Learn how to easily fine-tune Meta's powerful new Llama 3 language model using Unsloth in this step-by-step tutorial. We cover:
* Overview of Llama 3's 8B and 70B parameter models
* Benchmarks showing Llama 3's strong performance
* How to fine-tune Llama 3 efficiently with Unsloth
* Choosing sequence length based on your data
* Configuring the model, adapter layers, and hyperparameters
* Preparing a custom fine-tuning dataset
* Training the model and monitoring results
* Testing the fine-tuned model on new data
* Saving and publishing your custom model to Hugging Face
Code, example data, and resources provided. Fine-tune Llama 3 for summarization, question-answering, analysis and more. Integrate your model into applications, chatbots, and pipelines.
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Demonstration Diagram and Code: https://github.com/nodematiclabs/llama-3-finetune-unsloth
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0:00 Introduction
1:38 Conceptual Overview
5:31 Initial Setup
7:05 Token Counting
9:57 Model and Quantization
10:23 QLoRA Adapter Layers
11:05 Dataset Preparation
16:43 Trainer Specification
18:43 Inference Testing
21:12 Model Saving (Hugging Face)