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README.md
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@@ -77,6 +77,27 @@ readme in the tools directory of the source tree https://github.com/ggml-org/lla
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Use of the best available model (Q4_K_H) is recommended to maximize the accuracy of vision mode. To run it on a 12G VRAM
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GPU use --ngl 32. Generation speed is still quite good with partial offload.
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## Download the file from below:
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| Link | Type | Size/e9 B | Notes |
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|------|------|-----------|-------|
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Use of the best available model (Q4_K_H) is recommended to maximize the accuracy of vision mode. To run it on a 12G VRAM
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GPU use --ngl 32. Generation speed is still quite good with partial offload.
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Benchmarks:
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A full set of benchmarks for the model will eventually be given here: https://huggingface.co/spaces/steampunque/benchlm
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Mistral-Small-3.1-24B-Instruct-2503 compares most closely with gemma-3-27B-it available here: https://huggingface.co/steampunque/gemma-3-27b-it-Hybrid-GGUF .
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A short summary of some key evals comparing the two models is given here for convenience:
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model | gemma-3-27b-it | Mistral-Small-3.1-24B-Instruct-2503 |
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------|-----------------|------------|
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quant | Q4_K_H | Q4_K_H |
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alignment | strict | permissive |
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TEST | | |
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Winogrande | 0.748 | 0.784 |
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Lambada | 0.742 | 0.798 |
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Hellaswag | 0.802 | 0.899 |
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BoolQ | 0.701 | 0.646 |
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Jeopardy | 0.830 | 0.740 |
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GSM8K | 0.964 | 0.940 |
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Apple | 0.850 | 0.820 |
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Humaneval | 0.890 | 0.853 |
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## Download the file from below:
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| Link | Type | Size/e9 B | Notes |
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|------|------|-----------|-------|
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