🧠 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