Unsloth MCP Server

What is Unsloth MCP Server?

An MCP server for the Unsloth library, which makes fine-tuning of large language models 2x faster with 80% less memory. It integrates with Unsloth to provide tools for loading, fine-tuning, generating text, and exporting models—designed for developers who want to fine-tune LLMs efficiently on consumer hardware.

How to use Unsloth MCP Server?

Install Unsloth (pip install unsloth), clone the repository, run npm install and npm run build, then configure your MCP client (Claude Desktop, Claude Code, Cline, Cursor) to run the built build/index.js with node. Optionally set HUGGINGFACE_TOKEN in the environment for gated models. After the planned npm publish, the same clients can use npx -y unsloth-mcp-server without cloning. The server exposes six tools: check_installation, list_supported_models, load_model, finetune_model, generate_text, and export_model.

Key features of Unsloth MCP Server

• 2x faster fine-tuning compared to standard methods
• 80% less VRAM usage for larger models on consumer GPUs
• Supports 4‑bit quantization for efficient training
• Extended context length (up to 13x longer)
• Export fine‑tuned models to GGUF, Ollama, vLLM, or Hugging Face
• Simple API for model loading, fine‑tuning, and inference

Use cases of Unsloth MCP Server

• Fine‑tune Llama, Mistral, Phi, Gemma, or other models on a single NVIDIA GPU
• Generate text from a fine‑tuned model using a custom prompt
• Export a fine‑tuned model to GGUF format for local inference or deployment
• Use a custom dataset hosted on Hugging Face or a local path
• Memory‑optimized fine‑tuning with gradient checkpointing and reduced batch size

FAQ from Unsloth MCP Server

What makes Unsloth different from standard fine‑tuning?

Unsloth achieves 2x faster training and 80% lower memory usage through custom CUDA kernels, optimized backpropagation, and dynamic 4‑bit quantization, with no loss in model accuracy.

What are the runtime requirements?

Python 3.10–3.12, an NVIDIA GPU with CUDA (11.8 or 12.1+ recommended), PyTorch 2.0+, and Node.js with npm. The Unsloth Python library must be installed separately.

Where does the fine‑tuned model data live?

Models and datasets are stored locally on the machine running the server. You can specify local paths or Hugging Face dataset/model names. Exported models are written to the output directory you provide.

What are known limitations?

CUDA out‑of‑memory errors can occur with large models on limited hardware—reduce batch size, use 4‑bit quantization, or enable gradient checkpointing. Import errors may arise from incompatible versions of PyTorch, transformers, or unsloth.

What transport and authentication does the server use?

The server communicates over stdio (standard input/output). An optional HUGGINGFACE_TOKEN environment variable can be provided for accessing gated or private models on Hugging Face.