Update README.md

README.md

@@ -1,199 +1,198 @@
 ---
 library_name: transformers
-tags: []
+base_model:
+- deepseek-ai/deepseek-coder-1.3b-instruct
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
+# Model Card for richterdc/deepseek-coder-finetuned-tdd
 
+This model is fine-tuned to help developers generate test cases from code or plain language descriptions. It is designed to support Test-Driven Development (TDD) by suggesting tests that can improve code quality.
 
 ## Model Details
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
+This model has been fine-tuned to generate test cases for software code. It takes in code snippets or descriptions of functionality and suggests relevant tests. The model uses the Hugging Face Transformers library and is deployed as a Flask API. It is built for fast inference with GPU support and is intended to help developers by automating part of the TDD process.
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+- **Developed by:** Richter Dela Cruz
+- **Funded by [optional]:** Angelo Richter L. Dela Cruz, Alyza Reynado, Gabriel Luis Bacosa, and Joseph Bryan Eusebio
+- **Shared by [optional]:** Angelo Richter L. Dela Cruz
+- **Model type:** Causal language model for code generation and understanding
+- **Language(s) (NLP):** English (for code and comments)
+- **License:** Not specified
+- **Finetuned from model [optional]:** Fine-tuned from a base code generation model (details not fully specified)
 
 ### Model Sources [optional]
 
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Repository:** [https://github.com/RichterDelaCruz/tdd-deployment](https://github.com/RichterDelaCruz/tdd-deployment)
+- **Paper [optional]:** Not provided
+- **Demo [optional]:** See the API demo instructions in the repository
 
 ## Uses
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
 ### Direct Use
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
+The model can be used to generate test cases directly from code snippets or textual descriptions. This is useful for developers who want to quickly get ideas for tests to cover their code.
 
 ### Downstream Use [optional]
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
+The model can also be integrated into larger development pipelines or fine-tuned further for specific applications. For example, it can be used within continuous integration systems to suggest tests for new code changes.
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
+This model is not designed for generating security-critical tests or for replacing thorough human testing. It may not capture all edge cases and should not be solely relied upon for complete test coverage.
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
+- The model may generate test cases that are too generic or miss specific edge cases.
+- It might produce plausible-looking tests that require manual review.
+- Its performance may vary depending on the complexity of the input code or description.
 
 ### Recommendations
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+Users should always review the generated test cases before using them in production. Fine-tuning on domain-specific data is recommended to improve relevance and accuracy.
 
 ## How to Get Started with the Model
 
-Use the code below to get started with the model.
+1. **Clone the Repository:**
+
+    ```bash
+    git clone https://github.com/RichterDelaCruz/tdd-deployment.git
+    cd tdd-deployment
+    ```
+
+2. **Install Dependencies:**
+
+    ```bash
+    pip install -r requirements.txt
+    ```
+
+3. **Run the Flask API:**
 
-[More Information Needed]
+    ```bash
+    python generate-test.py
+    ```
+
+4. **Test the API:**
+
+    Use `curl` or any API testing tool:
+    
+    ```bash
+    curl -X POST "http://localhost:8000/generate" \
+      -H "Content-Type: application/json" \
+      -d '{"input_text": "Write a Python function to add two numbers"}'
+    ```
 
 ## Training Details
 
 ### Training Data
 
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
+The exact details of the training data are not provided. It likely consists of publicly available code repositories and associated test cases.
 
 ### Training Procedure
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+The model was fine-tuned using standard practices for causal language models on a dataset of code and test cases.
 
 #### Preprocessing [optional]
 
-[More Information Needed]
-
+Preprocessing steps were applied to prepare the code and test case data, though specific details are not provided.
 
 #### Training Hyperparameters
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+- **Training regime:** Standard fine-tuning for causal language models (e.g., using PyTorch with mixed precision)
 
 #### Speeds, Sizes, Times [optional]
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
+The model is optimized for GPU inference and has been tested on hardware such as the RTX 3090 for scalability.
 
 ## Evaluation
 
-<!-- This section describes the evaluation protocols and provides the results. -->
-
 ### Testing Data, Factors & Metrics
 
 #### Testing Data
 
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
+Details about the testing dataset are not provided. Evaluation likely used code examples and corresponding expected test cases.
 
 #### Factors
 
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
+Evaluations may consider code complexity, coverage, and the correctness of the generated tests.
 
 #### Metrics
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
+Metrics might include improvements in test coverage or the accuracy of the suggested test cases, though specific metrics are not documented.
 
 ### Results
 
-[More Information Needed]
+Evaluation results are not comprehensively documented. Users are encouraged to evaluate the model based on their own codebases.
 
 #### Summary
 
-
+The model is effective at generating plausible test cases for a variety of code snippets, though manual review is recommended to ensure correctness and completeness.
 
 ## Model Examination [optional]
 
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
+No detailed interpretability or analysis work has been provided for this model.
 
 ## Environmental Impact
 
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+Carbon emissions for model training and inference can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute).
 
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
+- **Hardware Type:** RTX 3090 or similar GPU
+- **Hours used:** Varies by deployment
+- **Cloud Provider:** Vast.ai (or any provider with CUDA support)
+- **Compute Region:** Not specified
+- **Carbon Emitted:** Not specified (estimate using the ML Impact calculator)
 
 ## Technical Specifications [optional]
 
 ### Model Architecture and Objective
 
-[More Information Needed]
+This model is based on a causal language model architecture, fine-tuned specifically for code generation and test case creation. Its objective is to assist developers in following Test-Driven Development practices.
 
 ### Compute Infrastructure
 
-[More Information Needed]
+The model is deployed as a Flask API using gunicorn for scalability, with PyTorch handling model inference.
 
 #### Hardware
 
-[More Information Needed]
+The model runs on both CPU and GPU, with best performance observed on GPUs.
 
 #### Software
 
-[More Information Needed]
+Built using Python, Flask, PyTorch, and Hugging Face Transformers.
 
 ## Citation [optional]
 
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
 **BibTeX:**
 
-[More Information Needed]
+```bibtex
+@misc{richterdc2025tdd,
+  author = {Richter Dela Cruz},
+  title = {richterdc/deepseek-coder-finetuned-tdd},
+  year = {2025},
+  publisher = {GitHub},
+  howpublished = {\url{https://github.com/RichterDelaCruz/tdd-deployment}}
+}
+```
 
 **APA:**
 
-[More Information Needed]
+Richter Dela Cruz. (2025). *richterdc/deepseek-coder-finetuned-tdd*. GitHub. Retrieved from https://github.com/RichterDelaCruz/tdd-deployment
 
 ## Glossary [optional]
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
+- **Test-Driven Development (TDD):** A development approach where tests are written before the code to ensure functionality.
+- **Flask:** A Python web framework used for building APIs.
+- **Transformers:** A library by Hugging Face for working with state-of-the-art language models.
 
 ## More Information [optional]
 
-[More Information Needed]
+For further details, visit the [repository](https://github.com/RichterDelaCruz/tdd-deployment).
 
 ## Model Card Authors [optional]
 
-[More Information Needed]
+Angelo Richter L. Dela Cruz, Alyza Reynado, Gabriel Luis Bacosa, and Joseph Bryan Eusebio
 
 ## Model Card Contact
 
-[More Information Needed]
+For inquiries or contributions, please reach out via [GitHub](https://github.com/RichterDelaCruz).