NVIDIA / Model-Optimizer

A library of model optimization techniques (quantization, pruning, distillation, NAS, speculative decoding, sparsity) that exports compressed checkpoints for inference frameworks.

qualcomm / aimet

AIMET is a library that provides advanced quantization and compression techniques for trained neural network models.

PipeNetwork / kimi-k3-mlx

MLX port of moonshotai/Kimi-K3 (2.78T multimodal MoE): streaming converter, REAP expert pruning, and per-language expert-overlap analysis

intel / neural-compressor

SOTA low-bit LLM quantization (INT8/FP8/MXFP8/INT4/MXFP4/NVFP4) & sparsity; leading model compression techniques on PyTorch, TensorFlow, and ONNX Runtime

opendilab / LightZero

[NeurIPS 2023 Spotlight] LightZero: A Unified Benchmark for Monte Carlo Tree Search in General Sequential Decision Scenarios (awesome MCTS)

tensorflow / model-optimization

A toolkit to optimize ML models for deployment for Keras and TensorFlow, including quantization and pruning.

openvinotoolkit / nncf

Neural Network Compression Framework for enhanced OpenVINO™ inference

freeai-org / Scalpel

✂️Scalpel: Recovery-Aware Layer Pruning for Faster Vision-Language Models

ashishsinha1602 / schemagate

Text-to-SQL access control: schema selection that gives the LLM only the tables the caller is allowed to read. Row-level-security-aware table pruning for LangChain, MCP and any SQL agent. Postgres, Oracle, MySQL, SQL Server. 75% fewer prompt tokens.

kimberlyii / Omni-Prune

Query-aware token pruning for efficient OmniLLMs.