mixtral-instruct-8x7b-quantized-gguf

This repository contains alternative Mixtral-instruct-8x7B (https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1) quantized models in GGUF format for use with llama.cpp. The models are fully compatible with the oficial llama.cpp release and can be used out-of-the-box.

I'm carefull to say "alternative" rather than "better" or "improved" as I have not put any effort into evaluating performance differences in actual usage. Perplexity is lower compared to the "official" llama.cpp quantization, but perplexity is not necessarily a good measure for real world performance. Nevertheless, perplexity does measure quantization error, so below is a table comparing perplexities of these quantized models to the current llama.cpp quantization approach on Wikitext for a context length of 512 tokens. The "Quantization Error" columns in the table are defined as (PPL(quantized model) - PPL(fp16))/PPL(fp16).