AI models will secretly scheme to protect other AI models from being shut down, researchers find - Fortune
<a href="https://news.google.com/rss/articles/CBMixgFBVV95cUxPdDVrRUpkN1RRQU91SDJYYzVzejV4b1JoTWdwVEZVamltZHdKaGtfS3FNQlMyWVdmS2NqRi1pUHJWbG9KX1ZkUmFPeEllc0Q1SjlPdnVPMHRYTXE2S2EtbThEM1lncnVac01Wc2N2V0NGelIwUVFWUTFtdGRxMGpSby11QWNEcHlqcF96QWhuYWQ0YWFuWDBhWGFqSDNFRVNGc19uNzJnUHR4X0VxQzdZTDhUNjg2Y3pOWWw2QjUweFc0djFUSFE?oc=5" target="_blank">AI models will secretly scheme to protect other AI models from being shut down, researchers find</a> <font color="#6f6f6f">Fortune</font>
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Is Turboquant really a game changer?
I am currently utilizing qwen3.5 and Gemma 4 model. Realized Gemma 4 requires 2x ram for same context length. As far as I understand, what turbo quant gives is quantizing kv cache into about 4 bit and minimize the loses But Q8 still not lose the context that much so isn't kv cache ram for qwen 3.5 q8 and Gemma 4 truboquant is the same? Is turboquant also applicable in qwen's cache architecture? because as far as I know they didn't tested it in qwen3.5 style kv cache in their paper. Just curious, I started to learn local LLM recently submitted by /u/Interesting-Print366 [link] [comments]
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