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Xorbits Inference (Xinference) | 🦜️🔗 LangChain

This page demonstrates how to use Xinference with LangChain.

Xinference is a powerful and versatile library designed to serve LLMs, speech recognition models, and multimodal models, even on your laptop. With Xorbits Inference, you can effortlessly deploy and serve your or state-of-the-art built-in models using just a single command.

Xinference can be installed via pip from PyPI:

pip install "xinference[all]"

Xinference supports various models compatible with GGML, including chatglm, baichuan, whisper, vicuna, and orca. To view the builtin models, run the command:

You can start a local instance of Xinference by running:

You can also deploy Xinference in a distributed cluster. To do so, first start an Xinference supervisor on the server you want to run it:

xinference-supervisor -H "${supervisor_host}"

Then, start the Xinference workers on each of the other servers where you want to run them on:

xinference-worker -e "http://${supervisor_host}:9997"

You can also start a local instance of Xinference by running:

Once Xinference is running, an endpoint will be accessible for model management via CLI or Xinference client.

For local deployment, the endpoint will be http://localhost:9997.

For cluster deployment, the endpoint will be http://${supervisor_host}:9997.

Then, you need to launch a model. You can specify the model names and other attributes including model_size_in_billions and quantization. You can use command line interface (CLI) to do it. For example,

xinference launch -n orca -s 3 -q q4_0

A model uid will be returned.

Example usage:

from langchain_community.llms import Xinference

llm = Xinference(
    server_url="http://0.0.0.0:9997",
    model_uid = {model_uid} 
)

llm(
    prompt="Q: where can we visit in the capital of France? A:",
    generate_config={"max_tokens": 1024, "stream": True},
)

For more information and detailed examples, refer to the example for xinference LLMs

Xinference also supports embedding queries and documents. See example for xinference embeddings for a more detailed demo.

Install the integration package with:

pip install langchain-xinference

from langchain_xinference.chat_models import ChatXinference

from langchain_xinference.llms import Xinference

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