Update README.md

README.md

@@ -29,6 +29,13 @@ According to the current demo version of the FortranHumanEval benchmark:
 | GPT-4o mini           |    18.90%   |           43.90%        |
 | GPT-4o                |    32.31%   |        17.07%           |
 
+Compared to its base model (Qwen 2.5 Coder 3B Instruct), FortranCodeGen 3B shows a strong improvement, increasing pass@1 accuracy from 5.48% to 23.17% and reducing the compile error rate from 63.41% to 17.68%. This highlights the effectiveness of this simple fine-tuning process, even though it was performed with limited resources: no human-labeled data, small synthetic dataset, and training on a single consumer GPU (L4 :'( ).
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+When compared to GPT-4o mini, FortranCodeGen 3B outperforms it in terms of both pass@1 accuracy (23.17% vs. 18.90%) and compile reliability (17.68% vs. 43.90%). This suggests that task-specific fine-tuning can produce better results than more general, (probably) larger models.
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+While it doesn't yet match the overall performance of GPT-4o, which achieves 32.31% pass@1, FortranCodeGen 3B reaches a comparable level of compilation correctness (17.68% vs. 17.07%), suggesting that its outputs are syntactically robust and close to executable, even when they don’t solve the full task.
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+These results confirm that targeted specialization can significantly enhance model performance on underrepresented tasks, and suggest a promising direction for very-low-resource fine-tuning in legacy or niche programming languages.
 
 ## Uses