Xorbits Inference (Xinference)
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.
Installation and Setupβ
Xinference can be installed via pip from PyPI:
pip install "xinference[all]"
LLMβ
Xinference supports various models compatible with GGML, including chatglm, baichuan, whisper, vicuna, and orca. To view the builtin models, run the command:
xinference list --all
Wrapper for Xinferenceβ
You can start a local instance of Xinference by running:
xinference
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:
xinference
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} # replace model_uid with the model UID return from launching the model
)
llm(
prompt="Q: where can we visit in the capital of France? A:",
generate_config={"max_tokens": 1024, "stream": True},
)
API Reference:
Usageβ
For more information and detailed examples, refer to the example for xinference LLMs
Embeddingsβ
Xinference also supports embedding queries and documents. See example for xinference embeddings for a more detailed demo.