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DJLougen / Harmonic-Hermes-9B
Model's Last Updated: April 08 2026
text-generation
Introduction of Harmonic-Hermes-9B
Model Details of Harmonic-Hermes-9B
☕ Support This Work
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.
Harmonic-Hermes-9B
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 tool-calling and agent interaction data. Inherits Stage 1's reasoning depth and adds structured action capabilities.
What This Model Does
- 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.
How Our Training Data Compares
Quality Comparison
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
Marker density across thinking traces — the filtered set shows tighter, more consistent reasoning structure.
Conversation Structure
Category Distribution
Training data: DJLougen/hermes-agent-traces-filtered
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("DJLougen/Harmonic-Hermes-9B")
tokenizer = AutoTokenizer.from_pretrained("DJLougen/Harmonic-Hermes-9B")
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>
Architecture
- Base : Harmonic-9B (Stage 1 reasoning fine-tune of Qwen 3.5 9B)
- Parameters : 9.65B
- Training : LoRA fine-tuning, merged into base weights
- Precision : BF16
- Context : 8192 tokens
Intended Use
- Agentic workflows with tool calling and function execution
- Multi-turn assistant interactions requiring structured reasoning
- Local inference as an always-on agent backbone
- Research into reasoning-grounded agent behavior
Limitations
- 9B parameter model — not suitable for tasks requiring extensive world knowledge
- Agent capabilities are shaped by the training data distribution
- Benchmark evaluation is ongoing
License
Apache 2.0 — same as the base model. Fully commercial use permitted.
Links
- Stage 1 reasoning backbone: DJLougen/Harmonic-9B
- GGUF quantizations (Stage 1): DJLougen/Harmonic-9B-GGUF
Runs of DJLougen Harmonic-Hermes-9B on huggingface.co
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More Information About Harmonic-Hermes-9B huggingface.co Model
Url of Harmonic-Hermes-9B
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Updated:April 04 2026
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