Unsloth MCP Server

🚀 Unsloth MCP Server

Unsloth is an open - source inference framework designed for large - scale language models, featuring high efficiency and low resource consumption. Unlike traditional deep - learning frameworks, Unsloth can be up to 20 times faster when handling tasks of the same scale, while significantly reducing the demand for GPU resources.

✨ Features

• Fast Processing: Unsloth offers faster inference speeds compared to PyTorch and other mainstream frameworks.
• Low Resource Consumption: It reduces memory usage by over 10 times under the same GPU configuration.
• Long Context Support: It can handle a context window of up to 4k tokens.

📦 MCP Server Features

By running Unsloth's MCP (Multi - Criteria Processing) server, you can enjoy the following features:

1. Model Management

   • Supports loading large - language models in multiple formats.
   • Provides a model weight caching mechanism to improve loading speed.
   • Supports online updates and hot reloading.

2. Inference Service

   • Provides RESTful API interfaces.
   • Supports batch processing of requests.
   • Includes built - in model monitoring and health checks.

3. Extension Plugins

   • Configurable middleware system.
   • Supports custom post - processing logic.
   • Provides detailed logging and tracking.

4. High Availability

   • Based on the high - performance gRPC communication protocol.
   • Built - in load balancing and fault - recovery mechanisms.
   • Supports horizontal scaling and cluster deployment.

💻 Usage Examples

Installation Dependencies

pip install mcp-server unsloth[tensor,transformers]

Start the Server

mcp-server --model-path <your-model-path> --port 8080

Call the Interface

You can call the MCP service via HTTP or gRPC protocols:

HTTP Example

import requests

url = "http://localhost:8080/api/infer"
payload = {
    "input": "这是一个测试句子。",
    "parameters": {
        "temperature": 0.7,
        "top_p": 0.9
    }
}
response = requests.post(url, json=payload)
print(response.json())

gRPC Example

import grpc
from unslothproto import inference_pb2_grpc

channel = grpc.insecure_channel('localhost:8081')
stub = inference_pb2_grpc.InferenceStub(channel)

request = inference_pb2.InferRequest(
    inputs=["这是一个测试句子"],
    parameters=inference_pb2.Parameters(temperature=0.7, top_p=0.9)
)
response = stub.Infer(request)
print(response.outputs)

📚 Model Support

We currently support the following model formats:

• ONNX: Conversion Guide
• TensorRT: Deployment Documentation
• PyTorch: Migration Tutorial

📊 Performance Benchmark

🤝 Contribution Guidelines

Community contributions are welcome! You can participate in the following ways:

• Submit issues: Issues
• Create pull requests: Pull Requests
• Share your usage experience: Discussions

Let's drive the development of language - model inference technology together!