Instructions to use GestaltLabs/Ornstein-3.5-9B-V1.5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use GestaltLabs/Ornstein-3.5-9B-V1.5 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="GestaltLabs/Ornstein-3.5-9B-V1.5")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("GestaltLabs/Ornstein-3.5-9B-V1.5")
model = AutoModelForMultimodalLM.from_pretrained("GestaltLabs/Ornstein-3.5-9B-V1.5")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use GestaltLabs/Ornstein-3.5-9B-V1.5 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "GestaltLabs/Ornstein-3.5-9B-V1.5"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "GestaltLabs/Ornstein-3.5-9B-V1.5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/GestaltLabs/Ornstein-3.5-9B-V1.5

SGLang

How to use GestaltLabs/Ornstein-3.5-9B-V1.5 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "GestaltLabs/Ornstein-3.5-9B-V1.5" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "GestaltLabs/Ornstein-3.5-9B-V1.5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "GestaltLabs/Ornstein-3.5-9B-V1.5" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "GestaltLabs/Ornstein-3.5-9B-V1.5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use GestaltLabs/Ornstein-3.5-9B-V1.5 with Docker Model Runner:
```
docker model run hf.co/GestaltLabs/Ornstein-3.5-9B-V1.5
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Ornstein 3.5 9B — V1.5

A reasoning-focused fine-tune of Qwen 3.5 9B, 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

Details

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

Usage

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))