ben fattori (fattorib)

fattorib

Geek Repo

Location:Toronto, Ontario

Home Page:fattorib.github.io

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ben fattori's repositories

Little-GPT

GPT* - Training faster small transformers using ALiBi, Parallel Residual Connections and more!

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transformer_shmap

Tensor Parallelism with JAX + Shard Map

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ZeRO-transformer

Two implementations of ZeRO-1 optimizer sharding in JAX

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Flax-ResNets

CIFAR10 ResNets implemented in JAX+Flax

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LeagueMatchScraper

Code to scrape League of Legends matches using the Riot Games API.

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RepVGG-CIFAR10

RepVGG models specifically for CIFAR10 and CIFAR 100. Based on RepVGG: Making VGG-style ConvNets Great Again (Ding et. al)

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StochasticDepthNets

PyTorch implementation of ResNet110 as described in Deep Networks with Stochastic Depth (Huang et al.)

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wtf-wikipedia-python

raw wikipedia XML to LM_Dataformat in under 4 hours

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Monte-Carlo-Fractal-Dimensionality

Efficient algorithm using random sampling to calculate the dimension of many basic fractals. Implemented algorithm in Python.

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picograd

picograd - Fully connected neural networks in Python

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GeometricDeepLearning

Introductory Geometric Deep Learning Presentation from September 2021

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Python-Unigram

Unigram tokenization algorithm in Python

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tritonformer

Differentiable transformer in Triton, matching the performance of PyTorch + cuDNN/cuBLAS

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CudaSoftmax

Softmax CUDA kernel :)

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flashy_linear_attention

Flash linear attention kernels in Triton

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Fundamental-Domain

Code to generate a section of the fundamental domain for the action of the special linear group on the space of (integral) binary cubic forms. As it stands, the code is quite inefficient. In the future I hope to optimize it.

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fusedswiglu

Fused SwiGLU Triton kernels

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InfoGAN-Jax

InfoGAN in Jax with small Gradio app

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jaxvae

Variational Autoencoder in JAX

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lm-evaluation-harness

Fork of lm-evaluation-harness for evaluating my custom models

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Python-BPE

I wrote Byte-Pair encoding but its 600x slower than 🤗

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ResNets-CIFAR10

PyTorch implementation of the CIFAR10 ResNets, based on Deep Residual Learning for Image Recognition (He et al.)

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