FujitsuResearch / OneCompression

What it does

• Quantization Error Propagation (QEP). A post-training quantization method that corrects quantization errors by propagating them to subsequent layers, improving the accuracy of quantized LLMs. See Arai & Ichikawa, NeurIPS 2025 for details. T
• Layer-Projected Coordinate Descent (LPCD). A unified Post Training Quantization (PTQ) framework that extends layer-wise quantization to arbitrary submodules by optimising relaxed objectives and projecting the solutions with layer-wise quant
• vLLM Plugin Integration. Serve OneComp-quantized models with vLLM via built-in plugins for DBF and Mixed-GPTQ quantization methods. Pair with Open WebUI for a ChatGPT-like chat experience on your local machine.
• AutoBit. Mixed-precision quantization with ILP-based bitwidth assignment. Automatically estimates the target bitwidth from available VRAM and assigns per-layer bitwidths to minimize quantization error under the memory budget.
• JointQ. Joint quantization method that optimizes weight assignments and scale parameters simultaneously for improved quantization accuracy. Supports group-wise quantization (e.g., 4-bit, groupsize=128).