Ototao Unsloth MCP Server

🚀 Unscaled Technical Server

This project offers an unscaled technical server that simplifies model loading, training, evaluation, and prediction processes. It provides a set of commands and tools to streamline these operations.

🚀 Quick Start

This section provides a quick guide on how to install, configure, and start using the Unscaled Technical Server.

📦 Installation

Installation Requirements

• Python Version: 3.10, 3.11, or 3.12 (3.13 is not supported).
• CUDA Version: It is recommended to use version 11.8 or higher.
• PyTorch Version: It is recommended to use version 2.0 or higher.
• Node.js and npm

Quick Installation Guide

Use the following commands for a quick installation:

pip install torch transformers unsloth
npm install -g unsloth-cli

Server Configuration

Create a configuration file named server.json with the following content:

{
  "port": 1234,
  "device": "cuda",
  "model_path": "path/to/model.json"
}

Start the server:

python -m unsloth.server start --port 1234 model.json

💻 Usage Examples

Loading a Model

Use the following command to load a model:

unsloth load --model_name llava-7b --device cuda

Training a Model

An example command for training a model is as follows:

unsloth train --dataset_path /path/to/dataset --output_dir ./checkpoints --batch_size 32 --learning_rate 0.001

Evaluating a Model

The command for evaluating a model is as follows:

unsloth evaluate --checkpoint_path ./checkpoints/last.pth --test_data_path /path/to/test.json

Prediction Inference

An example command for making predictions:

unsloth predict --model_path ./final_model.pth --input_data input.json

📚 Documentation

Notes

Performance Optimization

• Use quantization techniques to reduce memory usage.
• Adjust the batch size and GPU utilization to optimize performance.

Log Management

• Configure the logging level; it is recommended to use logging.DEBUG for detailed debugging.
• Regularly back up log files to prevent data loss.

Error Handling

• Ensure that the input data format is correct to avoid parsing errors.
• When handling exceptions, promptly capture and record error information.

Troubleshooting

Common Issues

1. GPU Unavailable

• Check if CUDA is installed correctly and confirm that the device name is correct.

2. Poor Performance

• Confirm that the batch size and GPU memory are sufficient. Try reducing the batch size or upgrading the hardware.

3. Model Loading Failure

• Verify the model file path and format to ensure they are consistent with those used during training.

Performance Benchmark

• Computing Resources: It is recommended to use a multi-GPU cluster to enhance parallel processing capabilities.
• Storage Requirements: Large datasets require at least 1TB of available space.
• Network Bandwidth: A high-bandwidth environment is recommended for distributed training to reduce communication latency.

📄 License

This project is licensed under the MIT License. For more details, please refer to the LICENSE file.