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README.md
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📖 <a href="https://angelslim.readthedocs.io/">Documentation</a>   |   🤗 <a href="https://huggingface.co/AngelSlim">Hugging Face</a>   |   🤖 <a href="https://modelscope.cn/organization/AngelSlim">ModelScope</a>   |   💬 <a href="./docs/source/assets/angel_slim_wechat.png">WeChat</a>
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Benchmark results for HY-1.8B-2Bit equivalent weights on vLLM across **cmmlu**,**ceval**,**arc**,**bbh**,**gsm8k**,**humaneval**,**livecodebench** and **gpqa_diamond**.
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| Model | cmmlu | ceval | arc | bbh | gsm8k | humaneval<br/>(pass@3) | livecodebench | gpqa_diamond<br/>(pass@3) |
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📣 <a href="https://huggingface.co/AngelSlim/HY-1.8B-2Bit-GGUF">GGUF</a>   |    📖 <a href="https://angelslim.readthedocs.io/">Documentation</a>   |   🤗 <a href="https://huggingface.co/AngelSlim">Hugging Face</a>   |   🤖 <a href="https://modelscope.cn/organization/AngelSlim">ModelScope</a>   |   💬 <a href="./docs/source/assets/angel_slim_wechat.png">WeChat</a>
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Benchmark results for HY-1.8B-2Bit equivalent weights on vLLM across **cmmlu**,**ceval**,**arc**,**bbh**,**gsm8k**,**humaneval**,**livecodebench** and **gpqa_diamond**.
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The empirical results reveal that HY-1.8B-2Bit maintains high-tier performance despite the extreme reduction in bit-width, incurring a marginal average degradation of only 3.97\% compared to its full-precision 1.8B teacher. Remarkably, HY-1.8B-2Bit performs nearly on par with the INT4 variant,with a negligible accuracy gap of only 0.13\%, while utilizing only half the weight precision. When compared to the dense HY-0.5B model, which occupies a comparable model size, the superiority of the 2-bit QAT approach becomes evident. While the 0.5B dense model suffers a catastrophic 21.87\% drop in average accuracy, HY-1.8B-2Bit remains robust, outperforming the smaller dense counterpart by 22.29\% in GSM8K and 20.62\% in LiveCodeBench.
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| Model | cmmlu | ceval | arc | bbh | gsm8k | humaneval<br/>(pass@3) | livecodebench | gpqa_diamond<br/>(pass@3) |
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