Metadata-Version: 2.1
Name: xinference
Version: 0.1.1
Summary: Model Serving Made Easy
Home-page: https://github.com/xorbitsai/inference
Author: Qin Xuye
Author-email: qinxuye@xprobe.io
Maintainer: Qin Xuye
Maintainer-email: qinxuye@xprobe.io
License: Apache License 2.0
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Topic :: Software Development :: Libraries
Description-Content-Type: text/markdown
Provides-Extra: dev
Provides-Extra: all
Provides-Extra: ggml
Provides-Extra: pytorch
Provides-Extra: doc
Provides-Extra: benchmark
License-File: LICENSE

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<img src="./assets/xorbits-logo.png" width="180px" alt="xorbits" />

# Xorbits Inference: Model Serving Made Easy 🤖

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English | [中文介绍](README_zh_CN.md) | [日本語](README_ja_JP.md)
</div>
<br />


Xorbits Inference(Xinference) is a powerful and versatile library designed to serve language, 
speech recognition, and multimodal models. With Xorbits Inference, you can effortlessly deploy 
and serve your or state-of-the-art built-in models using just a single command. Whether you are a 
researcher, developer, or data scientist, Xorbits Inference empowers you to unleash the full 
potential of cutting-edge AI models.

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## Key Features
🌟 **Model Serving Made Easy**: Simplify the process of serving large language, speech 
recognition, and multimodal models. You can set up and deploy your models
for experimentation and production with a single command.

⚡️ **State-of-the-Art Models**: Experiment with cutting-edge built-in models using a single 
command. Inference provides access to state-of-the-art open-source models!

🖥 **Heterogeneous Hardware Utilization**: Make the most of your hardware resources with
[ggml](https://github.com/ggerganov/ggml). Xorbits Inference intelligently utilizes heterogeneous
hardware, including GPUs and CPUs, to accelerate your model inference tasks.

⚙️ **Flexible API and Interfaces**: Offer multiple interfaces for interacting
with your models, supporting RPC, RESTful API(compatible with OpenAI API), CLI and WebUI
for seamless management and monitoring.

🌐 **Distributed Deployment**: Excel in distributed deployment scenarios, 
allowing the seamless distribution of model inference across multiple devices or machines.

🔌 **Built-in Integration with Third-Party Libraries**: Xorbits Inference seamlessly integrates
with popular third-party libraries like LangChain and LlamaIndex. (Coming soon)

## Getting Started
Xinference can be installed via pip from PyPI. It is highly recommended to create a new virtual
environment to avoid conflicts.

### Installation
```bash
$ pip install "xinference"
```
`xinference` installs basic packages for serving models. 

#### Installation with GGML
To serve ggml models, you need to install the following extra dependencies:
```bash
$ pip install "xinference[ggml]"
```
If you want to achieve acceleration on 
different hardware, refer to the installation documentation of the corresponding package.
- [llama-cpp-python](https://github.com/abetlen/llama-cpp-python#installation-from-pypi-recommended) is required to run `baichuan`, `wizardlm-v1.0`, `vicuna-v1.3` and `orca`.
- [chatglm-cpp-python](https://github.com/li-plus/chatglm.cpp#getting-started) is required to run `chatglm` and `chatglm2`.

#### Installation with PyTorch
To serve PyTorch models, you need to install the following extra dependencies:
```bash
$ pip install "xinference[pytorch]"
```

#### Installation with all dependencies
If you want to serve all the supported models, install all the dependencies:
```bash
$ pip install "xinference[all]"
```


### Deployment
You can deploy Xinference locally with a single command or deploy it in a distributed cluster. 

#### Local
To start a local instance of Xinference, run the following command:
```bash
$ xinference
```

#### Distributed

To deploy Xinference in a cluster, you need to start a Xinference supervisor on one server and 
Xinference workers on the other servers. Follow the steps below:

**Starting the Supervisor**: On the server where you want to run the Xinference supervisor, run the following command:
```bash
$ xinference-supervisor -H "${supervisor_host}"
```
Replace `${supervisor_host}` with the actual host of your supervisor server.

**Starting the Workers**: On each of the other servers where you want to run Xinference workers, run the following command:
```bash
$ xinference-worker -e "http://${supervisor_host}:9997"
```

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://${supervisor_host}:9997`, where
`${supervisor_host}` is the hostname or IP address of the server where the supervisor is running.

You can also view a web UI using the Xinference endpoint to chat with all the 
builtin models. You can even **chat with two cutting-edge AI models side-by-side to compare
their performance**!

![web UI](assets/demo.gif)

### Xinference CLI
Xinference provides a command line interface (CLI) for model management. Here are some useful 
commands:

- Launch a model (a model UID will be returned): `xinference launch`
- List running models: `xinference list`
- List all the builtin models: `xinference list --all`
- Terminate a model: `xinference terminate --model-uid ${model_uid}`

### Xinference Client
Xinference also provides a client for managing and accessing models programmatically:

```python
from xinference.client import Client

client = Client("http://localhost:9997")
model_uid = client.launch_model(model_name="chatglm2")
model = client.get_model(model_uid)

chat_history = []
prompt = "What is the largest animal?"
model.chat(
            prompt,
            chat_history,
            generate_config={"max_tokens": 1024}
        )
```

Result:
```json
{
  "id": "chatcmpl-8d76b65a-bad0-42ef-912d-4a0533d90d61",
  "model": "56f69622-1e73-11ee-a3bd-9af9f16816c6",
  "object": "chat.completion",
  "created": 1688919187,
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "The largest animal that has been scientifically measured is the blue whale, which has a maximum length of around 23 meters (75 feet) for adult animals and can weigh up to 150,000 pounds (68,000 kg). However, it is important to note that this is just an estimate and that the largest animal known to science may be larger still. Some scientists believe that the largest animals may not have a clear \"size\" in the same way that humans do, as their size can vary depending on the environment and the stage of their life."
      },
      "finish_reason": "None"
    }
  ],
  "usage": {
    "prompt_tokens": -1,
    "completion_tokens": -1,
    "total_tokens": -1
  }
}
```

See [examples](examples) for more examples.


