Hiroki11x

Hiroki11x's Stars

• youssefHosni/Data-Science-Interview-Questions-Answers

  Curated list of data science interview questions and answers

  3.4k786

• google-research/jestimator

  Amos optimizer with JEstimator lib.

  Language:Python816

• HIPS/hypergrad

  Exploring differentiation with respect to hyperparameters

  Language:Python29771

• ssnl/distributed_shampoo

  For optimization algorithm research and development. Submodule & Py38 ready

  Language:Python4

• MedicineToken/Medical-Graph-RAG

  Medical Graph RAG: Graph RAG for the Medical Data

  Language:Python23038

• team-approx-bayes/ivon-experiments

  Language:Python7

• prov-gigapath/prov-gigapath

  Prov-GigaPath: A whole-slide foundation model for digital pathology from real-world data

  Language:Python42555

• facebookresearch/lingua

  Meta Lingua: a lean, efficient, and easy-to-hack codebase to research LLMs.

  Language:Python4.2k218

• ankur-98/BERT_GLUE

  Exploring the GLUE benchmark and fine tuning tasks on pre-trained BERT model using Hugging Face on PyTorch..

  Language:Jupyter Notebook21

• jong980812/Slurm_MultiNode_DDP

  This helps you to submit job with multinode & multgpu in Slurm in Torchrun

  Language:Shell8

• tanganke/peta

  Code for paper "Parameter Efficient Multi-task Model Fusion with Partial Linearization"

  Language:Jupyter Notebook141

• hushon/JAX-ResNet-CIFAR10

  Simple CIFAR10 ResNet example with JAX.

  Language:Python212

• mariaref/nonconvex-lr

  Codes for theoretical study of learning rate scheduling in non-convex problems.

  Language:C++2

• KellerJordan/cifar10-airbench

  94% on CIFAR-10 in 2.6 seconds 💨 96% in 27 seconds

  Language:Python1789

• ibm-granite-community/granite-timeseries-cookbook

  Granite Time Series Cookbook

  Language:Jupyter Notebook134

• soyflourbread/cifar10-tiny

  A tiny neural network for CIFAR-10 dataset

  Language:Python3

• vict0rsch/tips-research-mila

  General tips to drive your research at Mila

  Language:Python171

• bharathgs/Awesome-Distributed-Deep-Learning

  A curated list of awesome Distributed Deep Learning resources.

  40584

• linkedin/Liger-Kernel

  Efficient Triton Kernels for LLM Training

  Language:Python3.5k208

• ChenRocks/UNITER

  Research code for ECCV 2020 paper "UNITER: UNiversal Image-TExt Representation Learning"

  Language:Python784109

• Liuhong99/Sophia

  The official implementation of “Sophia: A Scalable Stochastic Second-order Optimizer for Language Model Pre-training”

  Language:Python93952

• microsoft/Phi-3CookBook

  This is a Phi-3 book for getting started with Phi-3. Phi-3, a family of open sourced AI models developed by Microsoft. Phi-3 models are the most capable and cost-effective small language models (SLMs) available, outperforming models of the same size and next size up across a variety of language, reasoning, coding, and math benchmarks.

  Language:Jupyter Notebook2.5k259

• SakanaAI/AI-Scientist

  The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery 🧑‍🔬

  Language:Jupyter Notebook8.2k1.2k

• VITA-Group/TENAS

  [ICLR 2021] "Neural Architecture Search on ImageNet in Four GPU Hours: A Theoretically Inspired Perspective" by Wuyang Chen, Xinyu Gong, Zhangyang Wang

  Language:Python16731

• katoro8989/IRM_Variants_Calibration

  Towards Understanding Variants of Invariant Risk Minimization through the Lens of Calibration (TMLR 2024)

  Language:Python1

• AndreasMadsen/faithfulness-measurable-models

  Implementation of Faithfulness measurable masked language models

  Language:Python5

• yandex-research/DeDLOC

  Official code for "Distributed Deep Learning in Open Collaborations" (NeurIPS 2021)

  Language:Jupyter Notebook1166

• pytorch/torchtitan

  A native PyTorch Library for large model training

  Language:Python2.6k205

• prateeky2806/ties-merging

  Language:Python15318

• naver-ai/model-stock

  Model Stock: All we need is just a few fine-tuned models

  Language:Jupyter Notebook921