Ornstein-27B-GGUF

Ornstein-27B-GGUF

GGUF quantizations of DJLougen/Ornstein-27B — a reasoning-focused fine-tune of Qwen 3.5 27B trained on 1,229 high-quality reasoning traces curated through a custom Drift Diffusion Modeling (DDM) pipeline.

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. All training compute is self-funded — balancing GPU costs against a student budget. 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.

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What Makes Ornstein Different

Unlike typical reasoning fine-tunes that use large volumes of synthetic data, Ornstein implements quality-over-quantity:

• Detects degenerate reasoning: Identifies "fake" reasoning that mimics thought without substance (hedging, restating, circling)
• Premium vs. Degenerate split: 799 premium traces + 430 selected degenerate traces = 1,229 total
• DDM AUC of 0.9705 separating premium from degenerate reasoning with 99.49% sensitivity

The model uses <think>...</think> blocks for extended multi-phase reasoning with self-correction and verification before providing final answers.

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Available Quantizations

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Quick Start

llama.cpp

# Download a quantization (example: Q4_K_M)
huggingface-cli download DJLougen/Ornstein-27B-GGUF ornstein-27b-q4_k_m.gguf --local-dir .

# Run with llama.cpp
./llama-cli -m ornstein-27b-q4_k_m.gguf \
  -p "You are a helpful reasoning assistant." \
  --temp 0.6 -n 8192

Ollama

# Create a Modelfile
cat <<EOF > Modelfile
FROM ./ornstein-27b-q4_k_m.gguf
PARAMETER temperature 0.6
PARAMETER num_predict 8192
SYSTEM "You are a helpful reasoning assistant."
EOF

ollama create ornstein -f Modelfile
ollama run ornstein

LM Studio

1. Download the desired quantization from the Files tab
2. Load it in LM Studio
3. Set context length to 8192 for full reasoning depth

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Recommended Settings

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Training Details

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Data Quality Metrics

Training Data Profile

• Category Mix: Math (1,016), Code (124), Science (45), Logic (44)
• Reasoning Depth: Premium traces average ~1,263 words of thinking vs ~281 for degenerate traces
• Drift Score Threshold: 1.463 cleanly separates premium from degenerate traces
• DDM AUC: 0.9705 | Sensitivity: 99.49% | False Positive Rate: ~5%

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Intended Use

Designed for tasks requiring structured, multi-step reasoning:

• Mathematics
• Logic problems
• Code analysis
• Scientific problems
• Complex question answering

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Limitations

• Single epoch training on 1,229 examples means the model retains most base Qwen 3.5 27B behavior; the fine-tune primarily shapes reasoning style rather than injecting new knowledge
• Language scope: DDM pipeline optimized for English; other languages reflect base model performance
• Edge cases: Extended thinking can occasionally loop on adversarial or highly ambiguous prompts

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Citation

@misc{ornstein27b,
  author = {DJLougen},
  title = {Ornstein-27B: DDM-Curated Reasoning Fine-Tune of Qwen 3.5 27B},
  year = {2026},
  publisher = {Hugging Face},
  url = {https://huggingface.co/DJLougen/Ornstein-27B}
}

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Links

• Full-precision model: DJLougen/Ornstein-27B
• Base model: Qwen/Qwen3.5-27B | unsloth/Qwen3.5-27B