---
title: "Resources For Self-Guided Learning"
source: Microsoft Generative AI for Beginners
source_url: https://github.com/microsoft/generative-ai-for-beginners
licence: MIT
domain: agentic-ai
subdomain: ms-gen-ai-beginners
date_added: 2026-04-25
---

# 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-comprehensive list, for your own self-guided learning journeys.

## 1. Primary Resources

| Title/Link                                                                                                                                                                                                                   | Description                                                                                                                                                                                                                                                                                                                   |
| :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| [Fine-tuning with OpenAI Models](https://platform.openai.com/docs/guides/fine-tuning?WT.mc_id=academic-105485-koreyst)                                                                                                       | 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 OpenAI?](https://learn.microsoft.com/azure/ai-services/openai/concepts/fine-tuning-considerations#what-is-fine-tuning-with-azure-openai?WT.mc_id=academic-105485-koreyst)                   | 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](https://learn.microsoft.com/azure/ai-services/openai/how-to/fine-tuning?tabs=turbo%2Cpython&pivots=programming-language-studio#continuous-fine-tuning?WT.mc_id=academic-105485-koreyst) | 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](https://learn.microsoft.com/ai/playbook/technology-guidance/generative-ai/working-with-llms/fine-tuning-recommend?WT.mc_id=academic-105485-koreyst)                                    | 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](https://learn.microsoft.com/azure/ai-services/openai/how-to/fine-tuning?tabs=turbo%2Cpython&pivots=programming-language-studio#continuous-fine-tuning?WT.mc_id=academic-105485-koreyst)             | 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](https://learn.microsoft.com/azure/ai-services/openai/how-to/fine-tuning-functions?WT.mc_id=academic-105485-koreyst)                                                                       | 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](https://learn.microsoft.com/azure/ai-services/openai/concepts/models#fine-tuning-models?WT.mc_id=academic-105485-koreyst)                                                        | 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](https://learn.microsoft.com/shows/ai-show/to-fine-tune-or-not-fine-tune-that-is-the-question?WT.mc_id=academic-105485-koreyst)                                      | 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](https://learn.microsoft.com/ai/playbook/technology-guidance/generative-ai/working-with-llms/fine-tuning-recommend?WT.mc_id=academic-105485-koreyst)                                             | 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](https://learn.microsoft.com/azure/ai-services/openai/tutorials/fine-tune?tabs=python%2Ccommand-line?WT.mc_id=academic-105485-koreyst)                                  | 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](https://learn.microsoft.com/azure/ai-studio/how-to/fine-tune-model-llama?WT.mc_id=academic-105485-koreyst)                                                      | 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](https://learn.microsoft.com/azure/databricks/machine-learning/train-model/huggingface/fine-tune-model?WT.mc_id=academic-105485-koreyst)               | 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](https://learn.microsoft.com/training/modules/finetune-foundation-model-with-azure-machine-learning/?WT.mc_id=academic-105485-koreyst)         | 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](https://learn.microsoft.com/training/paths/work-with-generative-models-azure-machine-learning/?WT.mc_id=academic-105485-koreyst) |
| **Tutorial:** [Azure OpenAI Fine-Tuning](https://docs.wandb.ai/guides/integrations/azure-openai-fine-tuning?WT.mc_id=academic-105485-koreyst)                                                                                | 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](https://cookbook.openai.com/examples/chat_finetuning_data_prep?WT.mc_id=academic-105485-koreyst)                                      | 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](https://platform.openai.com/docs/guides/fine-tuning?WT.mc_id=academic-105485-koreyst).                                                                                                                                                                   |
| **OpenAI Cookbook**: [Fine-Tuning for Retrieval Augmented Generation (RAG) with Qdrant](https://cookbook.openai.com/examples/fine-tuned_qa/ft_retrieval_augmented_generation_qdrant?WT.mc_id=academic-105485-koreyst) | 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](https://cookbook.openai.com/examples/third_party/gpt_finetuning_with_wandb?WT.mc_id=academic-105485-koreyst)                                             | 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](https://docs.wandb.ai/guides/integrations/openai-fine-tuning/?WT.mc_id=academic-105485-koreyst) guide first, then try the Cookbook exercise.                                                                                                                                                                                                                  |
| **Community Tutorial** [Phinetuning 2.0](https://huggingface.co/blog/g-ronimo/phinetuning?WT.mc_id=academic-105485-koreyst) - fine-tuning for Small Language Models                                                   | Meet [Phi-2](https://www.microsoft.com/research/blog/phi-2-the-surprising-power-of-small-language-models/?WT.mc_id=academic-105485-koreyst), 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](https://www.philschmid.de/fine-tune-llms-in-2024-with-trl?WT.mc_id=academic-105485-koreyst)                                               | 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](https://github.com/huggingface/autotrain-advanced?WT.mc_id=academic-105485-koreyst)**                                                                                            | Brings faster and easier training and deployments of [state-of-the-art machine learning models](https://twitter.com/abhi1thakur/status/1755167674894557291?WT.mc_id=academic-105485-koreyst). Repo has Colab-friendly tutorials with YouTube video guidance, for fine-tuning. **Reflects recent [local-first](https://twitter.com/abhi1thakur/status/1750828141805777057?WT.mc_id=academic-105485-koreyst) update** . Read the [AutoTrain documentation](https://huggingface.co/autotrain?WT.mc_id=academic-105485-koreyst) |
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