This repository contains alternative Mistral-instruct-7B (https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) 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).
| Quantization | Model file | PPL(llama.cpp) | Quantization Error | PPL(new quants) | Quantization Error |
|---|---|---|---|---|---|
| Q3_K_S | mistral-instruct-7b-q3k-small.gguf | 6.9959 | 4.27% | 6.8920 | 2.72% |
| Q3_K_M | mistral-instruct-7b-q3k-medium.gguf | 6.8892 | 2.68% | 6.8089 | 1.48% |
| Q4_K_S | mistral-instruct-7b-q4k-small.gguf | 6.7649 | 0.82% | 6.7351 | 0.38% |
| Q5_K_S | mistral-instruct-7b-q5k-small.gguf | 6.7197 | 0.15% | 6.7186 | 0.13% |
| Q4_0 | mistral-instruct-7b-q40.gguf | 6.7728 | 0.94% | 6.7191 | 0.14% |
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