Unsloth MCP Server
by MCP-Mirror
The Unsloth MCP Server is designed to integrate with the Unsloth library, which optimizes fine-tuning of large language models. It provides tools to load, fine-tune, generate text, and export models using Unsloth's efficient methods.
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What is Unsloth MCP Server?
The Unsloth MCP Server is a server application that exposes Unsloth's LLM fine-tuning capabilities through a set of tools accessible via the MCP (Modular Control Program). It allows users to leverage Unsloth's optimizations for faster and more memory-efficient fine-tuning of models like Llama, Mistral, Phi, and Gemma.
How to use Unsloth MCP Server?
To use the Unsloth MCP Server, first install Unsloth and the server dependencies. Then, configure the server in your MCP settings with the appropriate command, arguments, and environment variables. You can then use the provided tools like check_installation, list_supported_models, load_model, finetune_model, generate_text, and export_model by calling them with the use_mcp_tool function and providing the necessary parameters.
Key features of Unsloth MCP Server
Optimized fine-tuning for Llama, Mistral, Phi, and Gemma models
4-bit quantization for efficient training
Extended context length support
Simple API for model loading, fine-tuning, and inference
Export to various formats (GGUF, Hugging Face, etc.)
Use cases of Unsloth MCP Server
Fine-tuning LLMs on consumer GPUs with limited VRAM
Accelerating the fine-tuning process for faster experimentation
Extending the context length of LLMs for improved performance on long-form text
Deploying fine-tuned models in various formats for different platforms
FAQ from Unsloth MCP Server
What models are supported by Unsloth?
What models are supported by Unsloth?
Unsloth supports Llama, Mistral, Phi, Gemma, and other models. Use the list_supported_models tool to get a complete list.
What are the system requirements for Unsloth?
What are the system requirements for Unsloth?
Unsloth requires Python 3.10-3.12, an NVIDIA GPU with CUDA support (recommended), and Node.js and npm.
How do I resolve CUDA Out of Memory errors?
How do I resolve CUDA Out of Memory errors?
Reduce the batch size, use 4-bit quantization, enable gradient checkpointing, or try a smaller model.
How do I use a custom dataset for fine-tuning?
How do I use a custom dataset for fine-tuning?
Format your dataset properly and host it on Hugging Face or provide a local path using the dataset_name and data_files parameters in the finetune_model tool.
What export formats are supported?
What export formats are supported?
The export_model tool supports exporting to gguf, ollama, vllm, and huggingface formats.