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

Libraries

How to use xavierwoon/cestermistral with Transformers:

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

pipe = pipeline("text-generation", model="xavierwoon/cestermistral")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

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

llama-cpp-python

How to use xavierwoon/cestermistral with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="xavierwoon/cestermistral",
	filename="unsloth.F16.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use xavierwoon/cestermistral with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf xavierwoon/cestermistral:F16
# Run inference directly in the terminal:
llama-cli -hf xavierwoon/cestermistral:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf xavierwoon/cestermistral:F16
# Run inference directly in the terminal:
llama-cli -hf xavierwoon/cestermistral:F16

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf xavierwoon/cestermistral:F16
# Run inference directly in the terminal:
./llama-cli -hf xavierwoon/cestermistral:F16

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf xavierwoon/cestermistral:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf xavierwoon/cestermistral:F16

Use Docker

docker model run hf.co/xavierwoon/cestermistral:F16

LM Studio
Jan

vLLM

How to use xavierwoon/cestermistral with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "xavierwoon/cestermistral"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "xavierwoon/cestermistral",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/xavierwoon/cestermistral:F16

SGLang

How to use xavierwoon/cestermistral 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/cestermistral" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "xavierwoon/cestermistral",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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/cestermistral" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "xavierwoon/cestermistral",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use xavierwoon/cestermistral with Ollama:
```
ollama run hf.co/xavierwoon/cestermistral:F16
```

Unsloth Studio new

How to use xavierwoon/cestermistral with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for xavierwoon/cestermistral to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for xavierwoon/cestermistral to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for xavierwoon/cestermistral to start chatting

Docker Model Runner
How to use xavierwoon/cestermistral with Docker Model Runner:
```
docker model run hf.co/xavierwoon/cestermistral:F16
```

Lemonade

How to use xavierwoon/cestermistral with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull xavierwoon/cestermistral:F16

Run and chat with the model

lemonade run user.cestermistral-F16

List all available models

lemonade list

Model Card for Model ID

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

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, 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)
```
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

Downloads last month: 10

GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

16-bit

Model tree for xavierwoon/cestermistral

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Quantizations

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cestermistral

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/cestermistral

Dataset used to train xavierwoon/cestermistral