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title: LLMs From Scratch Reaches 100,000 GitHub Stars | daily.dev
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# LLMs From Scratch Reaches 100,000 GitHub Stars

**[Sebastian Raschka](https://daily.dev/sources/sebastianraschka)** · 2 min read · 40 upvotes · 1 comments

## Summary

The LLMs-from-scratch GitHub repository by Sebastian Raschka has surpassed 100,000 stars. The repo covers the full LLM development pipeline from scratch — tokenization, attention mechanisms, pretraining, classification, and instruction fine-tuning — along with from-scratch implementations of Llama, Qwen, Gemma, and Olmo. It also includes advanced topics like GQA, MLA, sliding-window attention, Gated DeltaNet, DeepSeek Sparse Attention, mixture-of-experts, KV caching, DPO, LoRA, and memory-efficient weight loading. The author plans to continue adding new material and is working on a larger applied small LLM project.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://sebastianraschka.com/blog/2026/llms-from-scratch-reaches-100000-github-stars.html>

## Questions this post answers

### What topics does the LLMs-from-scratch GitHub repository cover?

It covers the complete path of building large language models from scratch, including tokenization, attention, pretraining, classification, and instruction fine-tuning. It also includes from-scratch implementations of Llama, Qwen, Gemma, and Olmo, attention variants like GQA, MLA, sliding-window attention, Gated DeltaNet, DeepSeek Sparse Attention, and mixture-of-experts, plus KV caching, DPO, evaluation, and LoRA materials.

_daily.dev surfaces resources like this for developers learning to build LLMs from first principles._

## Community discussion

Top comments from developers on daily.dev.

**@agustinbarrientos** · 0 upvotes

> Thanks for keeping the RL and reasoning material in separate repos

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---

Tags: [#llm](https://daily.dev/tags/llm), [#deep-learning](https://daily.dev/tags/deep-learning)

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