Free course · by Debugshala, Indore
Hugging Face and Fine-tuning Tutorial
7 free lessons, in the order we teach them in class: start at lesson 1 and work down. Every lesson has an analogy, a diagram, code you can run or copy, a quick quiz and an FAQ.
- 7Lessons
- 1Modules
- ~1 hReading time
- FreeAlways
01Hugging Face and Open Models
- Hugging Face Tutorial: Hub, Transformers, Pipelines and SpacesLearn Hugging Face step by step: the Hub, model cards and licences, tokenizers, pipeline(), AutoModel, datasets, hosted inference and Gradio Spaces.
- Open-Source LLMs Explained: Sizes, Quantisation and LicencesOpen-source LLMs explained: open-weight vs closed models, parameter sizes, what fits on a laptop, 4-bit quantisation maths, GGUF and ONNX, and licences.
- Fine-Tuning LLMs with LoRA: When, Why and How It WorksWhen to fine-tune an LLM instead of prompting or RAG, how LoRA and QLoRA work, how to prepare instruction data, and a transformers plus peft example.
- Prompting vs RAG vs Fine-tuning: How to Choose for an LLM AppPrompting vs RAG vs fine-tuning: diagnose failures first, compare cost per month, and learn when to combine them, with runnable Python decision tools.
- Instruction Dataset Creation for LLM Fine-tuning: Practical GuideBuild an instruction fine-tuning dataset: coverage grids, synthetic data, quality filters, near-duplicate removal and group splits, with Python and TRL code.
- SFT vs RLHF vs GRPO: Choosing an LLM Post-training MethodCompare SFT, RLHF, DPO and GRPO: training signals, data, compute and when to use each, with runnable DPO loss and GRPO advantage code plus TRL examples.
- Gradio and Hugging Face Spaces: Build a Streaming LLM DemoBuild an LLM chat demo with Gradio 6 and deploy it on Hugging Face Spaces: streaming replies, chat history, secrets, ZeroGPU and calling it from Python.