An Implementation of NanoQuant: A Flexible Binary Quantization Method
r/LocalLLaMA top day6 days agoNew ToolA r/LocalLLaMA post presents an unofficial PyTorch implementation of NanoQuant, a 2026 post-training quantization method for dense transformers. The method factorizes weights into scaling vectors and binary matrices, then quantizes and fine-tunes blocks sequentially to reduce hardware requirements. Early Qwen3-0.6B and Qwen3-4B experiments are promising for base models, but instruct quality remains weak and highly dependent on calibration data.
Gemma 4 QAT models: Optimizing model compression for mobile and laptop efficiency★ 72
Hacker News (AI keywords)9 days agoReleaseGoogle released new Gemma 4 checkpoints optimized with Quantization-Aware Training to preserve quality after compression. The release includes Q4_0 checkpoints and a mobile-focused quantization format that can reduce Gemma 4 E2B memory use to about 1GB, or below 1GB for a text-only configuration. The models are available through Hugging Face and supported across llama.cpp, Ollama, LM Studio, LiteRT-LM, Transformers.js, SGLang, vLLM, MLX, and Unsloth.
Hugging Face 推出 Quanto：適用於 Optimum 的全新 PyTorch 量化後端★ 75
Hugging Face Blog818 days agoReleaseHugging Face has officially introduced Quanto, a brand-new quantization library designed for PyTorch, which has been integrated as a backend into the Hugging…