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DJLougen / Harmonic-27B-GGUF
Model's Last Updated: April 10 2026
text-generation
Introduction of Harmonic-27B-GGUF
Model Details of Harmonic-27B-GGUF
Harmonic-27B-GGUF
GGUF quantizations of DJLougen/Harmonic-27B — the flagship of the Harmonic series. A reasoning-focused fine-tune of Qwen 3.5 27B trained on structurally validated data. Every row passes automated quality gates. No junk, no filler, no shallow traces.
The name comes from harmonic analysis of reasoning patterns — the structural signal that separates genuine thinking from surface-level chain-of-thought.
Available Quantizations
| File | Quant | Size | Description |
|---|---|---|---|
| Harmonic-27B-F16.gguf | F16 | 51 GB | Full precision — no quality loss |
| Harmonic-27B-Q8_0.gguf | Q8_0 | 27 GB | 8-bit — near-lossless quality |
| Harmonic-27B-Q4_K_M.gguf | Q4_K_M | 16 GB | 4-bit — best balance of size and quality |
Training Approach
Same pipeline as Harmonic-9B. 799 curated rows — a small, precisely curated dataset instead of tens of thousands of unfiltered examples. The base model already has the knowledge from pretraining — the fine-tune teaches it a reasoning behavior pattern.
Every training row contains explicit self-correction ("wait, that's not right"), verification ("let me check by plugging back in"), and multi-path exploration ("alternatively, I could try..."). The data was generated from multiple frontier models and filtered through a custom structural quality pipeline that enforces reasoning depth, coherence, and flow patterns. 100% of rows pass all quality gates simultaneously.
Training Data Quality
The same reasoning data as Harmonic-9B and Harmonic-2B, curated using a custom structural process supervision pipeline:
| Metric | Value |
|---|---|
| Signal quality score | 78.7 mean (61.5 min, 90.0 max) |
| Thinking trace depth | 1,667 words average |
| Self-correction | 100% of rows (17.2 per row avg) |
| Verification | 100% of rows (10.3 per row avg) |
| Exploration | 100% of rows (6.3 per row avg) |
| Quality gate pass rate | 100% |
How It Compares
We ran our structural quality analysis against every major public reasoning dataset used for Opus/Qwen distillation. The results:
| Dataset | Rows | Think Words | Self-Correction | Verification | Exploration | Signal Score | Gate Pass |
|---|---|---|---|---|---|---|---|
| Harmonic (ours) | 799 | 1,667 | 100% | 100% | 100% | 78.7 | 100% |
| Crownelius/Opus-3300x | 2,160 | 188 | 5.9% | 22.6% | 5.2% | 28.0 | 0.1% |
| nohurry/Opus-Filtered | 2,326 | 191 | 6.7% | 24.1% | 5.3% | 28.5 | 0.1% |
| TeichAI/Opus-250x | 250 | 323 | 17.2% | 26.8% | 6.8% | 24.6 | 0.4% |
| Jackrong/Qwen-700x | 633 | 6,653 | 97.5% | 97.6% | 69.8% | 75.6 | 22.7% |
| Bespoke-Stratos-17k | 16,710 | 1,322 | 88.2% | 72.7% | 59.7% | 71.7 | 49.0% |
| glaiveai/reasoning-20m | 22M+ | 799 | 64.1% | 41.4% | 37.3% | 46.2 | 12.8% |
| KingNish/reasoning-20k | 19,944 | 132 | 0.7% | 4.2% | 4.3% | 27.4 | 0.0% |
Speculative Decoding
Harmonic-27B pairs with Harmonic-2B for speculative decoding. Both models share the same training data, reasoning format, and architecture family (Qwen 3.5), which keeps draft token acceptance rates high.
from transformers import AutoModelForCausalLM
target = AutoModelForCausalLM.from_pretrained("DJLougen/Harmonic-27B")
draft = AutoModelForCausalLM.from_pretrained("DJLougen/Harmonic-2B")
outputs = target.generate(
**inputs,
assistant_model=draft,
max_new_tokens=512,
)
Usage
llama.cpp
llama-cli -m Harmonic-27B-Q4_K_M.gguf -p "What is the sum of the first 100 prime numbers?" -n 2048
Ollama
# Create a Modelfile
echo 'FROM ./Harmonic-27B-Q4_K_M.gguf' > Modelfile
ollama create harmonic-27b -f Modelfile
ollama run harmonic-27b
Reasoning format
The model uses think blocks for reasoning:
<|thinking|>
The user is asking about X. Let me consider two approaches...
Approach 1: ...
Approach 2: ...
I will go with Approach 1 because...
Wait, I need to be careful here - this assumes Y, which may not hold.
Let me verify by checking a special case...
Yes, that confirms the result.
<|/thinking|>
[Final answer here]
Training Configuration
base_model: unsloth/Qwen3.5-27B
dataset: 799 curated reasoning rows
epochs: 1
learning_rate: 1e-4
lr_scheduler: cosine
warmup_ratio: 0.1
max_seq_length: 8192
lora_rank: 32
lora_alpha: 32
dropout: 0.05
micro_batch_size: 1
gradient_accumulation_steps: 4
weight_decay: 0.01
Intended Use
- Reasoning tasks requiring genuine multi-step thinking
- Mathematical problem-solving with self-correction
- Code analysis and generation with structured verification
- General conversation (conversational ability preserved through training design)
- Target model for speculative decoding with Harmonic-2B
- Base model for Stage 2 agentic fine-tuning
Limitations
- Reasoning traces can be verbose for simple questions
- Not optimized for tool calling — see Harmonic-Hermes-9B for agentic use
- Benchmark evaluation is ongoing
Architecture
- Base : Qwen 3.5 27B (27.36B parameters)
- Training : LoRA fine-tuning, merged into base weights
- Precision : BF16 (original), quantized to F16/Q8_0/Q4_K_M
- Context : 8192 tokens
License
Apache 2.0 — same as the base model. All training data is from Apache 2.0 or MIT licensed sources. Fully commercial use permitted.
Links
- Original model: DJLougen/Harmonic-27B
- 9B variant: DJLougen/Harmonic-9B
- 9B GGUF: DJLougen/Harmonic-9B-GGUF
- 2B draft model: DJLougen/Harmonic-2B
- Agentic variant: DJLougen/Harmonic-Hermes-9B
Runs of DJLougen Harmonic-27B-GGUF on huggingface.co
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More Information About Harmonic-27B-GGUF huggingface.co Model
Url of Harmonic-27B-GGUF
Harmonic-27B-GGUF huggingface.co Url
Provider of Harmonic-27B-GGUF huggingface.co
Other API from DJLougen
Updated:April 04 2026
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