xavierwoon/cestermistral

Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->

Cestermistral is a fine-tuned Mistral 7B model that is able to generate Libcester unit test cases in the correct format.

Model Details

Model Description

<!-- Provide a longer summary of what this model is. -->

<!-- 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: Xavier Woon <!-- - Funded by [optional]: [More Information Needed]
• —Shared by [optional]: [More Information Needed] -->
• —Model type: Mistral <!-- - Language(s) (NLP): [More Information Needed]
• —License: [More Information Needed] -->
• —Finetuned from model [optional]: unsloth/mistral-7b-bnb-4bit

<!-- ### Model Sources [optional]

Provide the basic links for the model.

• —Repository: [More Information Needed]
• —Paper [optional]: [More Information Needed]
• —Demo [optional]: [More Information Needed] -->

<!-- ## 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] -->

<!-- ### 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] -->

<!-- ### Out-of-Scope Use -->

<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->

<!-- [More Information Needed] -->

Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

The model often regenerates the input prompt in the output. This can lead to limited test cases being printed due to truncations based on max_new_tokens.

Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

Expanding the dataset will help increase the accuracy and robustness of the model, and improve code coverage based on real life scenarios.

How to Get Started with the Model

Use the code below to get started with the model.

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "xavierwoon/cestermistral"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Paste your own code inside
code = """
void add()
{
   int a,b,c;
   printf("\nEnter The Two values:");
   scanf("%d%d",&a,&b);
   c=a+b;
   printf("Addition:%d",c);
}
"""

prompt = f"""### Instruction:
create cester test cases for this function:
{code}

### Input:

### Response:
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cpu")

from transformers import TextStreamer
text_streamer = TextStreamer(tokenizer)
_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 2048)

<!-- [More Information Needed] -->

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. -->

Training Data was created based on Data Structures and Algorithm (DSA) codes created using ChatGPT. It would also create corresponding Cester test cases. After testing and ensuring a good code coverage, the prompt and corresponding test cases were added to the dataset.

Training Procedure

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->

1. 1.Prompt GPT for sample DSA C code
2. 2.Prompt GPT for Libcester unit test cases with 100% code coverage
3. 3.Test generated test cases for robustness and code coverage

<!-- #### Preprocessing [optional]

[More Information Needed] -->

<!-- #### 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 -->

<!-- #### Speeds, Sizes, Times [optional] -->

<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->

<!-- [More Information Needed] -->

<!-- ## 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] -->

<!-- #### Factors -->

<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->

<!-- [More Information Needed] -->

<!-- #### Metrics -->

<!-- These are the evaluation metrics being used, ideally with a description of why. -->

<!-- [More Information Needed] -->

<!-- ### Results -->

<!-- [More Information Needed] -->

<!-- #### Summary -->

<!-- ## Model Examination [optional] -->

<!-- Relevant interpretability work for the model goes here -->

<!-- [More Information Needed] -->

<!-- ## Technical Specifications [optional]

Model Architecture and Objective

[More Information Needed]

Compute Infrastructure

[More Information Needed]

Hardware

[More Information Needed]

Software

[More Information Needed]

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]

APA:

[More Information Needed]

Glossary [optional] -->

<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->

<!-- [More Information Needed]

More Information [optional]

[More Information Needed]

Model Card Authors [optional]

[More Information Needed]

Model Card Contact

[More Information Needed] -->