🚀 FIT-GGUF brings controllable-size mixed-precision quantization to MiniCPM5-2B Developer @Scorp1o_117 used FIT-GGUF to build MiniCPM5-2B GGUF variants around specific size and quality targets. Instead of choosing a fixed quantization preset, you can set a target file size or fidelity tier, and FIT-GGUF automatically decides how much precision to allocate to different tensors—then predicts, generates, and verifies the final GGUF.

✨ What’s included 🧠 Tensor-level mixed-precision quantization 📦 Four MiniCPM5-2B builds from ~1.14 GiB to ~1.46 GiB 🎯 Quality / Balanced / Compact / Mini presets 📊 KL Divergence and Same-top evaluation ✅ Generated file sizes matched the predicted targets

A nice example of how MiniCPM5-2B can be tuned for different memory and deployment constraints, without being locked into a single Q4/Q5-style quantization preset.

Check out FIT-GGUF and try building a MiniCPM5-2B variant that fits your own device budget.

🤗Model: http://huggingface.co/SC117/MiniCPM5-2B-abliterated-FIT-GGUF/tree/main http://huggingface.co/openbmb/MiniCPM5-2B