TeichAI/Qwen3-4B-Thinking-2507-GPT-5-Codex-Distill
TeichAI/Qwen3-4B-Thinking-2507-GPT-5-Codex-Distill is a 4 billion parameter Qwen3-based language model developed by TeichAI, fine-tuned specifically for code generation tasks. This model leverages 1000 examples from OpenAI's GPT-5-Codex dataset, making it highly optimized for understanding and generating code. It offers a 32768 token context length, providing ample capacity for complex coding prompts and solutions.
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TeichAI/Qwen3-4B-Thinking-2507-GPT-5-Codex-Distill Overview
This model, developed by TeichAI, is a 4 billion parameter variant of the Qwen3 architecture, specifically fine-tuned for advanced code generation. It builds upon the unsloth/Qwen3-4B-Thinking-2507 base model and was trained using 1000 high-quality examples sourced from OpenAI's proprietary GPT-5-Codex dataset. The fine-tuning process was accelerated using Unsloth and Huggingface's TRL library, enabling efficient development.
Key Capabilities
- Code Generation: Specialized in generating high-quality code, benefiting from distillation of GPT-5-Codex examples.
- Qwen3 Architecture: Inherits the robust capabilities of the Qwen3 base model.
- Efficient Training: Utilizes Unsloth for faster training, indicating potential for further efficient fine-tuning.
- Extended Context: Supports a 32768 token context length, suitable for handling larger codebases or complex programming problems.
Good For
- Software Development: Assisting developers with writing, debugging, and understanding code.
- Code Completion & Generation: Generating functions, classes, or entire code snippets based on natural language prompts.
- Educational Tools: Creating programming tutorials or interactive coding environments.
- Research in Code LLMs: Exploring the effectiveness of distilling knowledge from advanced proprietary models like GPT-5-Codex into smaller, open-source alternatives.