I'm a PhD student in visual neuroscience at the University of Toronto who also happens to spend way too much time fine-tuning, merging, and quantizing open-weight models on rented H100s and a local DGX Spark. It's a hobby that got out of hand. If my uploads have been useful to you, consider buying a PhD student a coffee. It goes a long way toward keeping these experiments running.
GGUF quantizations of Harmonic-Hermes-9B for local inference with llama.cpp, Ollama, LM Studio, and other GGUF-compatible runtimes.
Harmonic-Hermes-9B is the Stage 2 agentic fine-tune of Harmonic-9B — a dedicated tool-calling and agent model built on top of a strong reasoning backbone.
Where Harmonic-9B teaches the model how to think, Harmonic-Hermes-9B teaches it how to act — structured tool use, multi-turn agent workflows, and function calling, all grounded in the reasoning depth from Stage 1.
Stage 1 — Harmonic-9B: Heavy reasoning fine-tune on privately generated, structurally validated data. Every row passes strict quality gates. The thinking backbone.
Stage 2 (this model): Agentic fine-tune on hermes-agent-traces-filtered — 3,679 structurally validated agent traces with deep reasoning, tool calling, and multi-turn workflows.
This model includes vision projectors for multimodal inference. Use with llama.cpp's --mmproj flag for image understanding tasks.
How Our Training Data Compares
Quality Comparison
Quality Comparison
Metrics Summary
Metrics Summary
We ran the same structural quality analysis used for Stage 1 against comparable public agentic datasets. The results show why starting from quality-filtered data matters:
Metric
Harmonic Traces (ours)
Carnice GLM-5 (kai-os)
Rows
3,679
1,627
Source model
Multiple frontier models
GLM-5 via OpenRouter
Think block depth
581 words avg
40 words avg
Self-correction
63.0%
29.7%
Verification
95.9%
63.7%
Alternative exploration
43.7%
51.3%
Valid JSON (all tool calls)
100%
100%
Tool calls per conversation
18.5
5.4
Messages per conversation
32.1
12.1
Multi-turn (>5 messages)
97.8%
89.6%
The critical gap is reasoning depth: 581 vs 40 words in think blocks. Carnice traces plan briefly then act — the model learns tool-call formatting but not deliberation. Our traces contain 14x deeper reasoning before every action, with nearly universal verification (96% vs 64%) and twice the self-correction rate.
The conversation depth also matters for agent training. Our traces average 32 messages and 18 tool calls per trajectory — complete agentic sessions, not short dispatches. This teaches the model to maintain coherent state across extended multi-step workflows.
Reasoning Flow
Reasoning Flow
Marker density across thinking traces — the filtered set shows tighter, more consistent reasoning structure.
Tool calling / function calling — structured JSON tool use in the Hermes agent format
Multi-turn agent workflows — maintains coherent state across extended tool-use conversations
Reasoning-grounded decisions — inherits Harmonic-9B's self-correction, verification, and exploration before committing to actions
Training Approach
Harmonic-Hermes-9B is a Stage 2 fine-tune of Harmonic-9B, trained on hermes-agent-traces-filtered — 3,679 structurally validated agent traces with deep reasoning, tool calling, and multi-turn workflows.
The key insight: most agent models are fine-tuned directly from base models or generic instruct tunes. They learn tool-call formatting but not when or why to use tools. By starting from a model that already reasons deeply (Stage 1), the agent behaviors are grounded in genuine multi-step thinking rather than pattern-matched tool invocations.
Usage
Ollama
ollama run DJLougen/Harmonic-Hermes-9B-GGUF
llama.cpp
./llama-cli -m Harmonic-Hermes-9B-Q8_0.gguf -p "Use the available tools to..." -n 4096
LM Studio
Download any quantization and load in LM Studio. The model follows standard ChatML formatting.
Reasoning + Tool Use
The model uses <think> blocks for reasoning before acting:
<think>
The user wants to check the weather in Toronto. I have a get_weather tool available.
Let me call it with the right parameters...
</think>
<tool_call>
{"name": "get_weather", "arguments": {"location": "Toronto, Canada"}}
</tool_call>
Intended Use
Agentic workflows with tool calling and function execution
