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Resources For Self-Guided Learning

The lesson was built using a number of core resources from OpenAI and Azure OpenAI as references for the terminology and tutorials. Here is a non-comprehenisve list, for your own self-guided learning journeys.

1. Primary Resources

Title/Link Description
Fine-tuning with OpenAI Models Fine-tuning improves on few-shot learning by training on many more examples than can fit in the prompt, saving you costs, improving response quality, and enabling lower-latency requests. Get an overview of fine-tuning from OpenAI.
What is Fine-Tuning with Azure Open AI? Understand what fine-tuning is (concept), why you should look at it (motivating problem), what data to use (training) and measuring the quality
Customize a model with fine-tuning Azure OpenAI Service lets you tailor our models to your personal datasets using fine-tuning. Learn how to fine-tune (process) select models using Azure AI Studio, Python SDK or REST API.
Recommendations for LLM fine-tuning LLMs may not perform well on specific domains, tasks, or datasets, or may produce inaccurate or misleading outputs. When should you consider fine-tuning as a possible solution to this?
Continuous Fine Tuning Continuous fine-tuning is the iterative process of selecting an already fine-tuned model as a base model and fine-tuning it further on new sets of training examples.
Fine-tuning and function calling Fine-tuning your model with function calling examples can improve model output by getting more accurate and consistent outputs - with similarly-formatted responses & cost-savings
Fine-tuning Models: Azure OpenAI Guidance Look up this table to understand what models can be fine-tuned in Azure OpenAI, and which regions these are available in. Look up their token limits and training data expiry dates if needed.
To Fine Tune or Not To Fine Tune? That is the Question This 30-min Oct 2023 episode of the AI Show discusses benefits, drawbacks and practical insights that help you make this decision.
Getting Started With LLM Fine-Tuning This AI Playbook resource walks you through data requirements, formatting, hyperparameter fine-tuning and challenges/limitations you should know.
Tutorial: Azure OpenAI GPT3.5 Turbo Fine-Tuning Learn to create a sample fine-tuning dataset, prepare for fine-tuning, create a fine-tuning job, and deploy the fine-tuned model on Azure.
Tutorial: Fine-tune a Llama 2 model in Azure AI Studio Azure AI Studio lets you tailor large language models to your personal datasets using a UI-based workflow suitable for low-code developers. See this example.
Tutorial:Fine-tune Hugging Face models for a single GPU on Azure This article describes how to fine-tune a Hugging Face model with the Hugging Face transformers library on a single GPU with Azure DataBricks + Hugging Face Trainer libraries
Training: Fine-tune a foundation model with Azure Machine Learning The model catalog in Azure Machine Learning offers many open source models you can fine-tune for your specific task. Try this module is from the AzureML Generative AI Learning Path
Tutorial: Azure OpenAI Fine-Tuning Fine-tuning GPT-3.5 or GPT-4 models on Microsoft Azure using W&B allows for detailed tracking and analysis of model performance. This guide extends the concepts from the OpenAI Fine-Tuning guide with specific steps and features for Azure OpenAI.

2. Secondary Resources

This section captures additional resources that are worth exploring, but that we did not have time to cover in this lesson. They may be covered in a future lesson, or as a secondary assignment option, at a later date. For now, use them to build your own expertise and knowledge around this topic.

Title/Link Description
OpenAI Cookbook: Data preparation and analysis for chat model fine-tuning This notebook serves as a tool to preprocess and analyze the chat dataset used for fine-tuning a chat model. It checks for format errors, provides basic statistics, and estimates token counts for fine-tuning costs. See: Fine-tuning method for gpt-3.5-turbo.
OpenAI Cookbook: Fine-Tuning for Retrieval Augmented Generation (RAG) with Qdrant The aim of this notebook is to walk through a comprehensive example of how to fine-tune OpenAI models for Retrieval Augmented Generation (RAG). We will also be integrating Qdrant and Few-Shot Learning to boost model performance and reduce fabrications.
OpenAI Cookbook: Fine-tuning GPT with Weights & Biases Weights & Biases (W&B) is the AI developer platform, with tools for training models, fine-tuning models, and leveraging foundation models. Read their OpenAI Fine-Tuning guide first, then try the Cookbook exercise.
Community Tutorial Phinetuning 2.0 - fine-tuning for Small Language Models Meet Phi-2, Microsoft’s new small model, remarkably powerful yet compact. This tutorial will guide you through fine-tuning Phi-2, demonstrating how to build a unique dataset and fine-tune model using QLoRA.
Hugging Face Tutorial How to Fine-Tune LLMs in 2024 with Hugging Face This blog post walks you thorugh how to fine-tune open LLMs using Hugging Face TRL, Transformers & datasets in 2024. You define a use case, setup a dev environment, prepare a dataset, fine tune the model, test-evaluate it, then deploy it to production.
Hugging Face: AutoTrain Advanced Brings faster and easier training and deployments of state-of-the-art machine learning models. Repo has Colab-friendly tutorials with YouTube video guidance, for fine-tuning. Reflects recent local-first update . Read the AutoTrain documentation