AlioLeuchtmann
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ALIO-SQL-7B

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 ---
 library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
 ## 60% on BIRD dev one shot
 ## 70% on BIRD dev 4 shot
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 ---
 library_name: transformers
+tags:
+- text-to-sql
+- sql
+license: apache-2.0
+language:
+- en
+base_model:
+- Qwen/Qwen2.5-Coder-7B-Instruct
 ---
+BIRD-bench: https://bird-bench.github.io/
+## 57% on BIRD private test Set (Qwen 2.5 Coder Models might have been trained on BIRD dev, which could explain the Performance Drop)
 ## 60% on BIRD dev one shot
 ## 70% on BIRD dev 4 shot
+Offical Results on BIRD Test Set
+| Metric      | Simple | Moderate | Challenging | Total |
+| ----------- | -----: | -------: | ----------: | ----: |
+| **Count**   |    949 |      555 |         285 |  1789 |
+| **EX**      |  65.33 |    53.33 |       39.65 | 57.52 |
+| **Soft F1** |  66.31 |    55.51 |       41.48 | 59.01 |
+| **R-VES**   |  61.15 |    47.73 |       36.71 | 53.09 |
+## Model Details
+SFT of Qwen2.5-Coder-7B <br>
+Dataset is a combination of BIRD and Spider; answers were created through knowledge distillation with CoT prompting from Qwen2.5-Coder-32B, and filtered for correctness. <br>
+<br>
+We increased the BIRD dev set performance from 51.1% to 60%. <br>
+Our model beats much larger universal models like GPT-4o and Gemini 1.5 Pro, demonstrating the effectiveness of black-box knowledge distillation for constrained domains. <br>
+After training, this checkpoint even exceeds the performance of its teacher model, Qwen2.5-Coder-32B, which can be achieved by filtering the teacher’s outputs for Quality <br>
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/65e01c279c88ffe889d64a73/pPwqvkFLB4VRKm6XBcZcn.png)