songhan

songhan's Stars

• home-assistant/core

  :house_with_garden: Open source home automation that puts local control and privacy first.

  Language:Python69.7k1.3k49.9k28.8k

• microsoft/DeepSpeed

  DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

  Language:Python33.6k3392.6k3.9k

• mit-han-lab/streaming-llm

  [ICLR 2024] Efficient Streaming Language Models with Attention Sinks

  Language:Python6.3k6078356

• gaogaotiantian/viztracer

  VizTracer is a low-overhead logging/debugging/profiling tool that can trace and visualize your python code execution.

  Language:Python4.6k48185354

• mit-han-lab/bevfusion

  [ICRA'23] BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representation

  Language:Python2.1k42603379

• mit-han-lab/llm-awq

  [MLSys 2024 Best Paper Award] AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration

  Language:Python2.1k23157150

• mit-han-lab/temporal-shift-module

  [ICCV 2019] TSM: Temporal Shift Module for Efficient Video Understanding

  Language:Python2k42219416

• mit-han-lab/once-for-all

  [ICLR 2020] Once for All: Train One Network and Specialize it for Efficient Deployment

  Language:Python1.8k5375332

• mit-han-lab/efficientvit

  EfficientViT is a new family of vision models for efficient high-resolution vision.

  Language:Python1.6k33114141

• intel/intel-extension-for-pytorch

  A Python package for extending the official PyTorch that can easily obtain performance on Intel platform

  Language:Python1.5k34489222

• mit-han-lab/data-efficient-gans

  [NeurIPS 2020] Differentiable Augmentation for Data-Efficient GAN Training

  Language:Python1.3k1997174

• mit-han-lab/torchquantum

  A PyTorch-based framework for Quantum Classical Simulation, Quantum Machine Learning, Quantum Neural Networks, Parameterized Quantum Circuits with support for easy deployments on real quantum computers.

  Language:Jupyter Notebook1.2k25115183

• mit-han-lab/torchsparse

  [MICRO'23, MLSys'22] TorchSparse: Efficient Training and Inference Framework for Sparse Convolution on GPUs.

  Language:Cuda1.1k18239130

• mit-han-lab/gan-compression

  [CVPR 2020] GAN Compression: Efficient Architectures for Interactive Conditional GANs

  Language:Python1.1k29101147

• mit-han-lab/smoothquant

  [ICML 2023] SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models

  Language:Python1.1k1981126

• Efficient-Large-Model/VILA

  VILA - a multi-image visual language model with training, inference and evaluation recipe, deployable from cloud to edge (Jetson Orin and laptops)

  Language:Python866196855

• mit-han-lab/anycost-gan

  [CVPR 2021] Anycost GANs for Interactive Image Synthesis and Editing

  Language:Python774233096

• mit-han-lab/tinyengine

  [NeurIPS 2020] MCUNet: Tiny Deep Learning on IoT Devices; [NeurIPS 2021] MCUNetV2: Memory-Efficient Patch-based Inference for Tiny Deep Learning; [NeurIPS 2022] MCUNetV3: On-Device Training Under 256KB Memory

  Language:C7582075127

• mit-han-lab/tinyml

  Language:Python7213726132

• mit-han-lab/pvcnn

  [NeurIPS 2019, Spotlight] Point-Voxel CNN for Efficient 3D Deep Learning

  Language:Python6292278128

• mit-han-lab/TinyChatEngine

  TinyChatEngine: On-Device LLM Inference Library

  Language:C++618123757

• mit-han-lab/lite-transformer

  [ICLR 2020] Lite Transformer with Long-Short Range Attention

  Language:Python597203980

• mit-han-lab/spvnas

  [ECCV 2020] Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution

  Language:Python5782499109

• mit-han-lab/amc

  [ECCV 2018] AMC: AutoML for Model Compression and Acceleration on Mobile Devices

  Language:Python4211725108

• mit-han-lab/tiny-training

  On-Device Training Under 256KB Memory [NeurIPS'22]

  Language:Python41417857

• mit-han-lab/offsite-tuning

  Offsite-Tuning: Transfer Learning without Full Model

  Language:Python36381036

• mit-han-lab/hardware-aware-transformers

  [ACL'20] HAT: Hardware-Aware Transformers for Efficient Natural Language Processing

  Language:Python323131748

• mit-han-lab/inter-operator-scheduler

  [MLSys 2021] IOS: Inter-Operator Scheduler for CNN Acceleration

  Language:C++18982429

• mit-han-lab/parallel-computing-tutorial

  Language:C++12310012

• mit-han-lab/tinychat-tutorial

  Language:C++357310