:widths: 25 25 50 5 5
@ -605,7 +607,9 @@ Since some model architectures support both generative and pooling tasks,
you should explicitly specify the task type to ensure that the model is used in pooling mode instead of generative mode.
:::
#### Text Embedding (`--task embed`)
#### Text Embedding
Specified using `--task embed`.
:::{list-table}
:widths: 25 25 50 5 5
@ -670,7 +674,9 @@ If your model is not in the above list, we will try to automatically convert the
{func}`~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 (`--task reward`)
#### Reward Modeling
Specified using `--task reward`.
:::{list-table}
:widths: 25 25 50 5 5
@ -711,7 +717,9 @@ For process-supervised reward models such as `peiyi9979/math-shepherd-mistral-7b
e.g.: `--override-pooler-config '{"pooling_type": "STEP", "step_tag_id": 123, "returned_token_ids": [456, 789]}'`.
:::
#### Classification (`--task classify`)
#### Classification
Specified using `--task classify`.
:::{list-table}
:widths: 25 25 50 5 5
@ -737,7 +745,9 @@ e.g.: `--override-pooler-config '{"pooling_type": "STEP", "step_tag_id": 123, "r
If your model is not in the above list, we will try to automatically convert the model using
{func}`~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.
#### Sentence Pair Scoring (`--task score`)
#### Sentence Pair Scoring
Specified using `--task score`.
:::{list-table}
:widths: 25 25 50 5 5
@ -824,7 +834,9 @@ vLLM currently only supports adding LoRA to the language backbone of multimodal
See [this page](#generative-models) for more information on how to use generative models.
#### Text Generation (`--task generate`)
#### Text Generation
Specified using `--task generate`.
:::{list-table}
:widths: 25 25 15 20 5 5 5
@ -1200,7 +1212,9 @@ Since some model architectures support both generative and pooling tasks,
you should explicitly specify the task type to ensure that the model is used in pooling mode instead of generative mode.
:::
#### Text Embedding (`--task embed`)
#### Text Embedding
Specified using `--task embed`.
Any text generation model can be converted into an embedding model by passing `--task embed`.
@ -1240,7 +1254,9 @@ The following table lists those that are tested in vLLM.
* ✅︎
:::
#### Transcription (`--task transcription`)
#### Transcription
Specified using `--task transcription`.
Speech2Text models trained specifically for Automatic Speech Recognition.