# R6410418/Jackrong-llm-finetuning-guide

Repository: https://github.com/R6410418/Jackrong-llm-finetuning-guide
Canonical: https://ross.abutalabs.com/products/jackrong-llm-finetuning-guide
Homepage: https://r6410418.github.io/Jackrong-llm-finetuning-guide/
Language: Jupyter Notebook
License: Apache-2.0
License Family: permissive
Topics: dataset, deepseek, fine-tuning, guide, llama3, llm, machine-learning, nlp, openai, pytorch, qwen, unsloth
Last push: 2026-07-11T05:10:47+00:00

## Health v2 (maintenance only)
Score: 56/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 92, release rhythm 35, longevity 10
- inputs: {"age_days": 150, "days_push": 53, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, young
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1665, forks 268 (observed 2026-08-28T04:05:18.981854+00:00)

## What it is
An open-source educational knowledge base covering LLM fine-tuning, dataset distillation, reinforcement learning workflows (SFT, GRPO, GSPO), and local GGUF deployment. It includes training recipe catalogs, data-processing scripts, curated dataset lists, and multi-language beginner guides built around PyTorch, Unsloth, and Hugging Face.

## Use cases
- learn how to fine-tune an llm with lora
- prepare and distill training data for sft
- run grpo or gspo reinforcement learning training
- convert a qwen model to gguf for local inference
- find curated reasoning and coding datasets
- fine-tune a model in google colab for free
- export a fine-tuned model to 16-bit or gguf

## When to choose
- you are a beginner wanting reproducible, end-to-end fine-tuning tutorials
- you want ready-made training scripts for Qwen, Llama, or DeepSeek models
- you need guidance on data preparation, distillation, and RL post-training
- you want to deploy fine-tuned models locally via GGUF

## When to avoid
- you need a production training framework rather than educational material
- you require guaranteed up-to-date coverage of the latest model architectures
- you want a maintained software library with an API instead of notebooks and guides

## Facets
- artifact type: learning-resource
- maturity: active
- function: llm-training, rag, data-generation, documentation
- domain: large-language-models, machine-learning, tutorials
- platform: python, cloud
- tags: fine-tuning, lora, peft, unsloth, gguf, grpo, gspo, sft, dataset-distillation, qwen, llama3, deepseek, colab-notebooks, beginner-friendly, natural-language-processing, gpu

## Member repositories
- R6410418/Jackrong-llm-finetuning-guide (main) score 56

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:18.981854+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 2026-08-30T03:43:00.984919+00:00, confidence not recorded.
  - readme: https://github.com/R6410418/Jackrong-llm-finetuning-guide (fetched 2026-08-28T04:05:18.981854+00:00, sha 130a0e94bd53)
  - homepage: https://r6410418.github.io/Jackrong-llm-finetuning-guide/ (fetched 2026-08-29T11:16:27.509308+00:00, sha 39d0997cc89f)
- Data as of 2026-08-30T08:39:29.467469+00:00.