Synthetic Data Generation and Fine-tuning for OpenAI GPT-4 or Llama 3

Overview

Dive into an extensive tutorial on synthetic data generation and fine-tuning techniques for large language models like OpenAI GPT-4o and Llama 3. Learn how to create synthetic questions and answers, implement chain of thought reasoning, and augment data from various sources including documents and structured data. Explore GPU setup, data extraction from PDFs, and the process of fine-tuning both OpenAI and open-source models. Master advanced concepts such as LoRA adapters, custom learning schedules, and performance evaluation methods. Discover strategies to improve model performance in specific domains like mathematics using augmented synthetic datasets. Gain practical insights on leveraging user conversations and feedback to enhance model capabilities.

Syllabus

How to generate synthetic data for fine-tuning
Video Overview fine-tune OpenAI or Llama 3
: Synthetic Question Generation
Synthetic Answer Generation
Why chain of thought is important in Synthetic Data
Augmented Synthetic Data
Generating Synthetic Data from Documents
Synthetic Data from Structured Data
Generating data from user conversations
GPU and notebook setup for Notebooks
OpenAI Notebook: Data Generation and Fine-tuning
Data extraction from pdfs
Synthetic Data Generation for GPT-4o-mini fine-tuning
Generating synthetic questions using structured outputs
Generating synthetic answers
Saving data in jsonl format for OpenAI fine-tuning
How to fine-tune an openai model on a synthetic dataset
Using an LLM as a judge for evaluation
Evaluation of gpt-4o-mini versus fine-tuned model
How to increase and improve the training data
Fine-tuning Open Source Models like Llama 3
Pushing a synthetic dataset to HuggingFace
Loading a model with transformers or Unsloth
Setting generation parameters incl. temperature and top p
Batch generation with transformers or unsloth, incl. padding and chat templating
Llama 3.2 8B model performance before fine-tuning
Fine-tuning on synthetic data with unsloth or transformers
LoRA adapter setup, rescaled LoRa, choice of rank and alpha
Dataset preparation for fine-tuning, incl. prompt formatting
SFTTrainer trainer setup incl. epochs, batch size, gradient accumulation
Defining a custom learning schedule with annealing
How to train on completions only like openai’s default
Running training on Llama 3.2 1B
Performance evaluation after fine-tuning Llama 3.2
Using augmented synthetic data to improve Maths performance Advanced / Speculative!
Evaluating the baseline maths performance of Llama 3.2 1B
Fine-tuning on a training split of the lighteval/MATH dataset
Training on synthetic data from Llama 3.1 8B instead of the training split
Comparing results of training on a training split vs on synthetic Llama 3.1 8B answers
Training on an augmented synthetic dataset generated with Llama 3.1 8B and ground truth answers
Comparing all results, base vs fine-tuned on the raw dataset vs 8B synth vs 8B synth with augmentation
How to use augmented data if you have access to user conversations or feedback