Hello, I created a calibration free quant method called TQ. It doesn't need any data so models could be quantized as soon as they come out and the quantized models would generalize better. It performs closely to calibration-based methods. For example, for Qwen 3.8 27B the 4-bit TQ has mean KLD of 0.0282 and top-1 of 92.4% I opened sourced the quant code as Quant Factory so anyone can quantize and run open source models. Right now it only supports 4-bit but I plan to add support for 2-bit and 3-bit soon. The repo link is: https://github.com/textclf-api/quant-factory I have a collection of quantized models using TQ at: https://huggingface.co/textclf You can run these models using either using the following docker image or by following the repo's instruction. For example you can run textclf/Qwen3.8-27B-TQ-4bit like this: docker run --rm --gpus all -p 8000:8000 docker.io/textclf/tq-quant:4bit-main vllm serve textclf/Qwen3.8-27B-TQ-4bit --quantization tq The Dockerfile in the repo shows how this docker image was created. You can try it and let me know what you think. Feedback appreciated.   submitted by   /u/textclf [link]   [comments]