jacobhoffmann
/

CodeLlama-13B-TestGen-Dart_v0.2-GGUF

@@ -1,196 +1,141 @@
 ---
 pipeline_tag: text-generation
 tags:
 - code
 ---
-# Model Card for Model ID
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 ---
+license: llama2
+language:
+- en
+base_model:
+- meta-llama/CodeLlama-13b-hf
 pipeline_tag: text-generation
 tags:
 - code
+- gguf
+- llama.cpp
+- llmstudio
 ---
+# Model Card for TestGen-Dart v0.2 (GGUF Version)
+This model card provides information about **TestGen-Dart v0.2 (GGUF Version)**, a fine-tuned version of Meta's Code Llama 13B model, optimized for generating unit tests in Dart for mobile applications. This GGUF-quantized model is designed to run efficiently with frameworks like **LLMStudio** and **llama.cpp**, enabling deployment on resource-constrained hardware while maintaining robust performance.
+---
 ## Model Details
 ### Model Description
+**TestGen-Dart v0.2** is a fine-tuned version of Code Llama 13B, specifically adapted for generating unit test cases for Dart code. The GGUF quantization enables its use on lightweight, consumer-grade systems without significant performance loss.
+- **Developed by:** Jacob Hoffmann, Demian Frister (Karlsruhe Institute of Technology - KIT, AIFB-BIS)
+- **Funded by:** Helmholtz Association's Initiative and Networking Fund on the HAICORE@FZJ partition
+- **Shared by:** Jacob Hoffmann, Demian Frister
+- **Model type:** Fine-tuned Code Llama 13B for test generation in Dart
+- **Language(s):** English
+- **License:** LLaMA 2 Community License
+- **Finetuned from model:** Meta's Code Llama 13B
+### Model Sources
+- **Repository:** [GitHub Repository](https://github.com/example/repo) (placeholder)
+- **Paper:** ["Generating Software Tests for Mobile Applications Using Fine-Tuned Large Language Models"](https://doi.org/10.1145/3644032.3644454) (published in AST '24)
+- **Demo:** Coming soon
+---
 ## Uses
 ### Direct Use
+The model can be used in a zero-shot setting with **llama.cpp** or **LLMStudio** to generate unit tests in Dart. Provide the class code as input, and the model outputs structured unit tests using Dart's `test` package.
+### Downstream Use
+This model is suitable for integration into developer tools, IDE extensions, or continuous integration pipelines to automate test generation for Dart-based applications.
 ### Out-of-Scope Use
+- Do not use this model for tasks unrelated to Dart test generation.
+- Avoid using this model to improve or train other LLMs not based on LLaMA or its derivatives, per the LLaMA 2 Community License.
+- Misuse for malicious purposes, such as generating incorrect or harmful test cases, is prohibited.
+---
+## Running the GGUF Model with llama.cpp
+To use this GGUF quantized model with llama.cpp:
+1. Clone the llama.cpp repository and build the binaries:
+   ```bash
+   git clone https://github.com/ggerganov/llama.cpp
+   cd llama.cpp
+   make
+   ```
+2. Place the GGUF file in the models directory:
+   ```bash
+   mkdir -p models/testgen-dart-v0.2
+   mv /path/to/CodeLlama-13B-TestGen-Dart_v0.2.gguf models/testgen-dart-v0.2/
+   ```
+3. Run the model:
+   ```bash
+   ./main -m models/testgen-dart-v0.2/CodeLlama-13B-TestGen-Dart_v0.2.gguf --prompt "Generate unit tests in Dart for the following class:\nclass Calculator { int add(int a, int b) { return a + b; } }"
+   ```
 ## 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={Generating Software Tests for Mobile Applications Using Fine-Tuned Large Language Models},
+  author={Hoffmann, Jacob and Frister, Demian},
+  booktitle={Proceedings of the 5th ACM/IEEE International Conference on Automation of Software Test (AST 2024)},
+  year={2024},
+  doi={10.1145/3644032.3644454}
+}
+```
 ## Model Card Contact
+- **Jacob Hoffmann**: [[email protected]](mailto:[email protected])
+- **Demian Frister**: [[email protected]](mailto:[email protected])