QuantEcon / QuantEcon/lectures
[lecture] Building a micro GPT (Blog Post)
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- Dominant language
- Python
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- 0
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- Merged PRs (30d)
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Description
This blog post is pretty cool
It builds a GPT in 243 lines of python including the autograd engine.
It learns baby names and then creates new ones.
Q: How is this different to a simple transition matrix building probabilities off of word transitions? Token size, how it builds patterns?
I think this could form the foundation of an interesting lecture using python.
Contributor guide
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First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start with the linked Towards Deep Learning article and its 243-line Python GPT example. Compare its token and pattern-building approach with the transition-matrix question, then define the lecture scope, examples, and implementation before work begins; done means an agreed lecture covering the proposed concepts.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- content, machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Quiet
- Clarity
- Needs clarification
- Newbie friendliness
- 35/100