[Docs] Convert rST to MyST (Markdown) (#11145)

Signed-off-by: Rafael Vasquez <rafvasq21@gmail.com>

docs/source/models/generative_models.md

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+(generative-models)=
+
+# Generative Models
+
+vLLM provides first-class support for generative models, which covers most of LLMs.
+
+In vLLM, generative models implement the {class}`~vllm.model_executor.models.VllmModelForTextGeneration` interface.
+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.
+
+## 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.
+It is similar to [its counterpart in HF Transformers](https://huggingface.co/docs/transformers/main/en/main_classes/text_generation#transformers.GenerationMixin.generate),
+except that tokenization and detokenization are also performed automatically.
+
+```python
+llm = LLM(model="facebook/opt-125m")
+outputs = llm.generate("Hello, my name is")
+
+for output in outputs:
+    prompt = output.prompt
+    generated_text = output.outputs[0].text
+    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
+```
+
+You can optionally control the language generation by passing {class}`~vllm.SamplingParams`.
+For example, you can use greedy sampling by setting {code}`temperature=0`:
+
+```python
+llm = LLM(model="facebook/opt-125m")
+params = SamplingParams(temperature=0)
+outputs = llm.generate("Hello, my name is", params)
+
+for output in outputs:
+    prompt = output.prompt
+    generated_text = output.outputs[0].text
+    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
+```
+
+A code example can be found in [examples/offline_inference.py](https://github.com/vllm-project/vllm/blob/main/examples/offline_inference.py).
+
+### `LLM.beam_search`
+
+The {class}`~vllm.LLM.beam_search` method implements [beam search](https://huggingface.co/docs/transformers/en/generation_strategies#beam-search-decoding) on top of {class}`~vllm.LLM.generate`.
+For example, to search using 5 beams and output at most 50 tokens:
+
+```python
+llm = LLM(model="facebook/opt-125m")
+params = BeamSearchParams(beam_width=5, max_tokens=50)
+outputs = llm.generate("Hello, my name is", params)
+
+for output in outputs:
+    prompt = output.prompt
+    generated_text = output.outputs[0].text
+    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
+```
+
+### `LLM.chat`
+
+The {class}`~vllm.LLM.chat` method implements chat functionality on top of {class}`~vllm.LLM.generate`.
+In particular, it accepts input similar to [OpenAI Chat Completions API](https://platform.openai.com/docs/api-reference/chat)
+and automatically applies the model's [chat template](https://huggingface.co/docs/transformers/en/chat_templating) to format the prompt.
+
+```{important}
+In general, only instruction-tuned models have a chat template.
+Base models may perform poorly as they are not trained to respond to the chat conversation.
+```
+
+```python
+llm = LLM(model="meta-llama/Meta-Llama-3-8B-Instruct")
+conversation = [
+    {
+        "role": "system",
+        "content": "You are a helpful assistant"
+    },
+    {
+        "role": "user",
+        "content": "Hello"
+    },
+    {
+        "role": "assistant",
+        "content": "Hello! How can I assist you today?"
+    },
+    {
+        "role": "user",
+        "content": "Write an essay about the importance of higher education.",
+    },
+]
+outputs = llm.chat(conversation)
+
+for output in outputs:
+    prompt = output.prompt
+    generated_text = output.outputs[0].text
+    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
+```
+
+A code example can be found in [examples/offline_inference_chat.py](https://github.com/vllm-project/vllm/blob/main/examples/offline_inference_chat.py).
+
+If the model doesn't have a chat template or you want to specify another one,
+you can explicitly pass a chat template:
+
+```python
+from vllm.entrypoints.chat_utils import load_chat_template
+
+# You can find a list of existing chat templates under `examples/`
+custom_template = load_chat_template(chat_template="<path_to_template>")
+print("Loaded chat template:", custom_template)
+
+outputs = llm.chat(conversation, chat_template=custom_template)
+```
+
+## Online Inference
+
+Our [OpenAI Compatible Server](../serving/openai_compatible_server) can be used for online inference.
+Please click on the above link for more details on how to launch the server.
+
+### Completions API
+
+Our Completions API is similar to `LLM.generate` but only accepts text.
+It is compatible with [OpenAI Completions API](https://platform.openai.com/docs/api-reference/completions)
+so that you can use OpenAI client to interact with it.
+A code example can be found in [examples/openai_completion_client.py](https://github.com/vllm-project/vllm/blob/main/examples/openai_completion_client.py).
+
+### Chat API
+
+Our Chat API is similar to `LLM.chat`, accepting both text and [multi-modal inputs](#multimodal-inputs).
+It is compatible with [OpenAI Chat Completions API](https://platform.openai.com/docs/api-reference/chat)
+so that you can use OpenAI client to interact with it.
+A code example can be found in [examples/openai_chat_completion_client.py](https://github.com/vllm-project/vllm/blob/main/examples/openai_chat_completion_client.py).