## Builtin models
To view the builtin models, run the following command:
```bash
$ xinference list --all
```

### ggmlv3 models

| Name          | Type             | Language | Format  | Size (in billions) | Quantization                            |
|---------------|------------------|----------|---------|--------------------|-----------------------------------------|
| llama-2       | Foundation Model | en       | ggmlv3  | 7, 13              | 'q2_K', 'q3_K_L', ... , 'q6_K', 'q8_0'  |
| baichuan      | Foundation Model | en, zh   | ggmlv3  | 7                  | 'q2_K', 'q3_K_L', ... , 'q6_K', 'q8_0'  |
| llama-2-chat  | RLHF Model       | en       | ggmlv3  | 7, 13, 70          | 'q2_K', 'q3_K_L', ... , 'q6_K', 'q8_0'  |
| chatglm       | SFT Model        | en, zh   | ggmlv3  | 6                  | 'q4_0', 'q4_1', 'q5_0', 'q5_1', 'q8_0'  |
| chatglm2      | SFT Model        | en, zh   | ggmlv3  | 6                  | 'q4_0', 'q4_1', 'q5_0', 'q5_1', 'q8_0'  |
| wizardlm-v1.0 | SFT Model        | en       | ggmlv3  | 7, 13, 33          | 'q2_K', 'q3_K_L', ... , 'q6_K', 'q8_0'  |
| wizardlm-v1.1 | SFT Model        | en       | ggmlv3  | 13                 | 'q2_K', 'q3_K_L', ... , 'q6_K', 'q8_0'  |
| vicuna-v1.3   | SFT Model        | en       | ggmlv3  | 7, 13              | 'q2_K', 'q3_K_L', ... , 'q6_K', 'q8_0'  |
| orca          | SFT Model        | en       | ggmlv3  | 3, 7, 13           | 'q4_0', 'q4_1', 'q5_0', 'q5_1', 'q8_0'  |

### pytorch models

| Name          | Type             | Language | Format  | Size (in billions) | Quantization             |
|---------------|------------------|----------|---------|--------------------|--------------------------|
| baichuan      | Foundation Model | en, zh   | pytorch | 7, 13              | '4-bit', '8-bit', 'none' |
| baichuan-chat | SFT Model        | en, zh   | pytorch | 13                 | '4-bit', '8-bit', 'none' |
| vicuna-v1.3   | SFT Model        | en       | pytorch | 7, 13, 33          | '4-bit', '8-bit', 'none' |


**NOTE**:
- Xinference will download models automatically for you, and by default the models will be saved under `${USER}/.xinference/cache`.
- Foundation models only provide interface `generate`.
- RLHF and SFT models provide both `generate` and `chat`.
- If you want to use Apple Metal GPU for acceleration, please choose the q4_0 and q4_1 quantization methods.
- `llama-2-chat` 70B ggmlv3 model only supports q4_0 quantization currently.


## Pytorch Model Best Practices

Pytorch has been integrated recently, and the usage scenarios are described below:

### supported models
- Foundation Model：baichuan（7B、13B）。
- SFT Model：baichuan-chat（13B）、vicuna-v1.3（7B、13B、33B）。

### supported devices
- CUDA: On Linux and Windows systems, `cuda` device is used by default.
- MPS: On Mac M1/M2 devices, `mps` device is used by default.
- CPU: It is not recommended to use a `cpu` device, as it takes up a lot of memory and the inference speed is very slow.

### quantization methods
- `none`: indicates that no quantization is used.
- `8-bit`: use 8-bit quantization.
- `4-bit`: use 4-bit quantization. Note: 4-bit quantization is only supported on Linux systems and CUDA devices.

### other instructions
- On MacOS system, baichuan-chat model is not supported, and baichuan model cannot use 8-bit quantization.

### use cases

The table below shows memory usage and supported devices of some models.

| Name          | Size (B) | OS    | No quantization (MB) | Quantization 8-bit (MB) | Quantization 4-bit (MB) |
|---------------|----------|-------|----------------------|-------------------------|-------------------------|
| baichuan-chat | 13       | linux | not currently tested | 13275                   | 7263                    |
| baichuan-chat | 13       | macos | not supported        | not supported           | not supported           |
| vicuna-v1.3   | 7        | linux | 12884                | 6708                    | 3620                    |
| vicuna-v1.3   | 7        | macos | 12916                | 565                     | not supported           |
| baichuan      | 7        | linux | 13480                | 7304                    | 4216                    |
| baichuan      | 7        | macos | 13480                | not supported           | not supported           |



## Roadmap
Xinference is currently under active development. Here's a roadmap outlining our planned 
developments for the next few weeks:

### Langchain & LlamaIndex integration
With Xinference, it will be much easier for users to use these libraries and build applications 
with LLMs.
