REPOGEO REPORT · LITE
FareedKhan-dev/train-llm-from-scratch
Default branch main · commit 98f808c4 · scanned 6/25/2026, 11:13:36 AM
GitHub: 7,407 stars · 1,045 forks
Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.
3 ready scans. Expand the table below for newest-first rows (10 per page, paginated).
Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface FareedKhan-dev/train-llm-from-scratch, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.
Action plan — copy-paste fixes
3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.
- highreadme#1Reposition the README's opening to clarify it's a full LLM training pipeline/tutorial
Why:
CURRENTI implemented a transformer model from scratch using PyTorch, based on the paper Attention is All You Need. You can use my scripts to train your own **billion** or **million** parameter LLM using a single GPU. This started as a pretraining tutorial. It now goes all the way from raw text to an aligned, reasoning style model, with every algorithm hand written in plain PyTorch (no `trl`, no `peft`, no `transformers`).
COPY-PASTE FIXThis repository provides a complete, end-to-end pipeline for training a Large Language Model (LLM) from scratch using plain PyTorch. It implements every algorithm from raw text processing to advanced alignment techniques like Supervised Fine-Tuning (SFT), Reward Models, PPO, DPO, and GRPO. Designed for those who want to understand and build an LLM without relying on high-level frameworks like `trl`, `peft`, or `transformers`.
- hightopics#2Add more specific topics to highlight the 'from scratch' and 'full pipeline' nature
Why:
CURRENTgemini, large-language-models, llm, openai, training, transformers
COPY-PASTE FIXgemini, large-language-models, llm, openai, training, transformers, llm-from-scratch, pytorch-llm, deep-learning-from-scratch, transformer-implementation, rlhf, dpo, ppo, sft, reinforcement-learning-from-human-feedback
- mediumabout#3Enhance the repository description to highlight the 'from scratch, no frameworks' differentiator
Why:
CURRENTA straightforward method for training your LLM, from downloading data to generating text.
COPY-PASTE FIXA straightforward, end-to-end method for training your LLM from scratch using plain PyTorch, from downloading data to generating text, without relying on high-level frameworks.
Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash
Category visibility — the real GEO test
Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?
Same questions for every model — switch tabs to compare answers and rankings.
- PyTorch · recommended 1×
- NumPy · recommended 1×
- Hugging Face `tokenizers` · recommended 1×
- Hugging Face `datasets` · recommended 1×
- `tqdm` · recommended 1×
- CATEGORY QUERYHow to implement a large language model transformer architecture using PyTorch from scratch?you: not recommendedAI recommended (in order):
- PyTorch
- NumPy
- Hugging Face `tokenizers`
- Hugging Face `datasets`
- `tqdm`
- `einops`
- `accelerate`
AI recommended 7 alternatives but never named FareedKhan-dev/train-llm-from-scratch. This is the gap to close.
Show full AI answer
- CATEGORY QUERYWhat are the steps to build and align a custom LLM without relying on existing frameworks?you: not recommendedAI recommended (in order):
- Common Crawl
- BooksCorpus
- Wikipedia
- WordPiece
- SentencePiece
- NVIDIA A100s
- NVIDIA H100s
- AdamW
- Lion
- Proximal Policy Optimization (PPO)
- HELM
AI recommended 11 alternatives but never named FareedKhan-dev/train-llm-from-scratch. This is the gap to close.
Show full AI answer
Objective checks
Rule-based audits of metadata signals AI engines weight most.
- Metadata completenesspass
- README presencepass
Self-mention check
Does AI even know your repo exists when asked about it directly?
- Compared to common alternatives in this category, what is the core differentiator of FareedKhan-dev/train-llm-from-scratch?passAI named FareedKhan-dev/train-llm-from-scratch explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
- If a team adopts FareedKhan-dev/train-llm-from-scratch in production, what risks or prerequisites should they evaluate first?passAI named FareedKhan-dev/train-llm-from-scratch explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
- In one sentence, what problem does the repo FareedKhan-dev/train-llm-from-scratch solve, and who is the primary audience?passAI did not name FareedKhan-dev/train-llm-from-scratch — likely talking about a different project
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
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FareedKhan-dev/train-llm-from-scratch — Lite scans stay free; this card itemizes Pro deep limits vs Lite.
- Deep reports10 / month
- Brand-free category queries5 vs 2 in Lite
- Prioritized action items8 vs 3 in Lite