FIT-GGUF 为 MiniCPM5-2B 带来可控尺寸混合精度量化
🚀 FIT-GGUF brings controllable-size mixed-precision quantization to MiniCPM5-2B
FIT-GGUF 为 MiniCPM5-2B 带来可控尺寸的混合精度量化，开发者 @Scorp1o_117 用它构建了 4 个 GGUF 版本，体积从约 1.14 GiB 到约 1.46 GiB。用户可设定目标文件大小或保真档位，FIT-GGUF 自动分配各张量精度，并预测、生成、验证最终 GGUF，提供 Quality / Balanced / Compact / Mini 四档预设。生成文件大小与预测目标一致，评测包含 KL Divergence 与 Same-top。
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🚀 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.