💯AI00 RWKV Server

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AI00 RWKV Server is an inference API server for the RWKV language model based upon the web-rwkv inference engine.

It supports VULKAN parallel and concurrent batched inference and can run on all GPUs that support VULKAN. No need for Nvidia cards!!! AMD cards and even integrated graphics can be accelerated!!!

No need for bulky pytorch, CUDA and other runtime environments, it's compact and ready to use out of the box!

Compatible with OpenAI's ChatGPT API interface.

100% open source and commercially usable, under the MIT license.

If you are looking for a fast, efficient, and easy-to-use LLM API server, then AI00 RWKV Server is your best choice. It can be used for various tasks, including chatbots, text generation, translation, and Q&A.

Join the AI00 RWKV Server community now and experience the charm of AI!

QQ Group for communication: 30920262

💥Features

• Based on the RWKV model, it has high performance and accuracy
• Supports VULKAN inference acceleration, you can enjoy GPU acceleration without the need for CUDA! Supports AMD cards, integrated graphics, and all GPUs that support VULKAN
• No need for bulky pytorch, CUDA and other runtime environments, it's compact and ready to use out of the box!
• Compatible with OpenAI's ChatGPT API interface

⭕Usages

• Chatbots
• Text generation
• Translation
• Q&A
• Any other tasks that LLM can do

👻Other

• Based on the web-rwkv project
• Model download: V5 or V6

Installation, Compilation, and Usage

📦Download Pre-built Executables

1. Directly download the latest version from Release

2. After downloading the model, place the model in the assets/models/ path, for example, assets/models/RWKV-x060-World-3B-v2-20240228-ctx4096.st

3. Optionally modify assets/Config.toml for model configurations like model path, quantization layers, etc.

4. Run in the command line

   $ ./ai00_rwkv_server

5. Open the browser and visit the WebUI https://localhost:65530

📜(Optional) Build from Source

1. Install Rust

2. Clone this repository

   $ git clone https://github.com/cgisky1980/ai00_rwkv_server.git
   $ cd ai00_rwkv_server

3. After downloading the model, place the model in the assets/models/ path, for example, assets/models/RWKV-x060-World-3B-v2-20240228-ctx4096.st

4. Compile

   $ cargo build --release

5. After compilation, run

   $ cargo run --release

6. Open the browser and visit the WebUI https://localhost:65530

📒Convert the Model

It only supports Safetensors models with the .st extension now. Models saved with the .pth extension using torch need to be converted before use.

1. Download the .pth model

2. In the Release you could find an executable called converter. Run

$ ./converter --input /path/to/model.pth

3. If you are building from source, run

$ cargo run --release --bin converter -- --input /path/to/model.pth

4. Just like the steps mentioned above, place the model in the .st model in the assets/models/ path and modify the model path in assets/Config.toml

📝Supported Arguments

• --config: Configure file path (default: assets/Config.toml)
• --ip: The IP address the server is bound to
• --port: Running port

📙Currently Available APIs

The API service starts at port 65530, and the data input and output format follow the Openai API specification.

• /api/oai/v1/models
• /api/oai/models
• /api/oai/v1/chat/completions
• /api/oai/chat/completions
• /api/oai/v1/completions
• /api/oai/completions
• /api/oai/v1/embeddings
• /api/oai/embeddings

The following is an example of ai00 invocation based on Python and an out of the box tool class implementation

import openai

class Ai00:
    def __init__(self,model="model",port=65530,api_key="JUSTSECRET_KEY") :
        openai.api_base = f"http://127.0.0.1:{port}/api/oai"
        openai.api_key = api_key
        self.ctx = []
        self.params = {
            "system_name": "System",
            "user_name": "User", 
            "assistant_name": "Assistant",
            "model": model,
            "max_tokens": 4096,
            "top_p": 0.6,
            "temperature": 1,
            "presence_penalty": 0.3,
            "frequency_penalty": 0.3,
            "half_life": 400,
            "stop": ['\x00','\n\n']
        }
        
    def set_params(self,**kwargs):
        self.params.update(kwargs)
        
    def clear_ctx(self):
        self.ctx = []
        
    def get_ctx(self):
        return self.ctx
    
    def continuation(self, message):
        response = openai.Completion.create(
            model=self.params['model'],
            prompt=message,
            max_tokens=self.params['max_tokens'],
            half_life=self.params['half_life'],
            top_p=self.params['top_p'],
            temperature=self.params['temperature'],
            presence_penalty=self.params['presence_penalty'],
            frequency_penalty=self.params['frequency_penalty'],
            stop=self.params['stop']
        )
        result = response.choices[0].text
        return result
    
    def append_ctx(self,role,content):
        self.ctx.append({
            "role": role,
            "content": content
        })
        
    def send_message(self, message,role="user"):
        self.ctx.append({
            "role": role,
            "content": message
        })
        result = openai.ChatCompletion.create(
            model=self.params['model'],
            messages=self.ctx,
            names={
                "system": self.params['system_name'],
                "user": self.params['user_name'],
                "assistant": self.params['assistant_name']
            },
            max_tokens=self.params['max_tokens'],
            half_life=self.params['half_life'],
            top_p=self.params['top_p'],
            temperature=self.params['temperature'],
            presence_penalty=self.params['presence_penalty'],
            frequency_penalty=self.params['frequency_penalty'],
            stop=self.params['stop']
        )
        result = result.choices[0].message['content']
        self.ctx.append({
            "role": "assistant",
            "content": result
        })
        return result
    
ai00 = Ai00()
ai00.set_params(
    max_tokens = 4096,
    top_p = 0.55,
    temperature = 2,
    presence_penalty = 0.3,
    frequency_penalty = 0.8,
    half_life = 400,
    stop = ['\x00','\n\n']
)
print(ai00.send_message("how are you?"))
print(ai00.send_message("me too!"))
print(ai00.get_ctx())
ai00.clear_ctx()
print(ai00.continuation("i like"))

📙WebUI Screenshots

Chat Feature

Continuation Feature

Paper Writing Feature

📝TODO List

• Support for text_completions and chat_completions
• Support for sse push
• Add embeddings
• Integrate basic front-end
• Parallel inference via batch serve
• Support for int8 quantization
• Support for NF4 quantization
• Support for LoRA model
• Hot loading and switching of LoRA model

👥Join Us

We are always looking for people interested in helping us improve the project. If you are interested in any of the following, please join us!

• 💀Writing code
• 💬Providing feedback
• 🔆Proposing ideas or needs
• 🔍Testing new features
• ✏Translating documentation
• 📣Promoting the project
• 🏅Anything else that would be helpful to us

No matter your skill level, we welcome you to join us. You can join us in the following ways:

• Join our Discord channel
• Join our QQ group
• Submit issues or pull requests on GitHub
• Leave feedback on our website

We can't wait to work with you to make this project better! We hope the project is helpful to you!

Thank you to these awesome individuals who are insightful and outstanding for their support and selfless dedication to the project

顾真牛 📖 💻 🖋 🎨 🧑‍🏫

研究社交 💻 💡 🤔 🚧 👀 📦

josc146 🐛 💻 🤔 🔧

l15y 🔧 🔌 💻

Cahya Wirawan 🐛

yuunnn_w 📖 ⚠️

longzou 💻 🛡️

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