Create README.md

README.md

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+---
+license: mit
+datasets:
+- Vezora/Tested-143k-Python-Alpaca
+language:
+- en
+base_model:
+- meta-llama/Llama-3.2-3B
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- code
+- Code Generation
+- Code Debugging
+- Generative AI
+- Llama
+- Programming Assistance
+- Fine-tuned Model
+---
+# PY-8B-1.0
+
+## Model Overview
+**PY-8B-1.0** is a highly optimized generative AI model fine-tuned for Python-related tasks. Built on the robust **Llama-3.2-3B** base model, PY-8B-1.0 leverages state-of-the-art training techniques to provide reliable, high-quality assistance for Python programming. This model has been extensively trained using the [Vezora/Tested-143k-Python-Alpaca](https://huggingface.co/datasets/Vezora/Tested-143k-Python-Alpaca) dataset, which ensures a comprehensive understanding of Python's syntax, libraries, and coding patterns.
+
+Designed with developers, educators, and learners in mind, PY-8B-1.0 offers a versatile solution for generating, debugging, and explaining Python code. The model's architecture, based on the Llama framework, has been further optimized for performance, supporting both low-bit (4-bit) and standard (16-bit) precision formats to meet diverse computational requirements. Whether you're a beginner or an experienced developer, PY-8B-1.0 aims to simplify Python programming and enhance productivity.
+
+---
+
+## Model Details
+
+### Model Description
+- **Architecture**: Llama
+- **Base Model**: Llama-3.2-3B
+- **Dataset**: Vezora/Tested-143k-Python-Alpaca
+- **GGUFFormat**: 
+  - Q4_K_M (4-bit)
+  - F16 (16-bit) 
+- **Training Framework**: unsloth
+
+### Key Features
+- Generate Python code snippets based on user-provided prompts.
+- Debug Python scripts by identifying errors and providing potential fixes.
+- Explain Python code with detailed comments and logic breakdowns.
+- Provide assistance for common Python-related queries, including best practices, algorithm design, and library usage.
+
+The model is designed to adapt to a wide range of Python development scenarios, making it a reliable tool for both casual and professional use.
+
+---
+
+## Uses
+
+### Intended Use
+- **Programming Assistance**: Automate repetitive coding tasks, debug code efficiently, and boost developer productivity.
+- **Education**: Support Python learners by breaking down complex programming concepts and offering step-by-step guidance.
+- **Code Explanation**: Provide detailed explanations for code functionality, helping users understand underlying logic and structure.
+- **Algorithm Design**: Assist in creating efficient algorithms and troubleshooting logic errors.
+
+### Out of Scope Use
+- **Non-Python Programming**: The model is tailored specifically for Python and may underperform with other programming languages.
+- **Critical Systems**: The model's outputs should not be used directly in critical systems without rigorous validation.
+- **Highly Specialized Tasks**: Domain-specific Python applications may require additional fine-tuning for optimal results.
+
+---
+
+## Bias, Risks, and Limitations
+- **Bias**: The model is optimized for Python tasks and may exhibit bias toward examples seen during training. It may not perform well on highly unconventional or niche use cases.
+- **Risks**: Outputs may include incomplete, incorrect, or suboptimal code. Users should always validate and test generated code.
+- **Limitations**: While powerful, the model lacks contextual awareness beyond the input prompt and does not inherently understand real-world constraints or requirements. Additionally, its understanding is confined to the Python programming domain.
+
+---
+
+## Training Details
+
+### Training Data
+The model was trained on the [Vezora/Tested-143k-Python-Alpaca](https://huggingface.co/datasets/Vezora/Tested-143k-Python-Alpaca) dataset. This dataset includes:
+- Python syntax and usage examples.
+- Debugging scenarios with annotated solutions.
+- Advanced topics such as machine learning pipelines, data manipulation, and performance optimization.
+- A mix of beginner, intermediate, and advanced-level Python challenges to ensure comprehensive coverage.
+
+### Training Procedure
+- **Framework**: Trained using **unsloth**, leveraging its robust optimization capabilities.
+- **Techniques**: The training process incorporated fine-tuning techniques to enhance generalization and precision for Python tasks.
+- **Validation**: The model underwent iterative testing on a wide range of Python problems to ensure consistent and reliable performance.
+
+### Training Hyperparameters
+- **Learning Rate**: Dynamically adjusted during training to balance convergence and stability.
+- **Batch Size**: Configured based on the model’s architecture and hardware resources.
+- **Epochs**: Optimized to ensure the model achieves high performance without overfitting.
+- **Precision Formats**: Trained in both 4-bit (Q4_K_M) and 16-bit (F16) formats to support diverse deployment environments.
+
+---
+
+## Getting Started
+
+### How to Use
+You can load and use the model via the Hugging Face library:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# Load the model and tokenizer
+model = AutoModelForCausalLM.from_pretrained("Cyanex/PY-8b-1.0")
+tokenizer = AutoTokenizer.from_pretrained("Cyanex/PY-8b-1.0")
+
+# Example prompt
+prompt = "Write a Python function to check if a number is prime."
+inputs = tokenizer(prompt, return_tensors="pt")
+outputs = model.generate(**inputs)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+This snippet demonstrates how to interact with the model for generating Python code. Replace the `prompt` with your specific query to explore its full capabilities.
+
+---
+
+## Acknowledgments
+Special thanks to the creators of the **Llama-3.2-3B** base model and the contributors to the **Vezora/Tested-143k-Python-Alpaca** dataset. Their work laid the foundation for this project and enabled the creation of PY-8B-1.0. Additionally, gratitude goes to the Hugging Face community for providing the tools and resources necessary to develop and share this model.
+
+---
+
+## License
+This model is shared under the terms and conditions outlined by its license. Please ensure compliance with the license before use.
+
+For questions or contributions, feel free to contact the creator on Hugging Face or via LinkedIn.