Fine-Tuning with Llama 3

The Korshub Take

This intermediate machine learning course walks developers through parameter-efficient fine-tuning for Llama 3 models. Covering end-to-end LLM specialization, it demonstrates data preparation with Hugging Face Datasets alongside resource-optimized training using LoRA, BitsAndBytes quantization, TorchTune, and SFTTrainer.

Who It's For

AI engineers, machine learning practitioners, and Python developers seeking to adapt open-source LLMs like Llama 3 to custom domain tasks efficiently.

Key Takeaways

• Dataset Curation: Master dataset loading, splitting, formatting, and saving pipelines using Hugging Face Datasets for LLM tuning.
• Fine-Tuning Workflows: Configure training recipes and arguments using modern frameworks like PyTorch's TorchTune and Hugging Face SFTTrainer.
• Resource Optimization: Implement Low-Rank Adaptation (LoRA) and BitsAndBytes quantization to fine-tune large models under hardware constraints.