Jay2003Bhatt/alpha-coder-14b

Model Overview

Jay2003Bhatt/alpha-coder-14b is a 14.8 billion parameter model built upon the Qwen/Qwen3-Coder-30B-A3B-Instruct architecture, tailored for specialized coding assistance. It supports a substantial context length of 32,768 tokens, enabling it to handle complex coding problems and longer code snippets. The project provides a structured approach for local execution, data preparation, and fine-tuning, emphasizing the use of high-quality coding instruction data.

Key Capabilities

• Code Generation & Correction: Excels at tasks like fixing bugs, generating code from descriptions, and correcting compiler/type errors.
• Refactoring & Review: Capable of handling refactoring requests and generating improved code based on review comments.
• Multi-language Support: Designed to provide multi-language explanations and executable solutions.
• Fine-tuning Framework: Includes scripts and configurations for QLoRA fine-tuning, allowing customization with specific datasets.
• Hardware Flexibility: Provides guidance for running on various hardware, including MacBook M-series chips (via GGUF) and cloud GPUs for fine-tuning.

Good for

• Developers: Ideal for programmers needing an AI assistant for daily coding tasks, from debugging to generating new functions.
• Customization: Users looking to fine-tune a coding model on proprietary or specialized datasets to enhance performance for specific domains.
• Educational Purposes: Can be used to generate examples, explain code, and assist in learning various programming concepts.
• High-Quality Code Tasks: Optimized for scenarios where clean, task-answer pairs are preferred over noisy, large-scale code dumps.