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README.md
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Based on our internal testing, ExCoT delivered state-of-the-art results on the [BIRD-test benchmark](https://bird-bench.github.io/), achieving best-in-class performance in the single-model, single-inference category using only public datasets (BIRD and Spider) and no additional Text2SQL data:
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* Arctic-ExCoT-70B
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* Both models significantly outperformed larger open-weight models, such as Mistral 123B, and even proprietary systems including GPT-4o and Claude 3.5 — achieving over 12 percentage points of improvement.
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Based on our internal testing, ExCoT delivered state-of-the-art results on the [BIRD-test benchmark](https://bird-bench.github.io/), achieving best-in-class performance in the single-model, single-inference category using only public datasets (BIRD and Spider) and no additional Text2SQL data:
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* [Llama-3.1-Arctic-ExCoT-70B](https://huggingface.co/Snowflake/Llama-3.1-Arctic-ExCoT-70B) improved execution accuracy on the BIRD-dev set from the base model’s 57.37% to 68.51%. [Qwen-2.5-coder-Arctic-ExCoT-32B](https://huggingface.co/Snowflake/Qwen-2.5-coder-Arctic-ExCoT-32B) achieved similarly strong gains.
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* Both models significantly outperformed larger open-weight models, such as Mistral 123B, and even proprietary systems including GPT-4o and Claude 3.5 — achieving over 12 percentage points of improvement.
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