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@@ -56,7 +56,19 @@ model = AutoModelForCausalLM.from_pretrained("Zyphra/Zamba2-7B", device_map="cud
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  Zamba2-7B-Instruct punches dramatically above its weight, achieving extremely strong instruction-following benchmark scores.
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  Moreover, due to its unique hybrid SSM architecture, Zamba2-7B-Instruct achieves extremely low inference latency and rapid generation with a significantly smaller memory footprint than comparable transformer-based models.
 
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  Zamba2-7B-Instruct punches dramatically above its weight, achieving extremely strong instruction-following benchmark scores.
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+ | Task | Score |
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+ |:------------:|:---------:|
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+ | IFEval | 69.95 |
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+ | BBH | 33.33 |
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+ | MATH Lvl 5 | 13.57 |
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+ | GPQA | 10.28 |
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+ | MUSR | 8.21 |
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+ | MMLU-PRO | 32.43 |
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+ | **Average** | **27.96** |
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  Moreover, due to its unique hybrid SSM architecture, Zamba2-7B-Instruct achieves extremely low inference latency and rapid generation with a significantly smaller memory footprint than comparable transformer-based models.