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145 lines
5.5 KiB
Markdown
145 lines
5.5 KiB
Markdown
# Generative Models
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vLLM provides first-class support for generative models, which covers most of LLMs.
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In vLLM, generative models implement the[VllmModelForTextGeneration][vllm.model_executor.models.VllmModelForTextGeneration] interface.
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Based on the final hidden states of the input, these models output log probabilities of the tokens to generate,
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which are then passed through [Sampler][vllm.model_executor.layers.sampler.Sampler] to obtain the final text.
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## Configuration
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### Model Runner (`--runner`)
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Run a model in generation mode via the option `--runner generate`.
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!!! tip
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There is no need to set this option in the vast majority of cases as vLLM can automatically
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detect the model runner to use via `--runner auto`.
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## Offline Inference
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The [LLM][vllm.LLM] class provides various methods for offline inference.
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See [configuration](../api/README.md#configuration) for a list of options when initializing the model.
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### `LLM.generate`
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The [generate][vllm.LLM.generate] method is available to all generative models in vLLM.
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It is similar to [its counterpart in HF Transformers](https://huggingface.co/docs/transformers/main/en/main_classes/text_generation#transformers.GenerationMixin.generate),
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except that tokenization and detokenization are also performed automatically.
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```python
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from vllm import LLM
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llm = LLM(model="facebook/opt-125m")
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outputs = llm.generate("Hello, my name is")
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for output in outputs:
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prompt = output.prompt
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generated_text = output.outputs[0].text
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print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
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```
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You can optionally control the language generation by passing [SamplingParams][vllm.SamplingParams].
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For example, you can use greedy sampling by setting `temperature=0`:
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```python
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from vllm import LLM, SamplingParams
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llm = LLM(model="facebook/opt-125m")
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params = SamplingParams(temperature=0)
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outputs = llm.generate("Hello, my name is", params)
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for output in outputs:
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prompt = output.prompt
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generated_text = output.outputs[0].text
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print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
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```
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!!! important
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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.
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However, if vLLM's default sampling parameters are preferred, please pass `generation_config="vllm"` when creating the [LLM][vllm.LLM] instance.
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A code example can be found here: <gh-file:examples/offline_inference/basic/basic.py>
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### `LLM.beam_search`
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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].
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For example, to search using 5 beams and output at most 50 tokens:
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```python
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from vllm import LLM
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from vllm.sampling_params import BeamSearchParams
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llm = LLM(model="facebook/opt-125m")
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params = BeamSearchParams(beam_width=5, max_tokens=50)
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outputs = llm.beam_search([{"prompt": "Hello, my name is "}], params)
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for output in outputs:
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generated_text = output.sequences[0].text
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print(f"Generated text: {generated_text!r}")
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```
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### `LLM.chat`
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The [chat][vllm.LLM.chat] method implements chat functionality on top of [generate][vllm.LLM.generate].
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In particular, it accepts input similar to [OpenAI Chat Completions API](https://platform.openai.com/docs/api-reference/chat)
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and automatically applies the model's [chat template](https://huggingface.co/docs/transformers/en/chat_templating) to format the prompt.
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!!! important
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In general, only instruction-tuned models have a chat template.
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Base models may perform poorly as they are not trained to respond to the chat conversation.
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??? code
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```python
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from vllm import LLM
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llm = LLM(model="meta-llama/Meta-Llama-3-8B-Instruct")
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conversation = [
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{
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"role": "system",
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"content": "You are a helpful assistant"
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},
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{
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"role": "user",
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"content": "Hello"
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},
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{
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"role": "assistant",
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"content": "Hello! How can I assist you today?"
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},
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{
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"role": "user",
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"content": "Write an essay about the importance of higher education.",
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},
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]
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outputs = llm.chat(conversation)
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for output in outputs:
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prompt = output.prompt
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generated_text = output.outputs[0].text
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print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
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```
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A code example can be found here: <gh-file:examples/offline_inference/basic/chat.py>
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If the model doesn't have a chat template or you want to specify another one,
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you can explicitly pass a chat template:
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```python
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from vllm.entrypoints.chat_utils import load_chat_template
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# You can find a list of existing chat templates under `examples/`
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custom_template = load_chat_template(chat_template="<path_to_template>")
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print("Loaded chat template:", custom_template)
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outputs = llm.chat(conversation, chat_template=custom_template)
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```
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## Online Serving
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Our [OpenAI-Compatible Server](../serving/openai_compatible_server.md) provides endpoints that correspond to the offline APIs:
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- [Completions API][completions-api] is similar to `LLM.generate` but only accepts text.
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- [Chat API][chat-api] is similar to `LLM.chat`, accepting both text and [multi-modal inputs](../features/multimodal_inputs.md) for models with a chat template.
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