# peremartra/Large-Language-Model-Notebooks-Course

Practical course about Large Language Models.

Repository: https://github.com/peremartra/Large-Language-Model-Notebooks-Course
Canonical: https://ross.abutalabs.com/products/large-language-model-notebooks-course
Homepage: https://medium.com/@peremartra/list/large-language-models-practical-course-66b4ce5943ce
Language: Jupyter Notebook
License: MIT
License Family: permissive
Topics: chatbots, hf, huggingface, langchain, large-language-models, transformers, vector-database, fine-tuning-llm, peft-fine-tuning-llm, pruning
Last push: 2026-05-28T06:32:56+00:00

## Health v2 (maintenance only)
Score: 67/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 84, release rhythm 35, longevity 87
- inputs: {"age_days": 1220, "days_push": 97, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1821, forks 448 (observed 2026-08-28T04:05:41.016846+00:00)

## What it is
A free, hands-on Jupyter Notebook course on building applications with Large Language Models, covering OpenAI and Hugging Face models, LangChain, vector databases, and fine-tuning techniques like LoRA and QLoRA. It serves as the unofficial companion repository to the Apress book 'Large Language Models: Projects' and is continuously updated with new lessons and projects.

## Use cases
- learn how to fine-tune large language models with LoRA and QLoRA
- build chatbots using LangChain and Hugging Face models
- understand how to use vector databases for retrieval-augmented generation
- practice prompt engineering and soft prompt tuning with notebooks
- evaluate and compare LLM performance
- learn knowledge distillation and model pruning techniques
- get hands-on projects for applying LLMs in enterprise settings

## When to choose
- you want a free, practical, notebook-based introduction to working with LLMs
- you prefer learning through small projects grounded in published papers
- you want coverage of modern fine-tuning methods like PEFT, LoRA, and QLoRA
- you are reading the Apress LLM book and want updated, extended examples

## When to avoid
- you need a production-ready library or framework rather than educational material
- you want a complete, polished curriculum with no ongoing changes
- you need comprehensive theory that only the companion book provides
- you require official support or a stable, versioned course syllabus

## Facets
- artifact type: learning-resource
- maturity: active
- function: machine-learning, llm-training, rag, chatbot, prompt-engineering
- domain: large-language-models, machine-learning, artificial-intelligence, tutorials
- platform: python, cross-platform
- tags: llm-course, jupyter-notebooks, fine-tuning, peft, lora, qlora, langchain, huggingface, vector-databases, knowledge-distillation, llmops, natural-language-processing

## Member repositories
- peremartra/Large-Language-Model-Notebooks-Course (main) score 67

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:41.016846+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:19:57.464556+00:00, confidence not recorded.
  - readme: https://github.com/peremartra/Large-Language-Model-Notebooks-Course (fetched 2026-08-28T04:05:41.016846+00:00, sha 038acc758ebe)
- Data as of 2026-08-30T08:39:29.467469+00:00.