Instructions to use xavierwoon/cesterqwen with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use xavierwoon/cesterqwen with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="xavierwoon/cesterqwen")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("xavierwoon/cesterqwen")
model = AutoModelForCausalLM.from_pretrained("xavierwoon/cesterqwen")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use xavierwoon/cesterqwen with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "xavierwoon/cesterqwen"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "xavierwoon/cesterqwen",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/xavierwoon/cesterqwen

SGLang

How to use xavierwoon/cesterqwen with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "xavierwoon/cesterqwen" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "xavierwoon/cesterqwen",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "xavierwoon/cesterqwen" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "xavierwoon/cesterqwen",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use xavierwoon/cesterqwen with Docker Model Runner:
```
docker model run hf.co/xavierwoon/cesterqwen
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Model Card for Model ID

Cesterqwen is a fine-tuned Qwen2.5-1.5B model that is able to generate Libcester unit test cases in the correct format.

Model Details

Model Description

Developed by: Xavier Woon
Bias, Risks, and 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

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, Qwen2Tokenizer

model_name = "xavierwoon/cesterqwen"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = Qwen2Tokenizer.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)
```
Training Details

Training Data

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
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

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Safetensors

Model size

2B params

Tensor type

F32

Model tree for xavierwoon/cesterqwen

Base model

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xavierwoon
/

cesterqwen

Model Card for Model ID

Model Details

Model Description

Bias, Risks, and Limitations

Recommendations

How to Get Started with the Model

Training Details

Training Data

Training Procedure

Model tree for xavierwoon/cesterqwen

Dataset used to train xavierwoon/cesterqwen