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mobiledet-pytorch Public
PyTorch Implementation of MobileDet (https://arxiv.org/abs/2004.14525v3) backbones.
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llama.cpp Public
Forked from ggerganov/llama.cppPort of Facebook's LLaMA model in C/C++
C++ MIT License UpdatedFeb 10, 2024 -
vit-pytorch Public
Forked from lucidrains/vit-pytorchImplementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch
Python MIT License UpdatedDec 23, 2023 -
exllamav2 Public
Forked from turboderp/exllamav2A fast inference library for running LLMs locally on modern consumer-class GPUs
Python MIT License UpdatedOct 21, 2023 -
transformers Public
Forked from huggingface/transformers🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
Python Apache License 2.0 UpdatedJul 10, 2023 -
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textual_inversion Public
Forked from rinongal/textual_inversionJupyter Notebook MIT License UpdatedNov 7, 2022 -
hf-blog Public
Forked from huggingface/blogPublic repo for HF blog posts
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LCODEC-deep-unlearning Public
Forked from vsingh-group/LCODEC-deep-unlearningCode for CVPR22 paper "Deep Unlearning via Randomized Conditionally Independent Hessians"
Python MIT License UpdatedJul 9, 2022 -
ic-loss Public
PyTorch and Tensorflow implementation of inverse contrastive loss - https://arxiv.org/abs/2102.08343
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timm-vis Public archive
Visualizer for PyTorch image models
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HPAv2-37th-solution Public
37th place solution writeup of the Human Protein Atlas - Single Cell Classification Challenge hosted on Kaggle
Jupyter Notebook MIT License UpdatedMay 22, 2021 -
tucker-conv Public
PyTorch Implementation of Tucker Convolution Layers
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DefectSegNet-pytorch Public
A PyTorch segmentation model based on DefectSegNet
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Fake-News-Detection Public
Forked from SriRangaTarun/Fake-News-DetectionA deep learning algorithm that automatically classifies websites as being fake or not
Jupyter Notebook UpdatedDec 7, 2019 -
My approach to the Aerial Cactus Identification competition on Kaggle
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Predicting the type of message(spam or ham) using Naive Bayes
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My approach to the Kaggle Titanic competition.