generative_models.md

---
title: Generative Models
---
[](){ #generative-models }

vLLM provides first-class support for generative models, which covers most of LLMs.

In vLLM, generative models implement the [VllmModelForTextGeneration][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 [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.

## Offline Inference

The [LLM][vllm.LLM] class provides various methods for offline inference.
See [configuration][configuration] for a list of options when initializing the model.

### `LLM.generate`

The [generate][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
from vllm import LLM

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 [SamplingParams][vllm.SamplingParams].
For example, you can use greedy sampling by setting `temperature=0`:

```python
from vllm import LLM, SamplingParams

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}")
```

!!! important
    By default, vLLM will use sampling parameters recommended by model creator by applying the `generation_config.json` from the huggingface model repository if it exists. In most cases, this will provide you with the best results by default if [SamplingParams][vllm.SamplingParams] is not specified.

    However, if vLLM's default sampling parameters are preferred, please pass `generation_config="vllm"` when creating the [LLM][vllm.LLM] instance.
A code example can be found here: <gh-file:examples/offline_inference/basic/basic.py>

### `LLM.beam_search`

The [beam_search][vllm.LLM.beam_search] method implements [beam search](https://huggingface.co/docs/transformers/en/generation_strategies#beam-search) on top of [generate][vllm.LLM.generate].
For example, to search using 5 beams and output at most 50 tokens:

```python
from vllm import LLM
from vllm.sampling_params import BeamSearchParams

llm = LLM(model="facebook/opt-125m")
params = BeamSearchParams(beam_width=5, max_tokens=50)
outputs = llm.beam_search([{"prompt": "Hello, my name is "}], params)

for output in outputs:
    generated_text = output.sequences[0].text
    print(f"Generated text: {generated_text!r}")
```

### `LLM.chat`

The [chat][vllm.LLM.chat] method implements chat functionality on top of [generate][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.

??? code

    ```python
    from vllm import LLM

    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 here: <gh-file:examples/offline_inference/basic/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 Serving

Our [OpenAI-Compatible Server][serving-openai-compatible-server] provides endpoints that correspond to the offline APIs:

- [Completions API][completions-api] is similar to `LLM.generate` but only accepts text.
- [Chat API][chat-api]  is similar to `LLM.chat`, accepting both text and [multi-modal inputs][multimodal-inputs] for models with a chat template.