B S J Code 1 Stable

Introduction

BSJCode-1-Stable is a Java code optimization and bug-fixing model developed by BSAtlas. It leverages Hugging Face's Transformers library and is designed to analyze Java code, identify bugs or inefficiencies, and provide optimized corrections.

Architecture

BSJCode-1-Stable utilizes the Transformers library, specifically designed for causal language modeling. It supports 4-bit quantization for efficient deployment, leveraging techniques like NF4 quantization, double quantization, and enabling FP32 CPU offloading for improved performance.

Training

The model is trained to recognize patterns in Java code that may indicate bugs or inefficiencies and provide optimized alternatives. It uses a language generation approach to output corrected and enhanced code snippets.

Guide: Running Locally

To run the model locally:

1. Install Dependencies: Ensure you have Python, PyTorch, and Transformers installed.
2. Load Model and Tokenizer: Use the AutoModelForCausalLM and AutoTokenizer from the transformers library.
3. Configure Quantization: Set up the BitsAndBytesConfig for 4-bit quantization.
4. Prepare Input: Tokenize a Java code snippet.
5. Generate Output: Use the model to generate the optimized code.
6. Extract Results: Decode and extract the fixed Java code.

For enhanced performance, it is recommended to use cloud GPUs such as those provided by AWS, Google Cloud, or Azure.

License

BSJCode-1-Stable is released under the MIT License, allowing for wide use and distribution with minimal restrictions.