# Resources For Self-Guided Learning

The lesson was built using core resources from OpenAI and Microsoft Foundry as references for the terminology and tutorials. Here is a non-comprehensive list for your own self-guided learning journeys. Every link below points to current, supported material.

## 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 costs, improving response quality, and enabling lower-latency requests. **Get an overview of fine-tuning from OpenAI.** |
| [When to use Microsoft Foundry fine-tuning](https://learn.microsoft.com/azure/ai-foundry/openai/concepts/fine-tuning-considerations?WT.mc_id=academic-105485-koreyst) | Understand **what fine-tuning is (concept)**, why you should consider it, what data to use, and how to measure quality - plus when SFT, DPO, or RFT is the right fit. |
| [Customize a model with fine-tuning](https://learn.microsoft.com/azure/ai-foundry/openai/how-to/fine-tuning?WT.mc_id=academic-105485-koreyst) | The end-to-end **how-to (process)** for fine-tuning in Microsoft Foundry using the portal, the OpenAI / Foundry Python SDK, or the REST API - covering data prep, training, checkpoints, and deployment. |
| [Continuous fine-tuning](https://learn.microsoft.com/azure/ai-foundry/openai/how-to/fine-tuning?WT.mc_id=academic-105485-koreyst#perform-continuous-fine-tuning) | The iterative process of selecting an already fine-tuned model as the base model and **fine-tuning it further** on new sets of training examples. |
| [Fine-tuning with tool (function) calling](https://learn.microsoft.com/azure/ai-foundry/openai/how-to/fine-tuning-functions?WT.mc_id=academic-105485-koreyst) | Fine-tuning your model **with tool-calling examples** improves output - more accurate, consistent, similarly-formatted responses using fewer prompt tokens. |
| [Fine-tuning models: Microsoft Foundry guidance](https://learn.microsoft.com/azure/ai-foundry/foundry-models/concepts/models-sold-directly-by-azure?WT.mc_id=academic-105485-koreyst#fine-tuning-models) | Look up **which models can be fine-tuned**, the methods they support (SFT / DPO / RFT), and the regions where they're available. |
| [Fine-tuning overview: techniques and modalities](https://learn.microsoft.com/azure/ai-foundry/concepts/fine-tuning-overview?WT.mc_id=academic-105485-koreyst) | Compare the three training techniques (SFT, DPO, RFT) and the two modalities (serverless vs. managed compute), with guidance on choosing a base model and getting started. |
| **Tutorial**: [Fine-tune a model in Microsoft Foundry](https://learn.microsoft.com/azure/ai-foundry/openai/tutorials/fine-tune?WT.mc_id=academic-105485-koreyst) | Create a sample dataset, prepare for fine-tuning, run a fine-tuning job on a currently supported model such as `gpt-4.1-mini`, and deploy the fine-tuned model on Azure. |
| **Tutorial**: [Fine-tune models with serverless API deployments](https://learn.microsoft.com/azure/ai-foundry/how-to/fine-tune-serverless?WT.mc_id=academic-105485-koreyst) | Tailor open and partner models (Phi, Llama, Mistral, and more) to your datasets _using a low-code, UI-based workflow_ in Microsoft Foundry. |
| **Tutorial**: [Fine-tune Hugging Face models on Azure Databricks](https://learn.microsoft.com/azure/databricks/machine-learning/train-model/huggingface/fine-tune-model?WT.mc_id=academic-105485-koreyst) | Fine-tune a Hugging Face model with the `transformers` library on a single GPU using Azure Databricks and the Hugging Face Trainer. |
| **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 Azure Machine Learning model catalog offers many open-source models you can fine-tune. Part of the [Azure ML 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 with Weights & Biases](https://docs.wandb.ai/guides/integrations/azure-openai-fine-tuning?WT.mc_id=academic-105485-koreyst) | Track and analyze fine-tuning runs on Azure with W&B. Extends the OpenAI fine-tuning guide with Azure-specific steps and experiment tracking. |

## 2. Secondary Resources

This section captures additional resources worth exploring that we didn't have time to cover in the lesson. Use them to build your own expertise 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) | Preprocess and analyze a chat dataset before fine-tuning: check for format errors, get basic statistics, and estimate token counts (and cost). Pairs with the [OpenAI fine-tuning guide](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) | A comprehensive example of fine-tuning OpenAI models for RAG, integrating Qdrant and few-shot learning to boost 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) | Use W&B to track model training and fine-tuning. 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. |
| **Hugging Face Tutorial**: [How to Fine-Tune LLMs with Hugging Face TRL](https://www.philschmid.de/fine-tune-llms-in-2024-with-trl?WT.mc_id=academic-105485-koreyst) | Fine-tune open LLMs using Hugging Face TRL, Transformers, and datasets: define a use case, set up a dev environment, prepare a dataset, fine-tune, evaluate, and deploy. |
| **Hugging Face**: [AutoTrain Advanced](https://github.com/huggingface/autotrain-advanced?WT.mc_id=academic-105485-koreyst) | A no-code / low-code library from Hugging Face for fine-tuning many model types. Run it in your own cloud, on Hugging Face Spaces, or locally via GUI, CLI, or YAML config. |
| **Unsloth**: [Fine-tuning LLMs Guide](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide) | An open-source framework that streamlines local LLM fine-tuning and reinforcement learning, with ready-to-use [notebooks](https://github.com/unslothai/notebooks?WT.mc_id=academic-105485-koreyst). |