DJLougen
/

Harmonic-27B

@@ -1,167 +1,21 @@
 ---
-language:
-- en
-license: apache-2.0
-library_name: transformers
 tags:
-- reasoning
-- qwen3.5
-- conversational
 - unsloth
-- self-correction
-- chain-of-thought
-- speculative-decoding
-base_model: unsloth/Qwen3.5-27B
-pipeline_tag: text-generation
 ---
-# Harmonic-27B
-![Harmonic-27B](harmonic27B.jpeg)
-The flagship of the Harmonic family. A reasoning-focused fine-tune of [Qwen 3.5 27B](https://huggingface.co/unsloth/Qwen3.5-27B) trained on structurally validated data where every row passes automated quality gates. No junk, no filler, no shallow traces.
-Scales the same proven training approach from [Harmonic-9B](https://huggingface.co/DJLougen/Harmonic-9B) to 27B parameters. Pairs with [Harmonic-2B](https://huggingface.co/DJLougen/Harmonic-2B) as a draft model for speculative decoding.
-## The Harmonic Family
-| Model | Parameters | Role |
-|---|---|---|
-| [Harmonic-2B](https://huggingface.co/DJLougen/Harmonic-2B) | 2.3B | Draft model for speculative decoding |
-| [Harmonic-9B](https://huggingface.co/DJLougen/Harmonic-9B) | 9.65B | Mid-range reasoning backbone |
-| [Harmonic-Hermes-9B](https://huggingface.co/DJLougen/Harmonic-Hermes-9B) | 9.65B | Stage 2 agentic variant (tool calling) |
-| **Harmonic-27B** | **27B** | **Flagship reasoning model** |
-All models share the same training data and reasoning format, enabling speculative decoding across the family with high acceptance rates.
-## 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
-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
-The same structural quality analysis run against every major public reasoning dataset:
-| 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% |
-## 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
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
-model = AutoModelForCausalLM.from_pretrained("DJLougen/Harmonic-27B")
-tokenizer = AutoTokenizer.from_pretrained("DJLougen/Harmonic-27B")
-```
-### With speculative decoding (Harmonic-2B as draft)
-```python
-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,
-)
-```
-### 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
-- Complex reasoning tasks requiring deep multi-step thinking
-- Mathematical problem-solving with self-correction and verification
-- Code analysis, generation, and debugging with structured reasoning
-- General conversation (conversational ability preserved through training design)
-- Base model for Stage 2 agentic fine-tuning (Harmonic-Hermes-27B)
-- Target model for speculative decoding with Harmonic-2B
-## Limitations
-- 27B parameters - requires significant compute (single A100 80GB or equivalent)
-- Reasoning traces can be verbose for simple questions
-- Not optimized for tool calling - agentic Stage 2 variant planned
-- Benchmark evaluation is ongoing
-## Architecture
-- **Base**: Qwen 3.5 27B
-- **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
-- Draft model: [DJLougen/Harmonic-2B](https://huggingface.co/DJLougen/Harmonic-2B)
-- 9B variant: [DJLougen/Harmonic-9B](https://huggingface.co/DJLougen/Harmonic-9B)
-- 9B GGUF: [DJLougen/Harmonic-9B-GGUF](https://huggingface.co/DJLougen/Harmonic-9B-GGUF)
-- Agentic 9B: [DJLougen/Harmonic-Hermes-9B](https://huggingface.co/DJLougen/Harmonic-Hermes-9B)

 ---
+base_model: unsloth/Qwen3.5-27B
 tags:
+- text-generation-inference
+- transformers
 - unsloth
+- qwen3_5
+license: apache-2.0
+language:
+- en
 ---
+# Uploaded finetuned  model
+- **Developed by:** DJLougen
+- **License:** apache-2.0
+- **Finetuned from model :** unsloth/Qwen3.5-27B
+This qwen3_5 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
+[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)