A Detailed Guide to QLoRA Quantization and Fine-Tuning Using Llama 3(Development of Large Model Applications 15)

Discover Meta AI's powerful open-source Llama 3 model with up to 70B parameters, optimized by QLoRA for efficient deployment in resource-constrained environments.

Hello everyone, welcome to the "Development of Large Model Applications" column.

In the Era of Large Model Applications, Everyone Can Be a Programmer (Development of large model applications 1)

Order Management Using OpenAI Assistants' Functions(Development of large model applications 2)

Thread and Run State Analysis in OpenAI Assistants(Development of large model applications 3)

Using Code Interpreter in Assistants for Data Analysis(Development of large model applications 4)

Using the File Search (RAG) Tool in Assistants for Knowledge Retrieval(Development of large model applications 5)

5 Essential Prompt Engineering Tips for AI Model Mastery(Development of large model applications 6)

5 Frameworks to Guide Better Reasoning in Models (Development of Large Model Applications 7)

Using Multi-Step Prompts to Automatically Generate Python Unit Test Code (Development of Large Model Applications 8)

Using Large Models for Natural Language SQL Queries(Development of Large Model Applications 9)

Building a PDF-Based RAG System with Image Recognition (Development of Large Model Applications 10)

Building a Keyword-Based Recommendation System Using Embeddings(Development of Large Model Applications 11)

Strategies for Summarizing and Evaluating Long PDF Documents(Development of Large Model Applications 12)

Generating Business Report PPTs with Assistants' Independent Thinking(Development of Large Model Applications 13)

Explaining Parameter-Efficient Fine-Tuning (PEFT) Using Qwen as an Example(Development of Large Model Applications 14)

In the last class, we used the Qwen model to explore basic methods for efficient fine-tuning of large language model parameters. We focused on the popular LoRA technology and used the PEFT framework to fine-tune the Qwen model with Chinese data in the Alpaca style.

However, some points were not thoroughly explained, such as the mathematical principles behind LoRA and other techniques for compressing large models during fine-tuning. This time, we'll switch to another model, Llama 3, the king of open-source LLMs, to discuss fine-tuning and quantization.