xturing 0.0.1

Fine-tuning, evaluation and data generation for LLMs

Efficient, fast, and simple fine-tuning of LLM models

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xturing is a python package to perform efficient fine-tuning of LLM models like LLaMA, GPT-J, GPT-2 and more. It supports both single GPU and multi-GPU training. Leverage efficient fine-tuning techniques like LoRA to reduce your hardware costs by up to 90% and train your models in a fraction of the time.

⚙️ Installation

pip install xturing

🚀 Quickstart

from xturing import BaseModel, InstructionDataset

# Load the dataset
instruction_dataset = InstructionDataset("./alpaca_data")

# Initialize the model
model = BaseModel.create("llama_lora")

# Finetune the model
model.finetune(dataset=instruction_dataset)

# Perform inference
output = model.generate(texts=["Why LLM models are becoming so important?"])

print("Generated output by the model: {}".format(output))

You can find the data folder here.

✅ Requirements

• Source version of transformers library

  pip uninstall transformers && pip install git+https://github.com/huggingface/transformers.git
  

• Source version of peft library

  pip uninstall peft && pip install git+https://github.com/huggingface/peft.git
  

📚 Tutorials

• Preparing your dataset
• LLaMA efficient fine-tuning with LoRA
• LLaMA fine-tuning
• GPT-J efficient fine-tuning with LoRA
• GPT2 efficient fine-tuning with LoRA

📈 Roadmap

• Support for LLaMA, GPT-J, GPT-2
• Support for Stable Diffusion
• Dataset generation using self-instruction
• Evaluation of LLM models

🤝 Help and Support

If you have any questions, you can create an issue on this repository.

You can also join our Discord server and start a discussion in the #xturing channel.

📝 License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

🌎 Contributing

As an open source project in a rapidly evolving field, we welcome contributions of all kinds, including new features and better documentation. Please read our contributing guide to learn how you can get involved.