BERT-Transformer-Pytorch
minGPT
| BERT-Transformer-Pytorch | minGPT | |
|---|---|---|
| 2 | 35 | |
| 39 | 18,981 | |
| - | - | |
| 4.3 | 0.0 | |
| 4 months ago | 17 days ago | |
| Python | Python | |
| MIT License | MIT License |
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BERT-Transformer-Pytorch
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[P] Small problems to test out transformers?
The task is described in the paper I linked (3.1, Task #1: Masked LM). Any implementation of BERT should use it, like this one.
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[D] [NLP] Did anything significant happen between RNN and transformer approaches?
There are many guides online, I wrote one here (it also refers to other tutorials). It comes with a python file with 300 lines of code re-implementing Transformers and Bert (it's hard to do shorter)
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?
nanoGPT - The simplest, fastest repository for training/finetuning medium-sized GPTs.
gpt-2 - Code for the paper "Language Models are Unsupervised Multitask Learners"
simpletransformers - Transformers for Information Retrieval, Text Classification, NER, QA, Language Modelling, Language Generation, T5, Multi-Modal, and Conversational AI
Pytorch-Simple-Transformer - A simple transformer implementation without difficult syntax and extra bells and whistles.
nn-zero-to-hero - Neural Networks: Zero to Hero
huggingface_hub - The official Python client for the Huggingface Hub.
tesla-model-y-checklist - Checklist for Tesla Model Y
hlb-CIFAR10 - Train CIFAR-10 in <7 seconds on an A100, the current world record.
machine-learning-articles - 🧠💬 Articles I wrote about machine learning, archived from MachineCurve.com.
NNfSiX - Neural Networks from Scratch in various programming languages
cunumeric - An Aspiring Drop-In Replacement for NumPy at Scale
returnn-experiments - experiments with RETURNN