minGPT
doom-checkboxes
| minGPT | doom-checkboxes | |
|---|---|---|
| 35 | 7 | |
| 19,037 | 173 | |
| - | - | |
| 0.0 | 0.0 | |
| 23 days ago | over 2 years ago | |
| Python | JavaScript | |
| MIT License | - |
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minGPT
- FLaNK AI Weekly for 29 April 2024
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Ask HN: Daily practices for building AI/ML skills?
minGPT (Karpathy): https://github.com/karpathy/minGPT
Next, some foundational textbooks for general ML and deep learning:
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[D] What are some examples of being clever with batching for training efficiency?
Language Model novice here. I was going through the README section of minGPT and read this line.
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LLM Visualization: 3D interactive model of a GPT-style LLM network running inference.
The first network displayed with working weights is a tiny such network, which sorts a small list of the letters A, B, and C. This is the demo example model from Andrej Karpathy's minGPT implementation.
- LLM Visualization
- Learn Machine Learning
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Facebook Prophet: library for generating forecasts from any time series data
Tried it once. Its promise is to take the dataset's seasonal trend into account, which makes sense for Facebook's original use case.
We ran it on such a dataset and found out that directly using https://github.com/karpathy/minGPT consistently gives a better result. So we ended up using the output of Prophet as an input feature to a neural network, but the result was not improved in any significant way.
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Tokenization of numerical series
Sure, im trying to regenerate a bunch of complex numbers based on their absolute value. So im trying to embed these absolute values and then using gpt model(probably mini gpt) try to recover the original comples numbers. There is a certain connection between these complex numbers and their order which im not capable of explaining yet. Im hoping the model would be capable of recognizing certain sequences of these absolute values and match them with the desired complex counterparts (by training the model).
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Anyone know of any articles on training a LLM from scratch on a single GPU?
minGPT (https://github.com/karpathy/minGPT)
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Understanding LLMs(to the best of our knowledge)
Check out minGPT and nanoGPT from Karpathy, he puts out some of the best machine learning tutorials and teaching content.
doom-checkboxes
- FLaNK AI Weekly for 29 April 2024
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Doom-htop: The classic DOOM game over htop
> Just imagine a Todo List or a Calendar in Doom.
Nitpick: This is not a todo list or a calendar in Doom; this is Doom running in a todo list or a calendar.
We already have a doom rendered using checkboxes [1], integrating that into a todo app is left as an exercise for the reader.
[1]: https://healeycodes.github.io/doom-checkboxes/
- Advent Of Code using only the C preprocessor, Day 1 to 6
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Why not use Node.js for chess engines, it must be much faster than c++ right?
WebAssembly and checkboxes
- Doom in checkboxes
- Doom in HTML Checkboxes
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Doom Rendered via Checkboxes
If anyone wants to PR a fix for more browsers please do! (https://github.com/healeycodes/doom-checkboxes/issues/1). Otherwise, I'll probably look into this tomorrow.
What are some alternatives?
nanoGPT - The simplest, fastest repository for training/finetuning medium-sized GPTs.
js-chess-engine - Simple JavaScript chess engine without dependencies written in NodeJs. It can be used on both, server or client (web browser) and do not need persistent storage - handy for serverless solutions like AWS Lambda. This engine includes configurable AI computer logic.
gpt-2 - Code for the paper "Language Models are Unsupervised Multitask Learners"
Folders - A language where the code is written with folders
simpletransformers - Transformers for Information Retrieval, Text Classification, NER, QA, Language Modelling, Language Generation, T5, Multi-Modal, and Conversational AI
doom-workers - Website and Message Router source code for the Multiplayer Doom on Cloudflare Workers tech demo
Pytorch-Simple-Transformer - A simple transformer implementation without difficult syntax and extra bells and whistles.
chessbash - A simple chess game in a bash script
nn-zero-to-hero - Neural Networks: Zero to Hero
doom-emojis - 🕹️ DOOM rendered via emojis in a web browser.
huggingface_hub - The official Python client for the Huggingface Hub.
ffmpeg.wasm - FFmpeg for browser, powered by WebAssembly