---
base_model: Qwen/Qwen3.5-9B-Base
base_model_relation: finetune
language:
- en
license: apache-2.0
library_name: transformers
tags:
- transformers
- safetensors
- qwen3_5
- text-generation
- reasoning
- ai-research
- qwen3.5
- conversational
pipeline_tag: text-generation
---

![Ornstein 3.5 9B — V1.5](ornstein3.5-9bv1.5.png)

# Ornstein 3.5 9B — V1.5

A reasoning-focused fine-tune of [Qwen 3.5 9B](https://huggingface.co/Qwen/Qwen3.5-9B-Base), tuned for AI-research and technical problem-solving. Part of the **Ornstein** series — reasoning- and agent-oriented fine-tunes built on a custom, fully-automated data curation pipeline where every training row passes structural quality gates before it is used.

**V1.5 is an intermediate release.** It sits between the initial V1 reasoning fine-tune and the upcoming V2 — a much more rigorous post-training run involving reinforcement-learning methods. V1.5 is a refined supervised fine-tune: stronger reasoning than V1, fully usable today, and a clean foundation for the V2 RL stage.

The model is taught a disciplined reasoning behavior — work the evidence, weigh alternatives, verify, then commit — rather than surface-level chain-of-thought. The base model already holds the knowledge from pretraining; the fine-tune shapes how it thinks.

## Release line

- **V1** — initial reasoning fine-tune.
- **V1.5 — this release** — refined supervised fine-tune on quality-gated reasoning data.
- **V2 (in progress)** — a much more rigorous post-training run involving reinforcement-learning (verifiable-reward) methods, targeting further gains in hard reasoning.

## Benchmarks

Evaluated on the Gestalt Benchmark Suite (GBS, STANDARD-200) — a held-out, contamination-controlled reasoning + coding suite — paired against the base model on identical items with greedy decoding.

| | Qwen3.5-9B-Base | **Ornstein V1.5** |
|---|---|---|
| Overall | 0.725 | **0.850** |
| Reasoning | 0.68 | **0.90** |
| GPQA (graduate-level science) | 0.36 | **0.80** |
| Coding | 0.77 | 0.80 |

V1.5 lifts overall accuracy by **+12.5 points**, driven by large gains in multi-step and graduate-level scientific reasoning, while preserving coding ability.

## 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.

**[Support on Ko-fi](https://ko-fi.com/djlougen)**

---

## Details

- **Developed by:** DJLougen / GestaltLabs
- **Base model:** [Qwen/Qwen3.5-9B-Base](https://huggingface.co/Qwen/Qwen3.5-9B-Base)
- **Parameters:** ~9.65B
- **Precision:** BF16
- **Format:** ChatML (conversational)
- **License:** Apache 2.0

## Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "GestaltLabs/Ornstein-3.5-9B-V1.5"
tok = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, dtype=torch.bfloat16, device_map="auto")

messages = [{"role": "user", "content": "Derive the variance of a sum of two correlated random variables."}]
inputs = tok.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
out = model.generate(inputs, max_new_tokens=1024)
print(tok.decode(out[0][inputs.shape[-1]:], skip_special_tokens=True))
```

## Intended Use

Reasoning-heavy tasks, AI-research assistance, technical and scientific problem-solving, and general conversation.

## License

Apache 2.0 — inherited from the Qwen 3.5 9B base release.