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UniSVQ achieves state-of-the-art performance in 2-bit quantization, outperforming traditional methods while enhancing inference speed.
LC-QAT achieves superior performance in 2-bit quantization with just a fraction of the training data, setting a new standard for data-efficient model optimization.
Tibetan vision-language models can now leap forward thanks to FTibSuite, a new resource suite that closes the infrastructure gap and delivers a strong baseline with substantial performance gains.
Forget turn-based interactions: MiniCPM-o 4.5 lets you build AI that sees, hears, speaks, and *reacts* in real-time, all on a device with only 12GB of RAM.
