ShareLMAPI 0.1.1

A local language model API for sharing models between programs.

ShareLMAPI

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ShareLMAPI is a local language model sharing API that uses FastAPI to provide interfaces, allowing different programs to share the same local model, reducing resource consumption. It supports streaming generation and various model configuration methods.

Table of Contents

• Features
• Installation
• Configuration
• Usage
• API Documentation
• Client Usage
• Testing
• Contributing
• License

Features

• Support for multiple model loading methods: default, BitsAndBytes quantization, PEFT
• Support for streaming and non-streaming text generation
• Support for dialogue history and system prompts
• Easy to configure and extend
• Flexible model server URL configuration

Installation

1. Clone the repository

git clone https://github.com/yourusername/ShareLMAPI.git
cd ShareLMAPI

2. Install dependencies

Dependencies can be installed using Conda or Pip.

Using Conda:

conda env create -f environment.yml
conda activate ShareLMAPI

Using Pip:

pip install -r requirements.txt

3. Install for local development

If you plan to develop this package, use the following command to install it:

pip install -e .

Configuration

1. Navigate to the configs directory and open model_config.yaml.
2. Modify the configuration according to your needs. You can specify:
   • Model name
   • Loading method (default, bitsandbytes, or peft)
   • Device (CPU or CUDA)
   • Other model-specific settings
   • Model server URL

Example configuration:

model:
  name: "gpt-2"
  loading_method: "default"
  default:
    device: "cuda"
  bitsandbytes:
    device: "cuda"
    quantization_config:
      quant_type: "nf4"
      load_in_4bit: True
      bnb_4bit_quant_type: "nf4"
      bnb_4bit_compute_dtype: "float16"
      bnb_4bit_use_double_quant: False
  peft:
    device: "cuda"
    peft_type: "lora"
    peft_config:
      r: 8
      lora_alpha: 16
      lora_dropout: 0.1
      target_modules: ["q_proj", "v_proj"]

model_server:
  model_server_url: "http://localhost:5000"

Usage

Start the model server

First, start the model server to load and manage the language model:

uvicorn ShareLMAPI.server.model_server:app --host 0.0.0.0 --port 5000

Start the frontend API server

After the model server is running, start the frontend server to handle client requests:

gunicorn -w 4 -k uvicorn.workers.UvicornWorker ShareLMAPI.server.server:app --bind 0.0.0.0:8000

API Documentation

1. /generate_stream

Generate model responses and stream the results.

• Method: POST
• URL: http://localhost:8000/generate_stream
• Parameters:
  • dialogue_history: List of dialogue messages (optional)
  • prompt: User input prompt (if dialogue history is not provided)
  • max_length: Maximum number of tokens to generate
  • temperature: Parameter to control generation randomness
  • generation_kwargs: Other generation parameters (optional)

2. /generate

Generate model responses without streaming.

• Method: POST
• URL: http://localhost:8000/generate
• Parameters: Same as /generate_stream

Client Usage

install

pip install ShareLMAPI

Here's an example of how to use the LocalModelAPIClient to call the API:

from ShareLMAPI.client import ShareLMAPIClient

# Create API client
client = ShareLMAPIClient(base_url="http://localhost:8000")

# Streaming generation
for chunk in client.generate_text("Once upon a time", max_length=50, streamer=True):
    print(chunk, end='', flush=True)

# Non-streaming generation
response = client.generate_text("What is the capital of France?", max_length=50, streamer=False)
print(response)

# Using dialogue history
dialogue_history = [
    {"role": "user", "content": "Hi, who are you?"},
    {"role": "assistant", "content": "I'm an AI assistant. How can I help you today?"},
    {"role": "user", "content": "Can you explain quantum computing?"}
]
response = client.generate_text(dialogue_history=dialogue_history, max_length=200, streamer=False)
print(response)

Testing

Run the following command in the project root directory to execute tests:

pytest -s tests/test_client.py

This will run the tests and display the output results.

Contributing

Contributions of any form are welcome. Please follow these steps:

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

License

This project is open-sourced under the MIT License. See the LICENSE file for more details.