# ashishpatel26/LLM-Finetuning

LLM Finetuning with peft

Repository: https://github.com/ashishpatel26/LLM-Finetuning
Canonical: https://ross.abutalabs.com/products/llm-finetuning
Language: Jupyter Notebook
License Family: other
Topics: falcon, fine-tuning, huggingface, llama, llama2, llm, llms, lora, peft, pytorch, text-generation
Last push: 2025-08-01T12:00:20+00:00

## Health v2 (maintenance only)
Score: 44/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 34, release rhythm 35, longevity 84
- inputs: {"age_days": 1182, "days_push": 397, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2980, forks 771 (observed 2026-08-28T04:07:33.144502+00:00)

## What it is
A collection of Jupyter/Colab notebooks demonstrating efficient fine-tuning of large language models (Llama 2, Falcon, Guanaco) using LoRA and Hugging Face's PEFT library. It serves as a hands-on tutorial resource for parameter-efficient LLM training.

## Use cases
- learn how to fine-tune llama 2 with lora
- peft fine-tuning tutorial notebooks
- train a custom chatbot from llama 7b
- run llm fine-tuning in google colab
- parameter-efficient fine-tuning examples with huggingface
- fine-tune falcon or guanaco models

## When to choose
- you want runnable Colab notebooks for LoRA/PEFT fine-tuning
- you're learning LLM fine-tuning on consumer or free-tier GPUs
- you need examples for Llama 2, Falcon, or Guanaco models

## When to avoid
- you need a production training pipeline or library rather than notebooks
- you require a maintained, licensed software package
- you need full fine-tuning rather than parameter-efficient methods

## Facets
- artifact type: learning-resource
- maturity: active
- function: llm-training, machine-learning, deep-learning
- domain: large-language-models, machine-learning, deep-learning, tutorials
- platform: python, cross-platform
- tags: peft, lora, huggingface, llama2, falcon, jupyter-notebooks, colab, fine-tuning, text-generation, gpu

## Member repositories
- ashishpatel26/LLM-Finetuning (main) score 44

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:33.144502+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 2026-08-30T07:31:30.538920+00:00, confidence not recorded.
  - readme: https://github.com/ashishpatel26/LLM-Finetuning (fetched 2026-08-28T04:07:33.144502+00:00, sha 434bf348cdfe)
- Data as of 2026-08-30T08:39:29.467469+00:00.