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README.md
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- The difference with normal quantizations is that I quantize the output and embed tensors to f16.and the other tensors to 15_k,q6_k or q8_0.This creates models that are little or not degraded at all and have a smaller size.They run at about 3-6 t/sec on CPU only using llama.cpp And obviously faster on computers with potent GPUs
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- https://huggingface.co/ZeroWw/llama3-8B-DarkIdol-2.2-Uncensored-1048K-GGUF
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- More models here: https://huggingface.co/RobertSinclair
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## Why 1048K?
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Due to the optimization of the preferred model, its performance is excellent across the range of 2000-1048K. Personal usage scenarios, such as 8186, 32K, etc., are insufficient. My primary role involves managing virtual idol Twitter accounts and assisting with singing, etc. A good conversation can be very lengthy, and sometimes even 32K is not enough. Imagine having a heated chat with your virtual girlfriend, only for it to abruptly cut off—that feeling is too painful.
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- The difference with normal quantizations is that I quantize the output and embed tensors to f16.and the other tensors to 15_k,q6_k or q8_0.This creates models that are little or not degraded at all and have a smaller size.They run at about 3-6 t/sec on CPU only using llama.cpp And obviously faster on computers with potent GPUs
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- https://huggingface.co/ZeroWw/llama3-8B-DarkIdol-2.2-Uncensored-1048K-GGUF
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- More models here: https://huggingface.co/RobertSinclair
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## Why 1048K?
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Due to the optimization of the preferred model, its performance is excellent across the range of 2000-1048K. Personal usage scenarios, such as 8186, 32K, etc., are insufficient. My primary role involves managing virtual idol Twitter accounts and assisting with singing, etc. A good conversation can be very lengthy, and sometimes even 32K is not enough. Imagine having a heated chat with your virtual girlfriend, only for it to abruptly cut off—that feeling is too painful.
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