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.
1✅ "Design a 3-week beginner Python curriculum including AI tools."
2✅ "Write a Python function to recursively scan JSON for a key, without using recursion."
3✅ "Read a folder of images and extract text using OCR, save to files."
4✅ "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.
