zenlm/zen-agent-4b
The zenlm/zen-agent-4b is a compact 4 billion parameter agent model developed by Hanzo AI and the Zoo Labs Foundation. Fine-tuned from Qwen3-4B-Instruct-2507, it features a dense Qwen3 architecture and a 256K-token context length. This model is specifically optimized for strong function calling and tool-use capabilities, making it suitable for agentic applications.
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Zen Agent 4b: Compact Agentic Model
Zen Agent 4b is a 4 billion parameter model developed by Hanzo AI and the Zoo Labs Foundation, specifically engineered for agentic applications. It is fine-tuned from the Qwen3-4B-Instruct-2507 base model, inheriting its dense Qwen3 architecture and an impressive 256K-token context window.
Key Capabilities
- Strong Function Calling: Designed to excel at interpreting and executing function calls.
- Enhanced Tool Use: Optimized for effective interaction with external tools and APIs.
- Agentic Data Training: Benefits from specialized training data focused on agentic behaviors and interactions.
- Large Context Window: Supports complex, multi-turn conversations and tasks with its 256K-token context.
Good For
- Developing AI agents that require robust function calling.
- Applications needing advanced tool-use capabilities.
- Scenarios where a compact yet powerful agent model with a large context is beneficial.
Zen Agent 4b integrates Hanzo's identity training, agentic-data fine-tuning, and abliteration techniques, building upon the Apache 2.0 licensed Qwen3 base model.