Tinyllamas🦙 is an Extensible advanced language model framework, inspired by the original Llama model.
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Updated
Sep 17, 2024 - Python
Tinyllamas🦙 is an Extensible advanced language model framework, inspired by the original Llama model.
An unofficial implementation of TubeViT in "Rethinking Video ViTs: Sparse Video Tubes for Joint Image and Video Learning"
my personal website + blog!
Implementation of a Quantum Exact String Matching Algorithm.
AI research lab🔬: implementations of AI papers and theoretical research: InstructGPT, llama, transformers, diffusion models, RLHF, etc...
Language to Language Transformer model from scartch using pure Pytorch where I used my transformer model for translation task. from the paper "Attention is all you Need" 2017 using pytorch.
🎨"Denoising Diffusion Probabilistic Models" paper implementation. a stable diffusion engine: using pytorch as a backend and fastAPI as frontend using javascript, and slo providing gradio interface
FoodMini is an image recognition model trained on subset of FoodVision 101 dataset consisting of three labels (sushi, pizza, and steak). This project implements ViT (Vision Transformer) from its original research paper as state-of-the-art image recognition model.
Fast and Computationally efficient Continual Learning for NanoDet anchor-free Object Detector
Common Lisp port of Doug Lenat's EURISKO
Official implementation for the paper: "Code Generation with AlphaCodium: From Prompt Engineering to Flow Engineering""
Pytorch implementation of ACCV18 paper "Revisiting Distillation and Incremental Classifier Learning."
The python implementation of "Do As I Can, Not As I Say" paper
Implementation of a deep learning model (BiLSTM) to detect code-switching
Personal Implementation of Deep Learning Algorithms
Simulations for the paper "Inter node Hellinger Distance based Decision Tree by Pritom Saha Akash, Md. Eusha Kadir, Amin Ahsan Ali, Mohammad Shoyaib"
My Implementations' Archive
This repository contains Python implementations of various papers that I became familiar with through the Neural Networks and Deep Learning course. These implementations were part of the course homework assignments.
This project implements AlexNet, a pioneering convolutional neural network architecture, using PyTorch. The implementation is based on the original paper by Krizhevsky et al. (2012).
Experiment, data, and analysis code for "Text embedding models yield high-resolution insights into conceptual knowledge from short multiple-choice quizzes" by Paxton C. Fitzpatrick, Andrew C. Heusser, and Jeremy R. Manning
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