How to Fine-Tune Llama 3 with Custom Data on Colab скачать в хорошем качестве

How to Fine-Tune Llama 3 with Custom Data on Colab

Want to fine-tune a powerful LLM like Llama 3 for a specific task but don't have a supercomputer? This video shows you how to use the Unsloth framework to make the fine-tuning process up to 2x faster and use 70% less VRAM. We'll walk through every step, from setting up the environment and loading the model to preparing a custom dataset, applying LoRA adapters for efficiency, and running the training. By the end, you'll know how to create a specialized Llama 3 model that can handle your unique tasks, all on a free Google Colab GPU! Unsloth GitHub - https://github.com/unslothai/unsloth Timestamps: 00:00 - Intro: The Challenge of Fine-Tuning 00:30 - Setup Colab Notebook 00:57 - Step 1: Install Dependencies 02:07 - Step 2: Load the Llama 3 Model and Tokenizer 02:58 - Step 3: Prepare Your Custom Dataset 04:38 - Step 4: Add LoRA Adapters 05:59 - Step 5: Configure and Run the SFTTrainer 07:07 - Step 6: Test Your Fine-tuned Model 09:45 - Outro