Awesome Fine-Tuning

Welcome to the Awesome Fine-Tuning repository! This is a curated list of resources, tools, and information specifically about fine-tuning. Our focus is on the latest techniques and tools that make fine-tuning LLaMA models more accessible and efficient.

Table of Contents

• Awesome Fine-Tuning
  • Table of Contents
  • Tools and Frameworks
  • Tutorials and Guides
  • Data Preparation
  • Optimization Techniques
  • Evaluation and Quality Measurement
  • Best Practices
  • Contributing

Tools and Frameworks

A list of cutting-edge tools and frameworks used for fine-tuning LLaMA models:

• Hugging Face Transformers
  • Offers easy-to-use interfaces for working with models
• Unsloth
  • Accelerates fine-tuning of LLaMA models with optimized kernels
• Axolotl
  • Simplifies the process of fine-tuning LLaMA and other large language models
• PEFT (Parameter-Efficient Fine-Tuning)
  • Implements efficient fine-tuning methods like LoRA, prefix tuning, and P-tuning
• bitsandbytes
  • Enables 4-bit and 8-bit quantization for memory-efficient fine-tuning

Tutorials and Guides

Step-by-step tutorials and comprehensive guides on fine-tuning LLaMA:

• Fine-tuning LLaMA 3 with Hugging Face Transformers
• Efficient Fine-Tuning of LLaMA using Unsloth
• Custom Dataset Fine-Tuning with Axolotl
• Implementing LoRA for LLaMA Fine-Tuning

Data Preparation

Resources and techniques for preparing data to fine-tune LLaMA models:

• Creating High-Quality Datasets for LLaMA Fine-Tuning
• Data Cleaning and Preprocessing for LLM Fine-Tuning
• Techniques for Handling Limited Datasets

Optimization Techniques

Methods to optimize the fine-tuning process for LLaMA models:

• Quantization Techniques for Memory-Efficient Fine-Tuning
• LoRA: Low-Rank Adaptation for Fast Fine-Tuning
• Gradient Checkpointing to Reduce Memory Usage

Evaluation and Quality Measurement

Methods and metrics for evaluating the quality of fine-tuned LLaMA models:

• Perplexity and Other Language Model Metrics
• Task-Specific Evaluation for Fine-Tuned Models
• Human Evaluation Strategies for LLM Outputs

Best Practices

Tips and best practices for effective LLaMA fine-tuning:

• Choosing the Right LLaMA Model Size for Your Task
• Hyperparameter Tuning for LLaMA Fine-Tuning
• Ethical Considerations in LLM Fine-Tuning

Contributing

We welcome contributions to this repository! If you have resources, tools, or information to add about fine-tuning, please follow these steps:

1. Fork the repository
2. Create a new branch (git checkout -b add-new-resource)
3. Add your changes
4. Commit your changes (git commit -am 'Add new resource')
5. Push to the branch (git push origin add-new-resource)
6. Create a new Pull Request

Please ensure your contribution is relevant to fine-tuning and provides value to the community.

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We hope you find this repository helpful in your LLaMA fine-tuning journey. If you have any questions or suggestions, please open an issue or contribute to the discussions.

Happy fine-tuning!