# FareedKhan-dev/train-llm-from-scratch

A straightforward method for training your LLM, from downloading data to generating text.

Repository: https://github.com/FareedKhan-dev/train-llm-from-scratch
Canonical: https://ross.abutalabs.com/products/train-llm-from-scratch
Homepage: https://fareedkhan-dev.github.io/train-llm-from-scratch/
Language: Python
License: MIT
License Family: permissive
Topics: gemini, large-language-models, llm, openai, training, transformers
Last push: 2026-08-17T05:07:26+00:00

## Health v2 (maintenance only)
Score: 65/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 98, release rhythm 35, longevity 42
- inputs: {"age_days": 598, "days_push": 16, "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 9432, forks 1306 (observed 2026-08-28T04:10:31.141924+00:00)

## What it is
An educational repository and tutorial that implements a transformer-based LLM entirely from scratch in plain PyTorch, covering the full pipeline from raw text data to an aligned, reasoning-style model. It walks through pretraining, SFT, reward modeling, and preference optimization (DPO, PPO, GRPO) without relying on libraries like transformers, trl, or peft.

## Use cases
- learn how transformers work by implementing attention from scratch
- train a small LLM on a single GPU
- understand RLHF alignment algorithms like DPO and PPO
- build a next-token prediction model from raw text
- study the full LLM training pipeline end to end
- implement reward models and GRPO in plain PyTorch

## When to choose
- you want to deeply understand LLM internals rather than use high-level libraries
- you are learning how pretraining, SFT, and RLHF fit together
- you need a from-scratch PyTorch reference implementation of transformer training
- you want to train a small (million to billion parameter) model on limited hardware

## When to avoid
- you need a production-ready LLM training framework with optimizations and ecosystem support
- you want to fine-tune existing pretrained models quickly with Hugging Face tooling
- you need multi-node distributed training at scale
- you are looking for inference or deployment tooling rather than training education

## Facets
- artifact type: learning-resource
- maturity: active
- function: llm-training, deep-learning, machine-learning, nlp, developer-tools
- domain: large-language-models, deep-learning, machine-learning, tutorials, artificial-intelligence
- platform: python, cross-platform
- tags: transformer-from-scratch, pytorch, pretraining, sft, rlhf, dpo, ppo, grpo, reward-model, educational, single-gpu, natural-language-processing, gpu

## Member repositories
- FareedKhan-dev/train-llm-from-scratch (main) score 65

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:10:31.141924+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 2026-08-29T17:22:07.990877+00:00, confidence not recorded.
  - readme: https://github.com/FareedKhan-dev/train-llm-from-scratch (fetched 2026-08-28T04:10:31.141924+00:00, sha 57fd0a66b988)
  - homepage: https://fareedkhan-dev.github.io/train-llm-from-scratch/ (fetched 2026-08-29T08:22:06.271476+00:00, sha 0ebf2b5131ef)
- Data as of 2026-08-30T08:39:29.467469+00:00.