└── README.md


/README.md:
--------------------------------------------------------------------------------
 1 | # Awesome Fine-Tuning
 2 | 
 3 | 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.
 4 | 
 5 | ## Table of Contents
 6 | 
 7 | - [Awesome Fine-Tuning](#awesome-fine-tuning)
 8 |   - [Table of Contents](#table-of-contents)
 9 |   - [Tools and Frameworks](#tools-and-frameworks)
10 |   - [Tutorials and Guides](#tutorials-and-guides)
11 |   - [Data Preparation](#data-preparation)
12 |   - [Optimization Techniques](#optimization-techniques)
13 |   - [Evaluation and Quality Measurement](#evaluation-and-quality-measurement)
14 |   - [Best Practices](#best-practices)
15 |   - [Contributing](#contributing)
16 | 
17 | ## Tools and Frameworks
18 | 
19 | A list of cutting-edge tools and frameworks used for fine-tuning LLaMA models:
20 | 
21 | - [Hugging Face Transformers](https://github.com/huggingface/transformers)
22 |   - Offers easy-to-use interfaces for working with models
23 | - [Unsloth](https://github.com/unslothai/unsloth)
24 |   - Accelerates fine-tuning of LLaMA models with optimized kernels
25 | - [Axolotl](https://github.com/OpenAccess-AI-Collective/axolotl)
26 |   - Simplifies the process of fine-tuning LLaMA and other large language models
27 | - [PEFT (Parameter-Efficient Fine-Tuning)](https://github.com/huggingface/peft)
28 |   - Implements efficient fine-tuning methods like LoRA, prefix tuning, and P-tuning
29 | - [bitsandbytes](https://github.com/TimDettmers/bitsandbytes)
30 |   - Enables 4-bit and 8-bit quantization for memory-efficient fine-tuning
31 | 
32 | ## Tutorials and Guides
33 | 
34 | Step-by-step tutorials and comprehensive guides on fine-tuning LLaMA:
35 | 
36 | - Fine-tuning LLaMA 3 with Hugging Face Transformers
37 | - Efficient Fine-Tuning of LLaMA using Unsloth
38 | - Custom Dataset Fine-Tuning with Axolotl
39 | - Implementing LoRA for LLaMA Fine-Tuning
40 | 
41 | ## Data Preparation
42 | 
43 | Resources and techniques for preparing data to fine-tune LLaMA models:
44 | 
45 | - Creating High-Quality Datasets for LLaMA Fine-Tuning
46 | - Data Cleaning and Preprocessing for LLM Fine-Tuning
47 | - Techniques for Handling Limited Datasets
48 | 
49 | ## Optimization Techniques
50 | 
51 | Methods to optimize the fine-tuning process for LLaMA models:
52 | 
53 | - Quantization Techniques for Memory-Efficient Fine-Tuning
54 | - LoRA: Low-Rank Adaptation for Fast Fine-Tuning
55 | - Gradient Checkpointing to Reduce Memory Usage
56 | 
57 | ## Evaluation and Quality Measurement
58 | 
59 | Methods and metrics for evaluating the quality of fine-tuned LLaMA models:
60 | 
61 | - Perplexity and Other Language Model Metrics
62 | - Task-Specific Evaluation for Fine-Tuned Models
63 | - Human Evaluation Strategies for LLM Outputs
64 | 
65 | ## Best Practices
66 | 
67 | Tips and best practices for effective LLaMA fine-tuning:
68 | 
69 | - Choosing the Right LLaMA Model Size for Your Task
70 | - Hyperparameter Tuning for LLaMA Fine-Tuning
71 | - Ethical Considerations in LLM Fine-Tuning
72 | 
73 | ## Contributing
74 | 
75 | We welcome contributions to this repository! If you have resources, tools, or information to add about fine-tuning, please follow these steps:
76 | 
77 | 1. Fork the repository
78 | 2. Create a new branch (`git checkout -b add-new-resource`)
79 | 3. Add your changes
80 | 4. Commit your changes (`git commit -am 'Add new resource'`)
81 | 5. Push to the branch (`git push origin add-new-resource`)
82 | 6. Create a new Pull Request
83 | 
84 | Please ensure your contribution is relevant to fine-tuning and provides value to the community.
85 | 
86 | ---
87 | 
88 | 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.
89 | 
90 | Happy fine-tuning!


--------------------------------------------------------------------------------