Update README.md

README.md

@@ -92,4 +92,66 @@ prompt = f"Generate unit tests in Dart for the following class:\n{input_code}"
 # Generate tests
 inputs = tokenizer(prompt, return_tensors="pt")
 outputs = model.generate(**inputs, max_length=512)
-print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+
+## Training Details
+
+### Training Data
+
+The fine-tuning dataset consists of **16,252 Dart code-test pairs** extracted from open-source GitHub repositories using Google BigQuery. The data was subjected to quality filtering and deduplication to ensure high relevance and consistency.
+
+### Training Procedure
+
+- **Fine-tuning Approach:** Supervised Fine-Tuning (SFT) with QLoRA for memory efficiency.  
+- **Hardware:** Training was conducted on a single NVIDIA A100 GPU.  
+- **Optimization:** Flash Attention 2 was utilized for enhanced performance.  
+- **Duration:** The training process ran for up to 32 hours.
+
+### Training Hyperparameters
+
+- **Mixed Precision:** FP16  
+- **Optimizer:** AdamW  
+- **Learning Rate:** 5e-5  
+- **Epochs:** 3  
+
+### Environmental Impact
+
+- **Hardware Type:** NVIDIA A100 GPU  
+- **Hours Used:** 32 hours  
+- **Carbon Emitted:** 13.099 kgCO2eq  
+
+---
+
+## Evaluation
+
+### Testing Data, Factors & Metrics
+
+- **Testing Data:** A subset of **42 Dart files** from the training dataset, evaluated in a zero-shot setting.  
+- **Factors:** Syntax correctness, functional correctness.  
+- **Metrics:** pass@1, syntax error rate, functional correctness rate.
+
+### Results
+
+- **Syntax Correctness:** +76% improvement compared to the base model.  
+- **Functional Correctness:** +16.67% improvement compared to the base model.  
+
+---
+
+## Citation
+
+If you use this model in your research, please cite:
+
+**BibTeX:**
+```bibtex
+@inproceedings{hoffmann2024testgen,
+  title={Test Case Generation with Fine-Tuned LLaMA Models},
+  author={Hoffmann, Jacob and Frister, Demian},
+  booktitle={Proceedings of the 29th ACM/SIGSOFT International Workshop on Automated Software Testing (AST)},
+  year={2024},
+  doi={10.1145/3644032.3644454}
+}
+
+## Model Card Contact
+
+- **Jacob Hoffmann**: [jacob.hoffmann@partner.kit.edu](mailto:jacob.hoffmann@partner.kit.edu)  
+- **Demian Frister**: [demian.frister@kit.edu](mailto:demian.frister@kit.edu)