Ornstein-27B-v2

A reasoning-focused fine-tune of Qwen 3.5 27B, trained on a small, high-quality dataset curated through a custom Drift Diffusion Modeling (DDM) pipeline. Second iteration of the Ornstein series with improved segment-level reasoning modeling.

GGUF quantizations available at DJLougen/Ornstein-27B-v2-GGUF

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What Makes Ornstein Different

Most reasoning fine-tunes throw large volumes of synthetic data at a base model and hope for the best. Ornstein takes the opposite approach: every single training example passed through a multi-stage quality pipeline that measures whether a reasoning trace is actually reasoning or just generating tokens that look like reasoning.

The core insight is that language models frequently produce degenerate reasoning — long chains of text that superficially resemble deep thought (hedging, restating the problem, circling without progress) but carry little actual signal. The DDM pipeline detects and separates these from genuine premium reasoning traces, producing a training mix that teaches the model what good thinking actually looks like.

What's New in v2

Ornstein v2 builds on the original with improved segment-level reasoning modeling — the DDM pipeline now does more to model individual segments of the reasoning process, leading to finer-grained quality separation and better training signal.

Training Data at a Glance

• Drift Score Distribution: The DDM pipeline assigns each reasoning trace a drift score. Premium traces cluster low, degenerate traces cluster high, with a fitted threshold cleanly separating the two pools.
• Category Mix: Math-heavy dataset with code, science, and logic rounding it out.
• Reasoning Depth: Premium traces average substantially deeper thinking than degenerate traces, which tend to be shallow repetition that inflates token count without substance.
• Difficulty x Pool: The degenerate pool skews toward hard problems where models are most likely to loop or stall.

DDM Curation Pipeline

Drift Diffusion Modeling works by decomposing each reasoning trace into uniform segments and tracking how "reasoning quality" evolves across the trace. Each segment is scored on multiple dimensions that capture whether the model is mimicking cognitive progress — things like introducing new ideas, self-correcting, verifying intermediate results, and exploring alternative approaches.

These per-segment scores are accumulated into a drift trajectory. Premium traces maintain healthy trajectories throughout. Degenerate traces accumulate deficit as the model loops, repeats itself, or pads without substance — and the drift score crosses a threshold fitted via ROC analysis.

Training Details

Parameter	Value
Base model	unsloth/Qwen3.5-27B
Method	LoRA (rank 32, alpha 32)
Dropout	0.05
Epochs	1
Learning rate	1e-4 (cosine schedule, 10% warmup)
Max sequence length	8192
Micro batch size	1
Gradient accumulation	4 steps
Weight decay	0.01
LoRA targets	q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
Packing	Off
Training framework	Unsloth

Usage

With Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "DJLougen/Ornstein-27B-v2"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")

messages = [{"role": "user", "content": "Your question here"}]
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(inputs, max_new_tokens=8192)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

With Unsloth (Recommended for Inference)

from unsloth import FastLanguageModel

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name="DJLougen/Ornstein-27B-v2",
    max_seq_length=8192,
    load_in_4bit=True,
)
FastLanguageModel.for_inference(model)

Reasoning Format

Ornstein uses <think>...</think> blocks for extended reasoning:

<think>
Let me work through this step by step...
[multi-phase reasoning with self-correction and verification]
</think>

[Final answer]

Intended Use

Ornstein-27B-v2 is designed for tasks that benefit from structured, multi-step reasoning — math, logic, code analysis, scientific problems, and complex question answering. The DDM curation specifically optimizes for traces that exhibit genuine cognitive progress rather than verbose restating.

Limitations

• Single epoch training means the model retains most of the base Qwen 3.5 27B behavior; the fine-tune primarily shapes reasoning style rather than injecting new knowledge
• The DDM pipeline optimizes for English reasoning traces; performance on other languages reflects the base model
• Extended thinking can still occasionally loop on adversarial or highly ambiguous prompts

License

Apache 2.0

Citation

If you use Ornstein-27B-v2 or the DDM curation methodology in your work:

@misc{ornstein27bv2,
  author = {DJLougen},
  title = {Ornstein-27B-v2: DDM-Curated Reasoning Fine-Tune of Qwen 3.5 27B},
  year = {2026},
  publisher = {Hugging Face},
  url = {https://huggingface.co/DJLougen/Ornstein-27B-v2}
}