Harmonic-27B

The flagship of the Harmonic series. A reasoning-focused fine-tune of Qwen 3.5 27B trained on the same structurally validated data as Harmonic-9B and Harmonic-2B. 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.

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,
)

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

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("DJLougen/Harmonic-27B")
tokenizer = AutoTokenizer.from_pretrained("DJLougen/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]

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
• 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

• 9B variant: DJLougen/Harmonic-9B
• 9B GGUF: DJLougen/Harmonic-9B-GGUF
• 2B draft model: DJLougen/Harmonic-2B
• Agentic variant: DJLougen/Harmonic-Hermes-9B