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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: For some reason, the model was initially smaller than LLama-3.1-8B-Instruct after quantizing. This has since been rectified: if you want the most intelligent and most capable quantized GGUF version of Llama-3.1-SuperNova-Lite-8.0B, use the OF32.EF32.IQuants.
The original OQ8_0.EF32.IQuants will remain in the repo for those who want to use them. Cheers! 😁
Addendum: I'm stupid. I was comparing my OQ8_0.EF32 IQuants of Llama-3.1-SuperNova-Lite-8B to that of my OQ8_0.EF32 IQuants of Hermes-3-Llama-3.1-8B - thinking my OQ8_0.EF32 IQuants of Hermes-3-Llama-3.1-8B were the same size as my OQ8_0.EF32.IQuants of LLama-3.1-8B-Instruct; they're not: Hereme-3-Llama-3.1-8B is bigger. So, now we have both OQ8_0.EF32.IQuants and OF32.EF32.IQuants, and they're both great quant schemes. The only difference being, of course, OF32.EF32.IQuants have even more accuracy at the expense of more vRAM. Cheers! 😂 |