# mshumer/gpt-llm-trainer

Repository: https://github.com/mshumer/gpt-llm-trainer
Canonical: https://ross.abutalabs.com/products/gpt-llm-trainer
Language: Jupyter Notebook
License: MIT
License Family: permissive
Last push: 2025-05-02T02:41:23+00:00

## Health v2 (maintenance only)
Score: 37/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 19, release rhythm 35, longevity 80
- inputs: {"age_days": 1120, "days_push": 488, "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 4177, forks 554 (observed 2026-08-28T04:08:38.265941+00:00)

## What it is
A set of Jupyter/Colab notebooks that automate the LLM fine-tuning pipeline: given a task description, they generate a synthetic dataset with GPT-4/Claude, format it, and fine-tune LLaMA 2 or GPT-3.5. It abstracts dataset creation and training into a single runnable notebook.

## Use cases
- fine-tune llama 2 on a custom task without writing training code
- generate synthetic training data from a task description
- create a task-specific model from scratch easily
- fine-tune gpt-3.5 automatically from generated examples
- train a small model to imitate claude or gpt-4 outputs

## When to choose
- you want a quick, low-code way to fine-tune a model for a specific task
- you lack a dataset and want one generated automatically
- you're experimenting with fine-tuning in Colab

## When to avoid
- you need production-grade, reproducible training pipelines
- you already have a curated dataset and custom training setup
- you need fine-grained control over training hyperparameters

## Facets
- artifact type: learning-resource
- maturity: experimental
- function: llm-training, data-generation, machine-learning
- domain: large-language-models, machine-learning, artificial-intelligence
- platform: python, cloud
- tags: fine-tuning, jupyter-notebook, colab, synthetic-dataset, llama, openai, gpu

## Member repositories
- mshumer/gpt-llm-trainer (main) score 37

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:38.265941+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 2026-08-29T18:22:41.447443+00:00, confidence not recorded.
  - readme: https://github.com/mshumer/gpt-llm-trainer (fetched 2026-08-28T04:08:38.265941+00:00, sha 739af2df280b)
- Data as of 2026-08-30T08:39:29.467469+00:00.