Harmonic-9B: Two-stage Qwen3.5-9B fine-tune for AI agents
What's Harmonic-9B?
Harmonic-9B is a fine-tuned version of Qwen3.5-9B specifically designed for AI agent applications. The developer is using a two-stage training approach: Stage 1 focuses on heavy reasoning training (already complete), while Stage 2 focuses on light tool-calling and agent fine-tuning (still in progress as of the announcement).
Technical Details
The goal is to combine strong structured reasoning with clean, reliable tool use while maintaining natural chat capabilities. For Stage 2, the developer has filtered a dataset of Hermes agent traces, which they've open-sourced on Hugging Face.
Key improvements in the filtered dataset:
- Self-correction: 6% → 63%
- Verification steps: 26% → 96%
- Thinking depth: +40%
- Valid JSON/tool calls: 100%
GGUF quantized versions are already available for download, though the developer notes they haven't run proper benchmarks yet because Stage 2 is still training. Early checks on the Stage 1 checkpoint showed good results for reasoning structure.
Current Status and Next Steps
The developer is seeking feedback on how Harmonic-9B behaves in agent harnesses like OpenClaw, LangGraph, and ReAct. They plan to share benchmark numbers once Stage 2 finishes and they can run proper agent evaluations. This work is part of ongoing research into high-signal data curation and staged fine-tuning approaches.
📖 Read the full source: r/LocalLLaMA
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