🧠 AgenticCoder‑4B

AgenticCoder‑4B is a compact 4B parameter language model designed for autonomous agent workflows and intelligent code reasoning. It merges the planning and tool-use strengths of Jan-nano with the coding and logic capabilities of Qwen3‑4B‑Code‑Reasoning, creating a balanced model ideal for real-world assistant scenarios, research agents, and smart development tools.

✨ Key Features

🔁 Agentic Planning & MCP Alignment
Trained on datasets and architectures optimized for multi-step reasoning, task decomposition, and memory–contextual workflows.
💻 Code Understanding & Reasoning
Strong capabilities in Python code generation, script explanation, optimization, and multi-turn task development.
🧰 Tool Use Simulation
Handles realistic tool interaction prompts such as CSV analysis, OCR, and file parsing in code.
📦 Compact & Efficient (4B)
Lightweight enough for cost-efficient deployment, edge device integration, and fine-tuning.

🛠️ Merge Details

Merge Method: SLERP (t = 0.4)
Base Model: Menlo/Jan-nano
Merged With: ertghiu256/qwen3-4b-code-reasoning
Precision: float16
Tokenizer Source: Menlo/Jan-nano

📎 Example Use Cases

✅ "Design a 3-week beginner Python curriculum including AI tools."
✅ "Write a Python function to recursively scan JSON for a key, without using recursion."
✅ "Read a folder of images and extract text using OCR, save to files."
✅ "Summarize trends in a sales CSV and visualize monthly performance."

📁 License & Use

This model is provided for research and development use under the terms of the base models’ respective licenses. Please ensure compliance before commercial usage.

🧬 Citation

If you use this model, consider citing it as:

@misc{agenticcoder4b2025,
  title={AgenticCoder-4B: A Compact Agent + Code Reasoning Model},
  author={Yasser, M.},
  year={2025},
  url={https://huggingface.co/your-username/AgenticCoder-4B}
}

🤝 Acknowledgements

Menlo/Jan-nano by Menlo Systems
Qwen3‑4B‑Code‑Reasoning by ertghiu256
MergeKit, SLERP, Hugging Face

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AgenticCoder-4B