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-Custom GGUF quants of arcee-ai’s [Llama-3.1-SuperNova-Lite-8B](https://huggingface.co/arcee-ai/Llama-3.1-SuperNova-Lite), where the Output Tensors are quantized to Q8_0 while the Embeddings are kept at F32. Enjoy! 🧠🔥🚀
 UPDATE: This repo now contains updated O.E.IQuants, which were quantized, using a new F32-imatrix, using llama.cpp version: 4067 (54ef9cfc). This particular version of llama.cpp made it so all KQ mat_mul computations were done in F32 vs BF16, when using FA (Flash Attention). This change, plus the other very impactful prior change, which made all KQ mat_muls be computed with F32 (float32) precision for CUDA-Enabled devices, has compoundedly enhanced the O.E.IQuants and has made it excitingly necessary for this update to be pushed. Cheers!


1	+ Custom GGUF quants of [arcee-ai/Llama-3.1-SuperNova-Lite](https://huggingface.co/arcee-ai/Llama-3.1-SuperNova-Lite), where the Output Tensors are quantized to Q8_0 while the Embeddings are kept at F32. Enjoy! 🧠🔥🚀
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3	UPDATE: This repo now contains updated O.E.IQuants, which were quantized, using a new F32-imatrix, using llama.cpp version: 4067 (54ef9cfc). This particular version of llama.cpp made it so all KQ mat_mul computations were done in F32 vs BF16, when using FA (Flash Attention). This change, plus the other very impactful prior change, which made all KQ mat_muls be computed with F32 (float32) precision for CUDA-Enabled devices, has compoundedly enhanced the O.E.IQuants and has made it excitingly necessary for this update to be pushed. Cheers!