LLMs from Scratch vs 
Awesome Machine Learning
LLMs from Scratch vs Awesome Machine Learning compared for 2026 — features, license, ease of use, performance and which one to choose. Build a GPT from nothing, line by line vs The reference index of ML libraries, by language.
Updated regularly · curated by OpenSourceAI.tech
LLMs from Scratch vs Awesome Machine Learning at a glance
| Spec | LLMs from Scratch | Awesome Machine Learning |
|---|---|---|
| Category | Learn AI & machine learning | Learn AI & machine learning |
| Type | Book + code | Curated list |
| License | Apache-2.0 | CC0-1.0 |
| Runs locally | Yes | Yes |
| Primary language | Python | Markdown |
| Ease of use | Intermediate | Beginner |
| Best for | genuinely understanding how an LLM works | finding the right library in any language |
| GitHub stars | 100.1k | 73.8k |
How LLMs from Scratch and Awesome Machine Learning score
| Criterion | LLMs from Scratch | Awesome Machine Learning |
|---|---|---|
| Popularity | 5.0 | 4.5 |
| Maintenance | 5.0 | 5.0 |
| Ease of use | 3.5 | 5.0 |
| Privacy | 5.0 | 5.0 |
| License freedom | 5.0 | 3.5 |
Scores are computed automatically from public signals — GitHub stars (popularity), recent commit activity (maintenance), license type (freedom), local-first design (privacy) and onboarding complexity (ease of use). Indicative, not a verdict.
What each one is
LLMs from Scratch
Sebastian Raschka's companion repository to "Build a Large Language Model (From Scratch)": you implement attention, a transformer, pretraining and fine-tuning yourself, in plain PyTorch.
- You build every component yourself — no black boxes
- Runs on a laptop, no cluster needed
- The clearest explanation of attention anywhere
Awesome Machine Learning
The long-standing curated index of machine learning frameworks, libraries and software, organised by programming language — the reference people have used for a decade.
- Maintained for over a decade
- Organised by language, not by hype
- The reference the whole field points to
Key differences
LLMs from Scratch is book + code, while Awesome Machine Learning is curated list. Their licenses differ (Apache-2.0 vs CC0-1.0), which matters if you ship a commercial product. LLMs from Scratch leans more intermediate-friendly, whereas Awesome Machine Learning is more suited to beginner users. In short, LLMs from Scratch fits genuinely understanding how an LLM works, and Awesome Machine Learning fits finding the right library in any language.
Which should you choose?
Choose LLMs from Scratch for genuinely understanding how an LLM works. Choose Awesome Machine Learning for finding the right library in any language.
There is rarely one winner — many setups use both. The right pick depends on your hardware, your team's skills, and whether you value simplicity or control.
Frequently asked questions
Is LLMs from Scratch or Awesome Machine Learning easier to use?
Awesome Machine Learning is generally the easier of the two to get started with, while LLMs from Scratch rewards more setup with more control.
Are LLMs from Scratch and Awesome Machine Learning free?
LLMs from Scratch is free and open source (Apache-2.0), and Awesome Machine Learning is free and open source (CC0-1.0). Neither charges for the core software.
Can I run LLMs from Scratch and Awesome Machine Learning locally?
LLMs from Scratch: yes · Awesome Machine Learning: yes. Both can be used without sending your data to a third-party cloud where their setup allows.
LLMs from Scratch vs Awesome Machine Learning — which should I pick in 2026?
Choose LLMs from Scratch for genuinely understanding how an LLM works. Choose Awesome Machine Learning for finding the right library in any language.
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