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 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 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:

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.

Cyanex
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PY-8b-1.0