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llama.cpp.
The models are fully compatible with the oficial llama.cpp release and can be used out-of-the-box.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 |
|---|---|---|---|---|---|
| Q2_K | mixtral-instruct-8x7b-q2k.gguf | 6.8953 | 56.4% | 5.2679 | 19.5% |
| Q3_K_S | mixtral-instruct-8x7b-q3k-small.gguf | 4.7038 | 6.68% | 4.6401 | 5.24% |
| Q3_K_M | mixtral-instruct-8x7b-q3k-medium.gguf | 4.6663 | 5.83% | 4.5608 | 3.44% |
| Q4_K_S | mixtral-instruct-8x7b-q4k-small.gguf | 4.5105 | 2.30% | 4.4630 | 1.22% |
| Q4_K_M | mixtral-instruct-8x7b-q4k-medium.gguf | 4.5105 | 2.30% | 4.4568 | 1.08% |
| Q5_K_S | mixtral-instruct-8x7b-q5k-small.gguf | 4.4402 | 0.71% | 4.4277 | 0.42% |
| Q4_0 | mixtral-instruct-8x7b-q40.gguf | 4.5102 | 2.29% | 4.4908 | 1.85% |
| Q4_1 | mixtral-instruct-8x7b-q41.gguf | 4.5415 | 3.00% | 4.4612 | 1.18% |
| Q5_0 | mixtral-instruct-8x7b-q50.gguf | 4.4361 | 0.61% | 4.4297 | 0.47% |