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Co-authored-by: Lucain <lucainp@gmail.com> Co-authored-by: Omar Sanseviero <osanseviero@gmail.com>
75 lines
4.4 KiB
Markdown
75 lines
4.4 KiB
Markdown
# Consuming Text Generation Inference
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There are many ways you can consume Text Generation Inference server in your applications. After launching, you can use the `/generate` route and make a `POST` request to get results from the server. You can also use the `/generate_stream` route if you want TGI to return a stream of tokens. You can make the requests using the tool of your preference, such as curl, Python or TypeScrpt. For a final end-to-end experience, we also open-sourced ChatUI, a chat interface for open-source models.
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## curl
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After the launch, you can query the model using either the `/generate` or `/generate_stream` routes:
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```bash
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curl 127.0.0.1:8080/generate \
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-X POST \
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-d '{"inputs":"What is Deep Learning?","parameters":{"max_new_tokens":20}}' \
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-H 'Content-Type: application/json'
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```
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## Inference Client
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[`huggingface-hub`](https://huggingface.co/docs/huggingface_hub/main/en/index) is a Python library to interact with the Hugging Face Hub, including its endpoints. It provides a nice high-level class, [`~huggingface_hub.InferenceClient`], which makes it easy to make calls to a TGI endpoint. `InferenceClient` also takes care of parameter validation and provides a simple to-use interface.
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You can simply install `huggingface-hub` package with pip.
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```bash
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pip install huggingface-hub
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```
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Once you start the TGI server, instantiate `InferenceClient()` with the URL to the endpoint serving the model. You can then call `text_generation()` to hit the endpoint through Python.
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```python
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from huggingface_hub import InferenceClient
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client = InferenceClient(model=URL_TO_ENDPOINT_SERVING_TGI)
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client.text_generation(prompt="Write a code for snake game", model=URL_TO_ENDPOINT_SERVING_TGI)
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```
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To stream tokens in `InferenceClient`, simply pass `stream=True`. Another parameter you can use with TGI backend is `details`. You can get more details on generation (tokens, probabilities, etc.) by setting `details` to `True`. By default, `details` is set to `False`, and `text_generation` returns a string. If you pass `details=True` and `stream=True`, `text_generation` will return a `TextGenerationStreamResponse` which consists of the generated token, generated text, and details.
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```python
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output = client.text_generation(prompt="Meaning of life is", model=URL_OF_ENDPOINT, details=True)
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print(output)
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# TextGenerationResponse(generated_text=' a complex concept that is not always clear to the individual. It is a concept that is not always', details=Details(finish_reason=<FinishReason.Length: 'length'>, generated_tokens=20, seed=None, prefill=[], tokens=[Token(id=267, text=' a', logprob=-2.0723474, special=False), Token(id=11235, text=' complex', logprob=-3.1272552, special=False), Token(id=17908, text=' concept', logprob=-1.3632495, special=False),..))
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```
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You can see how to stream below.
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```python
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output = client.text_generation(prompt="Meaning of life is", model="http://localhost:3000/", stream=True, details=True)
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print(next(iter(output)))
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# TextGenerationStreamResponse(token=Token(id=267, text=' a', logprob=-2.0723474, special=False), generated_text=None, details=None)
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```
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You can check out the details of the function [here](https://huggingface.co/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceClient.text_generation).
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## ChatUI
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ChatUI is an open-source interface built for LLM serving. It offers many customization options, such as web search with SERP API and more. ChatUI can automatically consume the TGI server and even provides an option to switch between different TGI endpoints. You can try it out at [Hugging Chat](https://huggingface.co/chat/), or use the [ChatUI Docker Space](https://huggingface.co/new-space?template=huggingchat/chat-ui-template) to deploy your own Hugging Chat to Spaces.
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To serve both ChatUI and TGI in same environment, simply add your own endpoints to the `MODELS` variable in `.env.local` file inside the `chat-ui` repository. Provide the endpoints pointing to where TGI is served.
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```
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{
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// rest of the model config here
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"endpoints": [{"url": "https://HOST:PORT/generate_stream"}]
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}
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```
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## API documentation
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You can consult the OpenAPI documentation of the `text-generation-inference` REST API using the `/docs` route. The Swagger UI is also available [here](https://huggingface.github.io/text-generation-inference).
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