# DjangoPeng/LLM-quickstart

Quick Start for Large Language Models (Theoretical Learning and Practical Fine-tuning) 大语言模型快速入门（理论学习与微调实战）

Repository: https://github.com/DjangoPeng/LLM-quickstart
Canonical: https://ross.abutalabs.com/products/llm-quickstart
Language: Jupyter Notebook
License: Apache-2.0
License Family: permissive
Last push: 2025-06-09T03:45:01+00:00

## Health v2 (maintenance only)
Score: 38/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 25, release rhythm 35, longevity 71
- inputs: {"age_days": 996, "days_push": 450, "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 1056, forks 588 (observed 2026-08-28T04:03:24.510996+00:00)

## What it is
A quickstart learning resource for large language models combining theoretical study with hands-on fine-tuning practice, delivered as Jupyter Notebooks. It includes environment setup guidance for GPU servers (CUDA, drivers) and practical training examples.

## Use cases
- learn how large language models work from theory to practice
- fine-tune an LLM on my own data
- set up a GPU environment for LLM training
- get started with LLM fine-tuning as a beginner
- find hands-on LLM training notebooks
- understand LLM theory and then apply it

## When to choose
- you want a structured, notebook-based introduction to LLM fine-tuning
- you have access to a GPU with at least 16GB VRAM
- you prefer learning theory alongside practical exercises

## When to avoid
- you need production-ready training infrastructure rather than educational material
- you have no GPU available
- you need a maintained library with an API rather than a course

## Facets
- artifact type: learning-resource
- maturity: active
- function: llm-training, machine-learning, deep-learning
- domain: large-language-models, machine-learning, tutorials, deep-learning
- platform: python
- tags: llm-fine-tuning, jupyter-notebooks, hands-on-course, chinese-language, quickstart, linux, gpu

## Member repositories
- DjangoPeng/LLM-quickstart (main) score 38

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:24.510996+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 2026-08-30T06:58:14.407785+00:00, confidence not recorded.
  - readme: https://github.com/DjangoPeng/LLM-quickstart (fetched 2026-08-28T04:03:24.510996+00:00, sha 549be4bd7dda)
- Data as of 2026-08-30T08:39:29.467469+00:00.