Harmonic-9B - Two-stage Qwen3.5-9B fine-tune (Stage 2 still training)

Hey r/LocalLLaMA,

I just uploaded Harmonic-9B, my latest Qwen3.5-9B fine-tune aimed at agent use.

Current status:

• Stage 1 (heavy reasoning training) is complete

• Stage 2 (light tool-calling / agent fine-tune) is still training right now

The plan is to combine strong structured reasoning with clean, reliable tool use while trying to avoid making normal chat feel stiff or overly verbose.

Filtered dataset for Stage 2: I open-sourced the filtered version of the Hermes agent traces I’m using for the second stage:

https://huggingface.co/datasets/DJLougen/hermes-agent-traces-filtered

Key improvements after filtering:

• Self-correction: 6% → 63%

• Verification steps: 26% → 96%

• Thinking depth: +40%

• Valid JSON/tool calls: 100%

GGUF quants are already available here:

https://huggingface.co/DJLougen/Harmonic-9B-GGUF

I haven’t run proper benchmarks yet because Stage 2 is still training. Early checks on the Stage 1 checkpoint looked good for reasoning structure. Will share numbers once Stage 2 finishes and I can do real agent evals.

If you give it a spin, I’d appreciate any feedback — especially how it behaves in agent harnesses (OpenClaw, LangGraph, ReAct, etc.).

This is part of my ongoing work on high-signal data curation and staged fine-tuning. More updates coming soon.