README.md

---
library_name: transformers
tags:
- unsloth
- trl
- sft
datasets:
- xavierwoon/cestertrain
base_model:
- unsloth/mistral-7b-bnb-4bit
---

# Model Card for Model ID

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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

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<!-- 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
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- **Model type:** Mistral
<!-- - **Language(s) (NLP):** [More Information Needed]
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- **Finetuned from model [optional]:** unsloth/mistral-7b-bnb-4bit

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<!-- ## Uses -->

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<!-- ### Direct Use -->

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<!-- ### Downstream Use [optional] -->

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## Bias, Risks, and Limitations

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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

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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.

```py
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)
```

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## Training Details

### Training Data

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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. Prompt GPT for sample DSA C code
2. Prompt GPT for Libcester unit test cases with 100% code coverage
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 -->

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<!-- ## Evaluation -->

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

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<!-- ### Results -->

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



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