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README.md
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@@ -14,7 +14,6 @@ This repository contains [`mistralai/Mistral-Small-Instruct-2409`](https://huggi
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1. **Memory-efficiency:** The full-precision model is around 44 GB, while this xMADified model is only 12 GB, making it feasible to run on a 16 GB GPU.
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2. **Accuracy:** This xMADified model preserves the quality of the full-precision model. In the table below, we present the zero-shot accuracy on popular benchmarks of this xMADified model against the [GPTQ](https://github.com/AutoGPTQ/AutoGPTQ)-quantized model (both w4g128 for a fair comparison). GPTQ fails on the difficult **MMLU** task, while the xMADai model offers significantly higher accuracy.
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| Model | MMLU | Arc Challenge | Arc Easy | LAMBADA | WinoGrande | PIQA |
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| GPTQ Mistral-Small-Instruct-2409 | 49.45 | 56.14 | 80.64 | 75.1 | 77.74 | 77.48 |
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1. **Memory-efficiency:** The full-precision model is around 44 GB, while this xMADified model is only 12 GB, making it feasible to run on a 16 GB GPU.
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2. **Accuracy:** This xMADified model preserves the quality of the full-precision model. In the table below, we present the zero-shot accuracy on popular benchmarks of this xMADified model against the [GPTQ](https://github.com/AutoGPTQ/AutoGPTQ)-quantized model (both w4g128 for a fair comparison). GPTQ fails on the difficult **MMLU** task, while the xMADai model offers significantly higher accuracy.
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| Model | MMLU | Arc Challenge | Arc Easy | LAMBADA | WinoGrande | PIQA |
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| GPTQ Mistral-Small-Instruct-2409 | 49.45 | 56.14 | 80.64 | 75.1 | 77.74 | 77.48 |
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