Total runs:
1.5K
Run Growth:
-564
Growth Rate:
-37.50%
Sponsored by
YouArt
DJLougen / Harmonic-Hermes-9B-GGUF
Model's Last Updated: April 08 2026
Introduction of Harmonic-Hermes-9B-GGUF
Model Details of Harmonic-Hermes-9B-GGUF
☕ 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-GGUF
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.
Available Quantizations
| File | Quant | Size | Use Case |
|---|---|---|---|
Qwen3.5-9B-Harmonic.F16.gguf
|
F16 | ~18 GB | Maximum quality, needs 24GB+ VRAM |
Harmonic-Hermes-9B-Q8_0.gguf
|
Q8_0 | ~9.5 GB | Near-lossless, 16GB VRAM |
Qwen3.5-9B-Harmonic.Q4_K_M.gguf
|
Q4_K_M | ~5.3 GB | Best 4-bit for quality |
Vision (Multimodal)
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
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
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.
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
- 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
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
License
Apache 2.0 — same as the base model. Fully commercial use permitted.
Links
- Stage 2 full weights: DJLougen/Harmonic-Hermes-9B
- Stage 1 reasoning backbone: DJLougen/Harmonic-9B
- Stage 1 GGUF quantizations: DJLougen/Harmonic-9B-GGUF
- Training data: DJLougen/hermes-agent-traces-filtered
Runs of DJLougen Harmonic-Hermes-9B-GGUF on huggingface.co
695
Total runs
146
24-hour runs
121
3-day runs
89
7-day runs
-331
30-day runs
More Information About Harmonic-Hermes-9B-GGUF huggingface.co Model
Url of Harmonic-Hermes-9B-GGUF
Harmonic-Hermes-9B-GGUF huggingface.co Url
Provider of Harmonic-Hermes-9B-GGUF huggingface.co
Other API from DJLougen
Updated:April 04 2026
Total runs:
1.4K
Run Growth:
-3.4K
Growth Rate:
-234.70%
Total runs:
643
Run Growth:
69
Growth Rate:
10.73%
Total runs:
512
Run Growth:
-2.1K
Growth Rate:
-404.69%
Total runs:
496
Run Growth:
-269
Growth Rate:
-54.23%
Total runs:
400
Run Growth:
-3.0K
Growth Rate:
-749.50%
Total runs:
242
Run Growth:
-6.4K
Growth Rate:
-2660.33%
Total runs:
228
Run Growth:
-634
Growth Rate:
-278.07%
Total runs:
205
Run Growth:
27
Growth Rate:
13.17%
Total runs:
186
Run Growth:
-3.0K
Growth Rate:
-1627.42%
Total runs:
184
Run Growth:
-247
Growth Rate:
-134.24%
Total runs:
179
Run Growth:
-1.7K
Growth Rate:
-975.98%
Total runs:
168
Run Growth:
108
Growth Rate:
64.29%
Total runs:
154
Run Growth:
-2.1K
Growth Rate:
-1349.35%
Total runs:
141
Run Growth:
-1.7K
Growth Rate:
-1226.95%
Total runs:
136
Run Growth:
-307
Growth Rate:
-225.74%
Total runs:
129
Run Growth:
-3.2K
Growth Rate:
-2482.17%
Total runs:
122
Run Growth:
-984
Growth Rate:
-806.56%
Total runs:
119
Run Growth:
-1.3K
Growth Rate:
-1058.82%
Total runs:
118
Run Growth:
-186
Growth Rate:
-157.63%
Total runs:
111
Run Growth:
-254
