Code for the paper "FOCIL: Finetune-and-Freeze for Online Class-Incremental Learning by Training Randomly Pruned Sparse Experts"
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Updated
Jun 20, 2024 - Python
Code for the paper "FOCIL: Finetune-and-Freeze for Online Class-Incremental Learning by Training Randomly Pruned Sparse Experts"
Libraries for applying sparsification recipes to neural networks with a few lines of code, enabling faster and smaller models
🤗 Optimum Intel: Accelerate inference with Intel optimization tools
SOTA low-bit LLM quantization (INT8/FP8/INT4/FP4/NF4) & sparsity; leading model compression techniques on TensorFlow, PyTorch, and ONNX Runtime
Chess engine
TinyNeuralNetwork is an efficient and easy-to-use deep learning model compression framework.
Neural Network Compression Framework for enhanced OpenVINO™ inference
AIMET is a library that provides advanced quantization and compression techniques for trained neural network models.
HarvestNet 2.0: Improved Detection of Harvest Piles in Ethiopia
PaddleSlim is an open-source library for deep model compression and architecture search.
Neural network model repository for highly sparse and sparse-quantized models with matching sparsification recipes
[NeurIPS 2023] LLM-Pruner: On the Structural Pruning of Large Language Models. Support LLaMA, Llama-2, BLOOM, Vicuna, Baichuan, etc.
[CVPR 2023] Towards Any Structural Pruning; LLMs / SAM / Diffusion / Transformers / YOLOv8 / CNNs
This is the official PyTorch implementation of "LLM-QBench: A Benchmark Towards the Best Practice for Post-training Quantization of Large Language Models", and also an efficient LLM compression tool with various advanced compression methods, supporting multiple inference backends.
Tutorial notebooks for hls4ml
Prune is a simple tool that lets you remove archives in a folder, deleting any archives not matching the specified retention options.
NNS : Neural network surgery | academic assignment
Harness for training/finding lottery tickets in PyTorch. With support for multiple pruning techniques and augmented by distributed training, FFCV and AMP.
Architecture for pruning methods analysis using pytorch prune module
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