langchain/docs/versioned_docs/version-0.2.x/integrations/providers/xinference.mdx

# Xorbits Inference (Xinference)

This page demonstrates how to use [Xinference](https://github.com/xorbitsai/inference)
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 Setup

Xinference can be installed via pip from PyPI: 

```bash
pip install "xinference[all]"
```

## LLM

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

```bash
xinference list --all
```


### Wrapper for Xinference

You can start a local instance of Xinference by running:

```bash
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:

```bash
xinference-supervisor -H "${supervisor_host}"
```


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

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

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

```bash
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, 

```bash
xinference launch -n orca -s 3 -q q4_0
```

A model uid will be returned.

Example usage:

```python
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},
)

```

### Usage

For more information and detailed examples, refer to the
[example for xinference LLMs](/docs/integrations/llms/xinference)

### Embeddings

Xinference also supports embedding queries and documents. See
[example for xinference embeddings](/docs/integrations/text_embedding/xinference) 
for a more detailed demo.