iris
minGPT
iris | minGPT | |
---|---|---|
8 | 35 | |
756 | 18,981 | |
- | - | |
1.9 | 0.0 | |
3 months ago | 19 days ago | |
Python | Python | |
GNU General Public License v3.0 only | MIT License |
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iris
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From Deep to Long Learning
Yea, after all these LLMs are predicting one sequence of tokens from another sequence of tokens and the tokens could be anything, it just "happens" that text has the most knowledge and the easiest to input, then there are image, sound, video, but tokens could also be learned from world experience in RL:
Transformers are Sample-Efficient World Models:
https://github.com/eloialonso/iris#transformers-are-sample-e...
- What is the next booming topic in Deep RL?
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Most Popular AI Research Sept 2022 - Ranked Based On Total GitHub Stars
Transformers are Sample Efficient World Models https://github.com/eloialonso/iris https://arxiv.org/abs/2209.00588v1
- [D] Most Popular AI Research Sept 2022 - Ranked Based On GitHub Stars
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Minimal PyTorch re-implementation of GPT
This is actually a pretty neat, self-contained implementation that can super easily extended beyond stereotypical natural language models, for example to create world models for video games [1] or to create robot models that can learn to imitate from large, chaotic human demonstration data [2] (disclaimer, I'm an author on the second one.) Basically, GPT (or minGPT) models are EXCELLENT sequence modelers, almost to the point where you can throw any sensible sequence data at it and hope to get interesting results, as long as you don't overfit.
Even though I have only been working on machine learning for around six years, it's crazy to see how the landscape has changed so fast so recently, including diffusion models and transformers. It's not too much to say that we might expect more major breakthroughs by the end of this decade, and end in a place we can't even imagine right now!
[1] https://github.com/eloialonso/iris
- Transformers are Sample Efficient World Models
- [R] Transformers are Sample Efficient World Models: With the equivalent of only two hours of gameplay in the Atari 100k benchmark, IRIS outperforms humans on 10 out of 26 games and surpasses MuZero.
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.
What are some alternatives?
setfit - Efficient few-shot learning with Sentence Transformers
nanoGPT - The simplest, fastest repository for training/finetuning medium-sized GPTs.
Text2Light - [SIGGRAPH Asia 2022] Text2Light: Zero-Shot Text-Driven HDR Panorama Generation
gpt-2 - Code for the paper "Language Models are Unsupervised Multitask Learners"
block-recurrent-transformer-pytorch - Implementation of Block Recurrent Transformer - Pytorch
simpletransformers - Transformers for Information Retrieval, Text Classification, NER, QA, Language Modelling, Language Generation, T5, Multi-Modal, and Conversational AI
machine-learning-articles - 🧠💬 Articles I wrote about machine learning, archived from MachineCurve.com.
Pytorch-Simple-Transformer - A simple transformer implementation without difficult syntax and extra bells and whistles.
motion-diffusion-model - The official PyTorch implementation of the paper "Human Motion Diffusion Model"
nn-zero-to-hero - Neural Networks: Zero to Hero
CSL - [COLING 2022] CSL: A Large-scale Chinese Scientific Literature Dataset 中文科学文献数据集
huggingface_hub - The official Python client for the Huggingface Hub.