hasan-toontech/

codegen-phaser

CodeGen-Mono 350M Fine-Tuned for Phaser.js This model is a fine-tuned version of the CodeGen-Mono 350M, specifically optimized for generating Phaser.js code based on user prompts. Phaser.js is a popular 2D game framework used for creating web-based games, and this model aims to assist developers by generating relevant game code snippets, enhancing productivity and creativity.

Base Model: CodeGen-Mono 350M Training Data: Fine-tuned on a custom dataset of Phaser.js code examples, covering various aspects like User Input Handling, Audio Management, Text Display, Tilemap Handling, Scene Management, Camera Controls, and Particles and Effects.

Purpose: To generate Phaser.js code snippets based on natural language prompts, helping developers to quickly prototype and implement game features.

Key Features User-Friendly: Accepts plain English prompts and translates them into functional Phaser.js code. Versatile: Capable of handling a variety of game development tasks, from simple input handling to complex scene management. Optimized for Game Development: Specifically fine-tuned on a wide range of Phaser.js use cases to provide accurate and efficient code generation.

Use Cases Game Development Prototyping: Quickly generate code snippets to prototype game mechanics and features. Learning Tool: A valuable resource for beginners learning Phaser.js, providing examples and code structure. Coding Assistance: Acts as an intelligent code assistant for experienced developers, helping to reduce development time and improve workflow.

How to Use You can use this model with the Hugging Face transformers library or integrate it into your custom applications using the provided API. Here’s a basic example:

from transformers import pipeline

codegen_pipeline = pipeline("text2text-generation", model="your-huggingface-username/CodeGen-Mono-350M-Phaser")

prompt = "Create a Phaser.js code snippet to move a sprite with arrow keys." response = codegen_pipeline(prompt)

print(response[0]['generated_text'])

Model Limitations Phaser.js Specific: The model is specialized for Phaser.js and may not generalize well to other frameworks or coding languages. Code Quality: Generated code might require additional refinement and testing for edge cases.

Future Work Planned improvements include expanding the training dataset, incorporating more advanced game development scenarios, and enhancing model accuracy and usability.

Feedback and Contributions Your feedback is invaluable! If you encounter any issues or have suggestions, please feel free to open an issue or contribute directly to the repository.