npm package: @mseep/unsloth-server
Description: MCP server for Unsloth - a library for optimizing and fine-tuning LLMs
Latest version: 1.0.0

# Unsloth MCP Server

An MCP server for [Unsloth](https://github.com/unslothai/unsloth) - a library that makes LLM fine-tuning 2x faster with 80% less memory.

## What is Unsloth?

Unsloth is a library that dramatically improves the efficiency of fine-tuning large language models:

- **Speed**: 2x faster fine-tuning compared to standard methods
- **Memory**: 80% less VRAM usage, allowing fine-tuning of larger models on consumer GPUs
- **Context Length**: Up to 13x longer context lengths (e.g., 89K tokens for Llama 3.3 on 80GB GPUs)
- **Accuracy**: No loss in model quality or performance

Unsloth achieves these improvements through custom CUDA kernels written in OpenAI's Triton language, optimized backpropagation, and dynamic 4-bit quantization.

## Features

- Optimize fine-tuning for Llama, Mistral, Phi, Gemma, and other 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.)

## Quick Start

1. Install Unsloth: `pip install unsloth`
2. Install and build the server:
   ```bash
   cd unsloth-server
   npm install
   npm run build
   ```
3. Add to MCP settings:
   ```json
   {
     "mcpServers": {
       "unsloth-server": {
         "command": "node",
         "args": ["/path/to/unsloth-server/build/index.js"],
         "env": {
           "HUGGINGFACE_TOKEN": "your_token_here" // Optional
         },
         "disabled": false,
         "autoApprove": []
       }
     }
   }
   ```

## Available Tools

### check_installation

Verify if Unsloth is properly installed on your system.

**Parameters**: None

**Example**:
```javascript
const result = await use_mcp_tool({
  server_name: "unsloth-server",
  tool_name: "check_installation",
  arguments: {}
});
```

### list_supported_models

Get a list of all models supported by Unsloth, including Llama, Mistral, Phi, and Gemma variants.

**Parameters**: None

**Example**:
```javascript
const result = await use_mcp_tool({
  server_name: "unsloth-server",
  tool_name: "list_supported_models",
  arguments: {}
});
```

### load_model

Load a pretrained model with Unsloth optimizations for faster inference and fine-tuning.

**Parameters**:
- `model_name` (required): Name of the model to load (e.g., "unsloth/Llama-3.2-1B")
- `max_seq_length` (optional): Maximum sequence length for the model (default: 2048)
- `load_in_4bit` (optional): Whether to load the model in 4-bit quantization (default: true)
- `use_gradient_checkpointing` (optional): Whether to use gradient checkpointing to save memory (default: true)

**Example**:
```javascript
const result = await use_mcp_tool({
  server_name: "unsloth-server",
  tool_name: "load_model",
  arguments: {
    model_name: "unsloth/Llama-3.2-1B",
    max_seq_length: 4096,
    load_in_4bit: true
  }
});
```

### finetune_model

Fine-tune a model with Unsloth optimizations using LoRA/QLoRA techniques.

**Parameters**:
- `model_name` (required): Name of the model to fine-tune
- `dataset_name` (required): Name of the dataset to use for fine-tuning
- `output_dir` (required): Directory to save the fine-tuned model
- `max_seq_length` (optional): Maximum sequence length for training (default: 2048)
- `lora_rank` (optional): Rank for LoRA fine-tuning (default: 16)
- `lora_alpha` (optional): Alpha for LoRA fine-tuning (default: 16)
- `batch_size` (optional): Batch size for training (default: 2)
- `gradient_accumulation_steps` (optional): Number of gradient accumulation steps (default: 4)
- `learning_rate` (optional): Learning rate for training (default: 2e-4)
- `max_steps` (optional): Maximum number of training steps (default: 100)
- `dataset_text_field` (optional): Field in the dataset containing the text (default: 'text')
- `load_in_4bit` (optional): Whether to use 4-bit quantization (default: true)

**Example**:
```javascript
const result = await use_mcp_tool({
  server_name: "unsloth-server",
  tool_name: "finetune_model",
  arguments: {
    model_name: "unsloth/Llama-3.2-1B",
    dataset_name: "tatsu-lab/alpaca",
    output_dir: "./fine-tuned-model",
    max_steps: 100,
    batch_size: 2,
    learning_rate: 2e-4
  }
});
```

### generate_text

Generate text using a fine-tuned Unsloth model.

**Parameters**:
- `model_path` (required): Path to the fine-tuned model
- `prompt` (required): Prompt for text generation
- `max_new_tokens` (optional): Maximum number of tokens to generate (default: 256)
- `temperature` (optional): Temperature for text generation (default: 0.7)
- `top_p` (optional): Top-p for text generation (default: 0.9)

**Example**:
```javascript
const result = await use_mcp_tool({
  server_name: "unsloth-server",
  tool_name: "generate_text",
  arguments: {
    model_path: "./fine-tuned-model",
    prompt: "Write a short story about a robot learning to paint:",
    max_new_tokens: 512,
    temperature: 0.8
  }
});
```

### export_model

Export a fine-tuned Unsloth model to various formats for deployment.

**Parameters**:
- `model_path` (required): Path to the fine-tuned model
- `export_format` (required): Format to export to (gguf, ollama, vllm, huggingface)
- `output_path` (required): Path to save the exported model
- `quantization_bits` (optional): Bits for quantization (for GGUF export) (default: 4)

**Example**:
```javascript
const result = await use_mcp_tool({
  server_name: "unsloth-server",
  tool_name: "export_model",
  arguments: {
    model_path: "./fine-tuned-model",
    export_format: "gguf",
    output_path: "./exported-model.gguf",
    quantization_bits: 4
  }
});
```

## Advanced Usage

### Custom Datasets

You can use custom datasets by formatting them properly and hosting them on Hugging Face or providing a local path:

```javascript
const result = await use_mcp_tool({
  server_name: "unsloth-server",
  tool_name: "finetune_model",
  arguments: {
    model_name: "unsloth/Llama-3.2-1B",
    dataset_name: "json",
    data_files: {"train": "path/to/your/data.json"},
    output_dir: "./fine-tuned-model"
  }
});
```

### Memory Optimization

For large models on limited hardware:
- Reduce batch size and increase gradient accumulation steps
- Use 4-bit quantization
- Enable gradient checkpointing
- Reduce sequence length if possible

## Troubleshooting

### Common Issues

1. **CUDA Out of Memory**: Reduce batch size, use 4-bit quantization, or try a smaller model
2. **Import Errors**: Ensure you have the correct versions of torch, transformers, and unsloth installed
3. **Model Not Found**: Check that you're using a supported model name or have access to private models

### Version Compatibility

- Python: 3.10, 3.11, or 3.12 (not 3.13)
- CUDA: 11.8 or 12.1+ recommended
- PyTorch: 2.0+ recommended

## Performance Benchmarks

| Model | VRAM | Unsloth Speed | VRAM Reduction | Context Length |
|-------|------|---------------|----------------|----------------|
| Llama 3.3 (70B) | 80GB | 2x faster | >75% | 13x longer |
| Llama 3.1 (8B) | 80GB | 2x faster | >70% | 12x longer |
| Mistral v0.3 (7B) | 80GB | 2.2x faster | 75% less | - |

## Requirements

- Python 3.10-3.12
- NVIDIA GPU with CUDA support (recommended)
- Node.js and npm

## License

Apache-2.0