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🔗 Try the models: modelscope.ai 💻 Code: modelscope/entropack
Introducing EntroPack: a PyTorch tensor compression library that quantizes your model at flexible birrates to fit your exact GPU memory budget. 💻 Open Sourced at: modelscope/entropack 🎛️ Flexible compression: entropy-coding-based calibration-free quantization that offers arbitrary bpp (bits per parameter), not bound by fixed precisions like 4-bit or 8-bit. ⚡ GPU-native decoding: fast, fused decoding and reconstruction in a single kernel, with minimum overhead, suitable for compute-intensivie workloads such as Diffusion denoising and Transformers-Prefill. 🏆 Make the best use of your GPU VRAM: customized bpp for your exact GPU budget. For example, on a 16G GPU, Qwen-Image-2.1 can be quantized with at 4.8 bpp, resulting in 15.86 GiB usage while cutting reconstruction error from NF4's 9.40% to 4.26%. 📦 Ready to try: pre-quantized weights for Z-Image-Turbo, Qwen-Image-2.1, and MiniMax-H3 at 4 to 8 bpp, for DiffSynth-Studio and ComfyUI. The extreme Z-Image build reaches 2.3 bpp, shrinking its DiT weights to 1.6 GB, around one-seventh of BF16. 📜 Licensed under Apache 2.0. 📄 Technical report: modelscope.ai 🤖 modelscope.ai