2 repositorios
Awesome GitHub RepositoriesSection-Specific Precision Control
Applying different quantization strategies to specific sections of a model based on precision requirements.
Distinct from Precision Quantization: More specific than Precision Quantization by allowing granular, section-based precision application.
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Awesome Section-Specific Precision Control GitHub Repositories
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- nunchaku-ai/nunchaku
nunchaku-ai/nunchaku
3,883Ver en GitHubNunchaku is a 4-bit model quantization library and diffusion model inference engine designed to run large-scale neural networks on consumer GPUs. It functions as a GPU-accelerated optimizer that reduces VRAM usage and increases inference speed through weight compression and memory management. The project utilizes low-rank weight decomposition and SVD weight quantization to compress models to four-bit precision while maintaining visual fidelity. It employs kernel-level operator fusion to minimize data movement and hardware-aware precision mapping to adjust numerical precision based on the unde
Dynamically adjusts numerical precision based on the detected architecture and capabilities of the underlying graphics hardware.
Pythoncomfyuidiffusion-modelsflux - intel/neural-compressor
intel/neural-compressor
2,585Ver en GitHubNeural Compressor is a deep learning model compression toolkit and AI inference acceleration engine. It functions as an automated model quantization tool and hardware-aware model compiler designed to reduce the memory footprint of neural networks and decrease execution latency. The project provides specialized frameworks for optimizing large language models, utilizing weight-only quantization and hardware-specific kernels to improve the operational efficiency of generative AI workloads. It maps neural network operators to specialized CPU and GPU vector instructions to accelerate model executi
Applies granular quantization strategies to specific model sections to balance accuracy and computational efficiency.
Pythonauto-tuningawqfp4
