[Deprecation][2/N] Replace --task with --runner and --convert

docs/features/multimodal_inputs.md

@@ -279,7 +279,7 @@ Here is a simple example using Phi-3.5-Vision.
 First, launch the OpenAI-compatible server:
 
 ```bash
-vllm serve microsoft/Phi-3.5-vision-instruct --task generate \
+vllm serve microsoft/Phi-3.5-vision-instruct --runner generate \
   --trust-remote-code --max-model-len 4096 --limit-mm-per-prompt '{"image":2}'
 ```
 
@@ -358,7 +358,7 @@ Instead of `image_url`, you can pass a video file via `video_url`. Here is a sim
 First, launch the OpenAI-compatible server:
 
 ```bash
-vllm serve llava-hf/llava-onevision-qwen2-0.5b-ov-hf --task generate --max-model-len 8192
+vllm serve llava-hf/llava-onevision-qwen2-0.5b-ov-hf --runner generate --max-model-len 8192
 ```
 
 Then, you can use the OpenAI client as follows:

docs/features/prompt_embeds.md

@@ -34,7 +34,7 @@ Prompt embeddings are passed in as base64 encoded torch tensors.
 First, launch the OpenAI-compatible server:
 
 ```bash
-vllm serve meta-llama/Llama-3.2-1B-Instruct --task generate \
+vllm serve meta-llama/Llama-3.2-1B-Instruct --runner generate \
   --max-model-len 4096 --enable-prompt-embeds
 ```
 

docs/models/generative_models.md

@@ -6,8 +6,8 @@ In vLLM, generative models implement the [VllmModelForTextGeneration][vllm.model
 Based on the final hidden states of the input, these models output log probabilities of the tokens to generate,
 which are then passed through [Sampler][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.
+For model architectures that support both generation and pooling, you should set `--runner generate`
+to use the model as a generative model.
 
 ## Offline Inference
 

docs/models/pooling_models.md

@@ -3,22 +3,25 @@
 vLLM also supports pooling models, including embedding, reranking and reward models.
 
 In vLLM, pooling models implement the [VllmModelForPooling][vllm.model_executor.models.VllmModelForPooling] interface.
-These models use a [Pooler][vllm.model_executor.layers.Pooler] to extract the final hidden states of the input
+These models use a [Pooler][vllm.model_executor.layers.pooler.Pooler] to extract the final hidden states of the input
 before returning them.
 
 !!! note
     We currently support pooling models primarily as a matter of convenience.
     As shown in the [Compatibility Matrix](../features/compatibility_matrix.md), most vLLM features are not applicable to
     pooling models as they only work on the generation or decode stage, so performance may not improve as much.
 
-If the model doesn't implement this interface, you can set `--task` which tells vLLM
+If the model doesn't implement this interface, you can set `--convert` which tells vLLM
 to convert the model into a pooling model.
 
-| `--task`   | Model type           | Supported pooling tasks       |
-|------------|----------------------|-------------------------------|
-| `embed`    | Embedding model      | `encode`, `embed`             |
-| `classify` | Classification model | `encode`, `classify`, `score` |
-| `reward`   | Reward model         | `encode`                      |
+| `--convert` | Model type           | Supported pooling tasks       |
+|-------------|----------------------|-------------------------------|
+| `embed`     | Embedding model      | `encode`, `embed`             |
+| `classify`  | Classification model | `encode`, `classify`, `score` |
+| `reward`    | Reward model         | `encode`                      |
+
+For model architectures that support both generation and pooling, you should set `--runner pooling`
+to use the model as a pooling model.
 
 ## Pooling Tasks
 
@@ -31,9 +34,9 @@ In vLLM, we define the following pooling tasks and corresponding APIs:
 | `classify` | `classify`         |
 | `score`    | `score`            |
 
-\*The `score` API falls back to `embed` task if the model does not support `score` task.
+\* The `score` API falls back to `embed` task if the model does not support `score` task.
 
-Each pooling model in vLLM supports one or more of these tasks according to [Pooler.get_supported_tasks][vllm.model_executor.layers.Pooler.get_supported_tasks].
+Each pooling model in vLLM supports one or more of these tasks according to [Pooler.get_supported_tasks][vllm.model_executor.layers.pooler.Pooler.get_supported_tasks].
 
 By default, the pooler assigned to each task has the following attributes:
 
@@ -70,7 +73,7 @@ It returns the extracted hidden states directly, which is useful for reward mode
 ```python
 from vllm import LLM
 
-llm = LLM(model="Qwen/Qwen2.5-Math-RM-72B", task="reward")
+llm = LLM(model="Qwen/Qwen2.5-Math-RM-72B", runner="pooling")
 (output,) = llm.encode("Hello, my name is")
 
 data = output.outputs.data
@@ -85,7 +88,7 @@ It is primarily designed for embedding models.
 ```python
 from vllm import LLM
 
-llm = LLM(model="intfloat/e5-mistral-7b-instruct", task="embed")
+llm = LLM(model="intfloat/e5-mistral-7b-instruct", runner="pooling")
 (output,) = llm.embed("Hello, my name is")
 
 embeds = output.outputs.embedding
@@ -102,7 +105,7 @@ It is primarily designed for classification models.
 ```python
 from vllm import LLM
 
-llm = LLM(model="jason9693/Qwen2.5-1.5B-apeach", task="classify")
+llm = LLM(model="jason9693/Qwen2.5-1.5B-apeach", runner="pooling")
 (output,) = llm.classify("Hello, my name is")
 
 probs = output.outputs.probs
@@ -123,7 +126,7 @@ It is designed for embedding models and cross encoder models. Embedding models u
 ```python
 from vllm import LLM
 
-llm = LLM(model="BAAI/bge-reranker-v2-m3", task="score")
+llm = LLM(model="BAAI/bge-reranker-v2-m3", runner="pooling")
 (output,) = llm.score("What is the capital of France?",
                       "The capital of Brazil is Brasilia.")
 
@@ -175,7 +178,7 @@ You can change the output dimensions of embedding models that support Matryoshka
 from vllm import LLM, PoolingParams
 
 llm = LLM(model="jinaai/jina-embeddings-v3",
-          task="embed",
+          runner="pooling",
           trust_remote_code=True)
 outputs = llm.embed(["Follow the white rabbit."],
                     pooling_params=PoolingParams(dimensions=32))