Fine-Tuning LLMs (Gemma, LLaMA, Mistral, etc.) A practical guide to fine-tuning various large language models using popular frameworks. Includes examples, scripts, and tips for efficient training on custom datasets.
Last updated Jul 18, 2026
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README
๐ง Fine-Tuning LLMs (Gemma, LLaMA, Mistral, and More)
Welcome to this curated repository showcasing the fine-tuning of various open-source large language models (LLMs) such as Gemma, LLaMA, Mistral, and others using Hugging Face Transformers, PEFT (LoRA/QLoRA), and other modern libraries.
This repo is designed for researchers, ML engineers, and enthusiasts looking to explore or build on top of custom fine-tuned LLMs.
๐ง Features
- โ Fine-tuning with Hugging Face Trainer and PEFT (LoRA / QLoRA)
- โ Dataset loading and preprocessing
- โ Tokenization and model configuration
- โ Evaluation with custom metrics
- โ Easy-to-edit configs for reproducibility
- โ Support for mixed precision (fp16, bf16)
๐ง Contributing
Got improvements, additional models, or tips? Contributions are welcome! Just open an issue or submit a pull request.