Growth Rate:
-228.83%
Total runs:
111
Run Growth:
-797
Growth Rate:
-718.02%
Total runs:
106
Run Growth:
-930
Growth Rate:
-877.36%
Total runs:
96
Run Growth:
-303
Growth Rate:
-315.63%
Total runs:
93
Run Growth:
68
Growth Rate:
73.12%
Total runs:
92
Run Growth:
-1.3K
Growth Rate:
-1421.74%
Total runs:
91
Run Growth:
24
Growth Rate:
26.37%
Total runs:
89
Run Growth:
89
Growth Rate:
100.00%
Total runs:
83
Run Growth:
34
Growth Rate:
40.96%
Total runs:
75
Run Growth:
-142
Growth Rate:
-189.33%
Total runs:
72
Run Growth:
-192
Growth Rate:
-266.67%
Total runs:
66
Run Growth:
-174
Growth Rate:
-263.64%
Total runs:
59
Run Growth:
-960
Growth Rate:
-1627.12%
Total runs:
57
Run Growth:
-21
Growth Rate:
-36.84%
Total runs:
56
Run Growth:
-368
Growth Rate:
-657.14%
Total runs:
55
Run Growth:
-14
Growth Rate:
-25.45%
Total runs:
54
Run Growth:
54
Growth Rate:
100.00%
Total runs:
53
Run Growth:
-811
Growth Rate:
-1530.19%
Total runs:
49
Run Growth:
49
Growth Rate:
100.00%
Total runs:
48
Run Growth:
38
Growth Rate:
79.17%
Total runs:
47
Run Growth:
-492
Growth Rate:
-1046.81%
Total runs:
43
Run Growth:
-445
Growth Rate:
-1034.88%
Total runs:
37
Run Growth:
-389
Growth Rate:
-1051.35%
Total runs:
32
Run Growth:
32
Growth Rate:
100.00%
Total runs:
30
Run Growth:
-201
Growth Rate:
-670.00%
Total runs:
28
Run Growth:
-28
Growth Rate:
-100.00%
Total runs:
28
Run Growth:
-45
Growth Rate:
-160.71%
Total runs:
25
Run Growth:
-7
Growth Rate:
-28.00%
Total runs:
23
Run Growth:
-81
Growth Rate:
-352.17%
Total runs:
22
Run Growth:
-6
Growth Rate:
-27.27%
Total runs:
21
Run Growth:
-201
Growth Rate:
-957.14%
Total runs:
21
Run Growth:
-3
Growth Rate:
-14.29%
Total runs:
20
Run Growth:
-21
Growth Rate:
-105.00%
Total runs:
18
Run Growth:
-6
Growth Rate:
-33.33%
Total runs:
18
Run Growth:
-517
Growth Rate:
-2872.22%
Total runs:
18
Run Growth:
-135
Growth Rate:
-750.00%
Total runs:
12
Run Growth:
4
Growth Rate:
33.33%
Total runs:
12
Run Growth:
-10
Growth Rate:
-83.33%
Total runs:
11
Run Growth:
0
Growth Rate:
0.00%
Total runs:
8
Run Growth:
-28
Growth Rate:
-350.00%
Total runs:
5
Run Growth:
-138
Growth Rate:
-2760.00%
Total runs:
3
Run Growth:
-1
Growth Rate:
-33.33%
Total runs:
2
Run Growth:
-84
Growth Rate:
-4200.00%
Total runs:
1
Run Growth:
-105
Growth Rate:
-10500.00%
Total runs:
1
Run Growth:
-3
Growth Rate:
-300.00%
Total runs:
0
Run Growth:
0
Growth Rate:
0.00%
Total runs:
0
Run Growth:
0
Growth Rate:
0.00%
Total runs:
0
Run Growth:
0
Growth Rate:
0.00%
Total runs:
0
Run Growth:
0
Growth Rate:
0.00%
Total runs:
0
Run Growth:
0
Growth Rate:
0.00%
Total runs:
0
Run Growth:
0
Growth Rate:
0.00%
Total runs:
0
Run Growth:
0
Growth Rate:
0.00%
Total runs:
0
Run Growth:
0
Growth Rate:
0.00%
Total runs:
0
Run Growth:
0
Growth Rate:
0.00%