This repository provides practical, step-by-step tutorials for fine-tuning various Large Language Models (LLMs) using diverse techniques. It targets engineers and researchers seeking to adapt pre-trained LLMs for specific tasks and domains, offering a hands-on approach to achieve improved performance and specialized capabilities.

How It Works

The project offers a collection of distinct tutorials, each focusing on a specific LLM and fine-tuning methodology. It covers full fine-tuning from scratch, parameter-efficient fine-tuning (PEFT) methods like LoRA, instruction fine-tuning for tasks such as summarization and sentiment analysis, and reasoning fine-tuning using specialized libraries like Unsloth and GRPO. This modular approach allows users to select and apply relevant techniques to their chosen models.

Quick Start & Requirements

• Installation typically involves cloning the repository and installing Python dependencies via pip.
• Specific tutorials may require access to Hugging Face models, potentially large datasets, and GPU acceleration (e.g., CUDA) depending on the model size and fine-tuning method.
• Refer to individual tutorial directories for precise requirements and setup instructions.

Highlighted Details

• Covers a range of LLMs including GPT-2, Falcon-7b, FLAN-T5, Mistral 7B, DeepSeek R1, and Gemma 3.
• Demonstrates both full fine-tuning and parameter-efficient techniques (PEFT, LoRA, GRPO).
• Includes tutorials for specialized tasks like summarization, financial sentiment analysis, and reasoning.
• Features integration with libraries like Hugging Face AutoTrain and Unsloth for optimized fine-tuning.

Maintenance & Community

• The repository is maintained by youssefHosni.
• No specific community channels (Discord, Slack) or roadmap are explicitly mentioned in the README.

Licensing & Compatibility

• The repository's license is not specified in the provided README. Users should verify licensing for any code or models used.
• Compatibility for commercial use or closed-source linking is not detailed.

Limitations & Caveats

The README does not specify the project's license, making commercial use or integration into closed-source projects uncertain without further investigation. It also lacks explicit details on community support or a clear roadmap for future development.