Unsloth

The Unsloth MCP Server is a server for efficiently fine-tuning large language models, achieving a 2-fold speed increase and 80% memory savings through optimization techniques.

Developer tools Artificial intelligence chatbots #Model fine-tuning #Efficient training #Text generation Local .TypeScript

rating : 2.5 points

downloads : 6.5K

update time : 2025-04-28

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What is the Unsloth MCP Server?

The Unsloth MCP Server is an efficient model fine-tuning tool based on the Unsloth library. It significantly improves the fine-tuning speed of large language models while reducing GPU memory usage, allowing larger-scale models to run on consumer-grade GPUs.

How to use the Unsloth MCP Server?

Installation and configuration can be completed in just a few steps. It supports loading, fine-tuning, and exporting multiple models, and provides powerful APIs and rich functional options.

Applicable scenarios

Suitable for developers who need to quickly fine-tune large language models, especially those who still want to conduct efficient training under limited hardware conditions.

Main features

Support multiple models

Compatible with multiple mainstream models such as Llama, Mistral, Phi, and Gemma.

4-bit quantization

Significantly reduces GPU memory usage and supports longer context lengths.

Dynamic optimization

Improves performance through custom CUDA kernels and optimized backpropagation algorithms.

Diverse export formats

Supports multiple model formats such as GGUF, Hugging Face, and Ollama.

Advantages

Double the fine-tuning speed

Reduce GPU memory usage by 80%

Support ultra-long contexts (up to 13 times longer)

No loss of model accuracy

Limitations

Requires an NVIDIA GPU to support CUDA

Not fully compatible with some older Python versions

How to use

Install the Unsloth MCP Server

Clone the project repository and run the build script, ensuring that the environment dependencies are installed.

Configure MCP settings

Update the MCP configuration file to enable the Unsloth server.

Verify the installation

Run the check tool to confirm that Unsloth has been successfully installed.

Usage examples

Load a pre-trained model

Demonstrate how to load a pre-trained Llama model.

Fine-tune a model

Fine-tune the Llama model using the Alpaca dataset.

Text generation example

Generate a story using the fine-tuned model.

Frequently Asked Questions

How to solve the problem of insufficient CUDA GPU memory?

Does it support all Python versions?

What model formats does Unsloth support for export?

Related resources

Unsloth GitHub

Unsloth official code repository

Unsloth documentation

Detailed usage guides and technical documentation

Unsloth community forum

Developer communication platform

🚀 Unbeatable AI Plugin Development Guide

This guide aims to assist developers in creating powerful custom plugins with Unbeatable AI. By leveraging the provided APIs and interfaces, developers can easily integrate AI capabilities into their applications.

🚀 Quick Start

📦 Installation

Clone the repository

git clone https://github.com/yourusername/plugin-template.git
cd plugin-template

Install dependencies
```
npm install
```
Run the example
```
node index.js
```

✨ Features

Model Management

Model Loading: Supports mainstream AI model formats, including TensorFlow and PyTorch.
Model Switching: Easily switch between different models to meet various business needs.

Dataset Preparation

Data Preprocessing: Provides powerful data cleaning and formatting tools.
Data Augmentation: Supports multiple data augmentation methods to enhance model robustness.

Text Generation

Conditional Generation: Generate relevant text content based on input conditions.
Multilingual Support: Supports text generation in multiple languages.

💻 Usage Examples

Basic Usage

# Display plugin information
plugin info

# Load a specified model
plugin load model-name

# Generate text content
plugin generate --prompt "Input prompt"

📚 Documentation

Plugin Development Process

Requirement Analysis: Define the plugin's functionality and target users.
Architecture Design: Design a reasonable module division and interaction interface.
Coding Implementation: Develop specific functions based on the provided APIs.
Testing and Validation: Conduct comprehensive functional testing and performance tuning.
Deployment and Release: Integrate the plugin into the target system and publish it.

API Reference

Core Interface

// Initialize the plugin
const plugin = new Plugin({
  name: 'Custom Plugin',
  version: '1.0.0'
});

// Handle user requests
plugin.on('request', (ctx, next) => {
  // Processing logic
  next();
});

Advanced Usage

const { Plugin } = require('@unsloth/core');

// Create a plugin instance
const plugin = new Plugin({
  name: 'Text Generator',
  version: '1.0.0'
});

// Listen for request events
plugin.on('generate', async (ctx, next) => {
  const prompt = ctx.request.prompt;
  // Handle generation logic
  ctx.response.text = await generateText(prompt);
  next();
});