We open-sourced Chaperone-Thinking-LQ-1.0 — a 4-bit GPTQ + QLoRA fine-tuned DeepSeek-R1-32B that hits 84% on MedQA in ~20GB

Hey everyone, we just open-sourced our reasoning model, Chaperone-Thinking-LQ-1.0, on Hugging Face. It's built on DeepSeek-R1-Distill-Qwen-32B but goes well beyond a simple quantization. Here's what we actually did: We used 4-bit GPTQ quantization to compress the model from ~60GB down to ~20GB, applied quantization-aware training (QAT) via GPTQ with calibration to minimize accuracy loss, performed QLoRA fine-tuning on medical and scientific corpora, and removed the adaptive identity layer for transparency. The result is a medical reasoning AI that achieves 84% accuracy on MedQA while running on consumer hardware.