Trifiro makes a point. On the one hand, AI will help us find bugs so we can fix them. That's the good news. On the other hand, and here's the bad news, AI can also break into programs still in use that are no longer being patched or supported.
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The combined approach achieves 3.5 bits per channel with "absolute quality neutrality" across Gemma, Mistral, and Llama-3.1-8B-Instruct, validated across LongBench, Needle In A Haystack, ZeroSCROLLS, RULER, and L-Eval. At 2.5 bits, accuracy degradation remains minimal. The headline achievement: 6x KV memory reduction without measurable accuracy loss, with 4-bit TurboQuant delivering 8x performance improvement over 32-bit unquantized keys on H100 GPUs.。关于这个话题，Line下载提供了深入分析
It’s an open source model, so surely there should be some training code online. But it turns out there isn’t really any. LLaMA-Factory + KTransformers is supposed to support it, but I encountered a bunch of bugs. Also, it’s designed for CPU offloading + GPU training, which adds unnecessary complexity and is inefficient.
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