Update README.md

README.md

@@ -6,13 +6,15 @@ tags:
 - sft
 datasets:
 - xavierwoon/cestertrain
+base_model:
+- unsloth/llama-3-8b-bnb-4bit
 ---
 
 # Model Card for Model ID
 
 <!-- Provide a quick summary of what the model is/does. -->
 
-
+Cesterllama is a fine-tuned Llama 3 8B model that is able to generate Libcester unit test cases in the correct format.
 
 ## Model Details
 
@@ -20,26 +22,29 @@ datasets:
 
 <!-- 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.
+<!-- 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
-- **Shared by:** Xavier
-- **Model type:** Text Generation
-- **Finetuned from model [optional]:** Llama 3 8B
+<!-- - **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed] -->
+- **Model type:** Llama
+<!-- - **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed] -->
+- **Finetuned from model [optional]:** unsloth/llama-3-8b-bnb-4bit
 
-<!-- ### Model Sources [optional] -->
+<!-- ### Model Sources [optional]
 
-<!-- Provide the basic links for the model. -->
+Provide the basic links for the model.
 
-<!--- **Repository:** [More Information Needed]
+- **Repository:** [More Information Needed]
 - **Paper [optional]:** [More Information Needed]
 - **Demo [optional]:** [More Information Needed] -->
 
-## Uses
+<!-- ## 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
+<!-- ### Direct Use -->
 
 <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
 
@@ -61,42 +66,79 @@ This is the model card of a 🤗 transformers model that has been pushed on the
 
 <!-- This section is meant to convey both technical and sociotechnical limitations. -->
 
-<!-- [More Information Needed] -->
+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 -->
+### 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. -->
+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/cesterllama"
+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:
+"""
 
-<!-- ## How to Get Started with the Model -->
+inputs = tokenizer(prompt, return_tensors="pt").to("cpu")
 
-<!-- Use the code below to get started with the model. -->
+from transformers import TextStreamer
+text_streamer = TextStreamer(tokenizer)
+_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 2048)
+```
 
 <!-- [More Information Needed] -->
 
 ## Training Details
 
-Trained with 3 epochs.
-
 ### 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] -->
+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 -->
+### Training Procedure
 
 <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
 
-<!-- #### Preprocessing [optional] -->
+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
 
-<!-- [More Information Needed] -->
+<!-- #### 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 -->
+<!-- - **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] -->
 
@@ -108,11 +150,9 @@ Trained with 3 epochs.
 
 <!-- This section describes the evaluation protocols and provides the results. -->
 
-### Testing Data, Factors & Metrics
-
-Code coverage and Robustness.
+<!-- ### Testing Data, Factors & Metrics -->
 
-#### Testing Data
+<!-- #### Testing Data -->
 
 <!-- This should link to a Dataset Card if possible. -->
 
@@ -130,11 +170,11 @@ Code coverage and Robustness.
 
 <!-- [More Information Needed] -->
 
-### Results
+<!-- ### Results -->
 
 <!-- [More Information Needed] -->
 
-#### Summary
+<!-- #### Summary -->
 
 
 
@@ -144,20 +184,7 @@ Code coverage and Robustness.
 
 <!-- [More Information Needed] -->
 
-<!-- ## 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 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]
-
-## Technical Specifications [optional]
+<!-- ## Technical Specifications [optional]
 
 ### Model Architecture and Objective
 
@@ -175,11 +202,11 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 
 [More Information Needed]
 
-## Citation [optional]
+## 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.
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
 
-**BibTeX:**
+<!-- **BibTeX:**
 
 [More Information Needed]
 
@@ -187,11 +214,11 @@ If there is a paper or blog post introducing the model, the APA and Bibtex infor
 
 [More Information Needed]
 
-## Glossary [optional]
+## Glossary [optional] -->
 
-If relevant, include terms and calculations in this section that can help readers understand the model or model card.
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
 
-[More Information Needed]
+<!-- [More Information Needed]
 
 ## More Information [optional]