Instructions to use vectionlabs/Maestro1-9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use vectionlabs/Maestro1-9B with Transformers:

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

pipe = pipeline("image-text-to-text", model="vectionlabs/Maestro1-9B")
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("vectionlabs/Maestro1-9B")
model = AutoModelForMultimodalLM.from_pretrained("vectionlabs/Maestro1-9B")
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 vectionlabs/Maestro1-9B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "vectionlabs/Maestro1-9B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "vectionlabs/Maestro1-9B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/vectionlabs/Maestro1-9B

SGLang

How to use vectionlabs/Maestro1-9B 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 "vectionlabs/Maestro1-9B" \
    --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": "vectionlabs/Maestro1-9B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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 "vectionlabs/Maestro1-9B" \
        --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": "vectionlabs/Maestro1-9B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use vectionlabs/Maestro1-9B with Docker Model Runner:
```
docker model run hf.co/vectionlabs/Maestro1-9B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Maestro1-9B

VectionLabs Maestro 1 Banner

A 9B multimodal reasoning model — math, code, and deep thinking that can see.

Vection Labs

Weights · Benchmarks · Quickstart · Limitations

Abstract

Maestro1-9B is a dense, 9-billion-parameter vision-language model built for hard problems: multi-step mathematical proof, competitive-programming-grade code synthesis, and visual reasoning over images and video — within a single model and a single context window of up to 1M tokens.

It is designed for users who care less about chat pleasantries and more about whether the model can actually solve the thing: derive the bound, find the bug, read the diagram, finish the proof. Maestro1-9B pairs an explicit step-by-step reasoning mode with native multimodal perception, so the same chain of thought that solves a math olympiad problem can also reason about a chart, a UI screenshot, or a short clip.

Highlights

Reasoning-first. Produces structured, inspectable chains of thought for math, logic, and code.
Genuinely multimodal. Images and video are first-class inputs, not bolted-on captioning.
Long context. Up to 1M tokens via interleaved multimodal RoPE — whole codebases, long papers, or long videos in a single prompt.
Open weights. Apache-2.0, transformers-native, single-file deployment.
9B dense. Runs on a single modern accelerator; no mixture-of-experts routing to manage.

Model overview


Parameters	9B (dense)
Modalities	text, image, video → text
Context window	up to 1,000,000 tokens (interleaved multimodal RoPE)
Precision	bfloat16
Architecture	decoder-only transformer LM + native vision encoder
License	Apache-2.0
Library	🤗 `transformers` (`AutoModelForImageTextToText`)

Intended use

Maestro1-9B targets technical assistance and research:

Step-by-step math and quantitative reasoning.
Code generation, explanation, debugging, and review.
Visual question answering and document/diagram/chart understanding.
Video understanding over short clips.
Long-document and long-context analysis.

It is not intended for high-stakes decisions without human review, nor as a source of truth for medical, legal, or financial advice.

Benchmarks

Reasoning, math & code

Benchmark	Setting	Maestro1-9B
GSM8K	0-shot CoT, exact match	—
MATH-500	0-shot CoT, exact match	—
AIME 2024	0-shot, pass@1	—
HumanEval	0-shot, pass@1	—
MBPP	3-shot, pass@1	—
MMLU	0-shot	—

Multimodal

Benchmark	Setting	Maestro1-9B
MMMU (val)	0-shot	—
MathVista (testmini)	0-shot	—
DocVQA (val)	0-shot, ANLS	—

Quickstart

from transformers import AutoModelForImageTextToText, AutoProcessor
import torch

model_id = "vectionlabs/Maestro1-9B"
proc = AutoProcessor.from_pretrained(model_id)
model = AutoModelForImageTextToText.from_pretrained(
    model_id, dtype=torch.bfloat16, device_map="auto"
)

messages = [{
    "role": "user",
    "content": [
        {"type": "image", "image": "https://example.com/diagram.png"},
        {"type": "text", "text": "Explain what this diagram proves, step by step."},
    ],
}]

text = proc.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
from qwen_vl_utils import process_vision_info
imgs, vids = process_vision_info(messages)
inputs = proc(text=[text], images=imgs, videos=vids, return_tensors="pt").to(model.device)
out = model.generate(**inputs, max_new_tokens=1024)
print(proc.batch_decode(out[:, inputs.input_ids.shape[1]:], skip_special_tokens=True)[0])

Text-only works the same way with a plain {"type": "text", ...} message.

Prompting tips

For math/logic, ask the model to reason step by step; it is tuned to externalize its work.
For code, specify language, constraints ("no external libraries"), and the exact I/O contract.
For vision, put the image/video before the question in the message content.
Lower temperature (0.2–0.7) for deterministic reasoning; raise it for brainstorming.

Deployment

GPU: a single 24–80 GB GPU in bf16 (device_map="auto").
Serving: compatible with standard transformers generation; for high throughput use a vision-capable serving stack.

Limitations & responsible use

Maestro1-9B can be confidently wrong. Verify mathematical and factual claims.
Generated code may be insecure or incorrect — review before running, never execute untrusted output.
Long-context and long-video inputs increase latency and memory substantially.
It inherits the biases and failure modes of large web-trained models. Do not use it for surveillance, manipulation, or any use that violates applicable law or the Apache-2.0 terms.
No audio modality.

Citation

@misc{vectionlabs2026maestro1,
  title  = {Maestro1-9B: A Multimodal Reasoning Model},
  author = {Vection Labs},
  year   = {2026},
  url    = {https://huggingface.co/vectionlabs/Maestro1-9B}
}

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Safetensors

Model size

9B params

Tensor type

BF16

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Quantizations

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