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Fine-tuningFreeOpen Source
NEURAL COMPRESSOR
Model compression and optimization for faster inference
Apache-2.0
ABOUT
Deploying deep learning models to production requires balancing accuracy with latency, memory, and power constraints. Neural Compressor automates model compression through quantization, pruning, and knowledge distillation, reducing model size and speeding up inference by 2-4x while preserving accuracy, across CPUs, GPUs, and edge devices.
INSTALL
pip install neural-compressorINTEGRATION GUIDE
1. Quantize large language models from FP32 to INT8 for 2-4x faster inference with minimal accuracy loss
2. Prune redundant parameters from deep learning models to reduce memory footprint and latency
3. Apply knowledge distillation to compress large teacher models into smaller student models
4. Optimize models for deployment across Intel CPUs, GPUs, and edge hardware platforms
5. Automate the compression pipeline with one-shot tuning that balances accuracy versus performance
TAGS
quantizationcompressionoptimizationinferenceintelpruning