qwegpt
qwegpt is a fine-tuned conversational AI model designed to provide clear, helpful, and structured responses with a natural, ChatGPT-like behavior. It is optimized for coding assistance, creative tasks, and general problem-solving.
🧠 Model Details
Model Description
qwegpt is built on top of Qwen2.5-0.5B-Instruct and further fine-tuned to improve helpfulness, clarity, and user guidance. The model focuses on delivering concise yet informative answers, adapting to both beginners and more advanced users.
It is designed to:
- Explain concepts in a simple and structured way
- Assist with coding and debugging
- Generate creative content (prompts, ideas, UI concepts)
- Provide step-by-step guidance when needed
🚀 Intended Use
Primary Use Cases
- Coding help and debugging guidance
- Learning and explaining technical concepts
- Generating creative prompts (art, UI, ideas)
- General-purpose assistant tasks
Out-of-Scope Use
- High-risk domains requiring strict accuracy (e.g., legal, medical advice)
- Tasks requiring guaranteed factual correctness
⚙️ Training Details
Base Model
- Qwen/Qwen2.5-0.5B-Instruct
- unsloth/Qwen2.5-0.5B-Instruct
Fine-Tuning Approach
The model was fine-tuned on a custom dataset focused on:
- Helpful conversational behavior
- Clear explanations and structured responses
- Coding-related Q&A
- Creative prompt generation
The dataset emphasizes:
- Practical problem-solving
- Step-by-step guidance
- Natural and engaging tone
📊 Capabilities
- Strong at explaining concepts clearly
- Good at generating structured answers
- Helpful for coding and debugging tasks
- Can generate creative prompts and UI ideas
⚠️ Limitations
- May produce incorrect or outdated information
- Limited reasoning compared to larger models
- Can struggle with highly complex or multi-step logic
- Not optimized for real-time or factual verification tasks
💡 Example Usage
Input:
"How do I center a div?"
Output:
"The cleanest way is to use flexbox on the parent container, which allows you to center elements both horizontally and vertically with minimal effort."
🧩 Future Improvements
- Better reasoning and multi-step problem solving
- Improved factual accuracy
- Expanded dataset for more domains
- Enhanced creativity and style control
📜 License
This model is released under the MIT License.
🤝 Acknowledgements
- Qwen team for the base model
- Unsloth for efficient fine-tuning tools
- Open-source community for datasets and inspiration
