Canstralian/starcoder2-pentesting

StarCoder2

🌟 Latest Coding Model By 🤗 Huggingface & Friends - Supports 101 Programming Languages! 🌟

StarCoder2 is an advanced coding model designed to support a wide range of programming tasks, from code generation to bug fixing, across 101 programming languages. With state-of-the-art performance and seamless integration, StarCoder2 is your go-to tool for coding excellence.

📜 License

StarCoder2 is distributed under the Apache 2.0 License.

🏅 Model Badges

Languages Supported: 101

Framework Compatibility: Hugging Face Transformers

Model Size: 15B Parameters

🌐 Model Capabilities

🗣 Languages Supported

StarCoder2 supports 101 programming languages, including but not limited to:

Python, JavaScript, Java, C++, Go, Rust, HTML, CSS, SQL, TypeScript, and more.

🚀 Performance

StarCoder2 achieves high accuracy and performance on multiple coding benchmarks, making it a reliable choice for complex programming tasks.

🔧 Tasks

StarCoder2 excels at:

Code Generation: Create boilerplate code or complete functions.

Code Completion: Predict the next lines of code with context.

Bug Fixing: Identify and resolve errors in your code.

📥 Installation and Usage

🛠 Installation

Ensure you have the Hugging Face Transformers library installed. You can do so by running:

pip install git+https://github.com/huggingface/transformers.git

Alternatively, use the provided requirements.txt file:

pip install -r requirements.txt

💻 Usage Examples

Here’s how you can load the StarCoder2 model and generate code:

Python Code Example:

from transformers import AutoModelForCausalLM, AutoTokenizer

Load the model and tokenizer

modelname = "bigcode/starcoder2-15b" tokenizer = AutoTokenizer.frompretrained(modelname) model = AutoModelForCausalLM.frompretrained(modelname, trustremote_code=True)

Input prompt

prompt = "def fibonacci(n):" inputs = tokenizer(prompt, return_tensors="pt")

Generate code

outputs = model.generate(inputs["inputids"], maxnewtokens=50) print(tokenizer.decode(outputs[0], skipspecial_tokens=True))

JavaScript Code Example:

const { AutoModelForCausalLM, AutoTokenizer } = require('@huggingface/transformers');

async function generateCode() { const modelName = 'bigcode/starcoder2-15b'; const tokenizer = await AutoTokenizer.frompretrained(modelName); const model = await AutoModelForCausalLM.frompretrained(modelName);

const prompt = 'function calculateFactorial(num) {'; const inputs = tokenizer.encode(prompt, { return_tensors: 'pt' });

const output = await model.generate(inputs, { maxnewtokens: 50 }); console.log(tokenizer.decode(output[0])); }

generateCode();

📚 Additional Resources

Hugging Face Model Hub

Explore StarCoder2 on Hugging Face: StarCoder2-15B Model Card

Configuration Reference

Detailed configuration options are available in the documentation.

📖 Citation

If you use StarCoder2 in your research or projects, please cite the following:

@misc{starcoder2, author = {Huggingface and Friends}, title = {StarCoder2: A State-of-the-Art Coding Model}, year = {2025}, url = {https://huggingface.co/bigcode/starcoder2-15b/blob/main/README.md} }

🤝 Community and Support

We welcome contributions and feedback from the community! Here’s how you can get involved:

Report Issues: Submit issues on GitHub.

Contribute: Fork the repository and make pull requests to improve StarCoder2.

Join Discussions: Participate in discussions on Hugging Face Forums.

Thank you for using StarCoder2! 🚀