Instructions to use vectionlabs/Maestro-2-9B-Preview with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use vectionlabs/Maestro-2-9B-Preview with Transformers:

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

pipe = pipeline("image-text-to-text", model="vectionlabs/Maestro-2-9B-Preview")
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/Maestro-2-9B-Preview")
model = AutoModelForMultimodalLM.from_pretrained("vectionlabs/Maestro-2-9B-Preview", device_map="auto")
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/Maestro-2-9B-Preview with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "vectionlabs/Maestro-2-9B-Preview"
# 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/Maestro-2-9B-Preview",
		"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/Maestro-2-9B-Preview

SGLang

How to use vectionlabs/Maestro-2-9B-Preview 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/Maestro-2-9B-Preview" \
    --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/Maestro-2-9B-Preview",
		"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/Maestro-2-9B-Preview" \
        --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/Maestro-2-9B-Preview",
		"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/Maestro-2-9B-Preview with Docker Model Runner:
```
docker model run hf.co/vectionlabs/Maestro-2-9B-Preview
```

Maestro 2 — 9B (Preview)

Math, code, and deep thinking that can see. Now small enough to actually run.

Vection Labs

Weights · Quickstart · Limitations

Preview release. An early build, published for the people who follow us. This has been trained in parallel with Salience-1.5-Pro, with the same data, but in a smaller model! Bugs you report in the Community tab get fixed in Maestro 2.1.

Abstract

Maestro 2 is a 9B dense multimodal model built for engineering work you can run on one GPU. It thinks natively inside <think> blocks, writes and debugs real code, designs frontends that do not look machine-generated, and reads images, screenshots, diagrams and video as first-class input. Context window: 262,144 tokens.

It is the successor to Maestro 1 — the most-liked model we have shipped — and it keeps that model idea intact: one small brain that reasons, codes, and sees, rather than three specialists you have to switch between.

Highlights

Native thinking. Real <think> reasoning on by default; switch it off with enable_thinking=False when you want an instant answer.
Agentic software engineering. Repo-scale edits, methodical debugging, minimal precise patches, well-formed tool calls.
Frontend and SVG. Modern stacks (React/Next, TypeScript, Tailwind, shadcn/ui), real design tokens and spacing scales, WCAG-accessible semantics, and disciplined SVG geometry.
Genuinely multimodal. Images and video are inputs, not an afterthought.
Long context. 262K tokens native.
Small. 9B dense. One consumer GPU. Apache-2.0.

Model overview


Parameters	9.65B dense
Modalities	text, image, video -> text
Context window	262,144 tokens native(In version 2.1, the context will be updated to 1M.)
Precision	bfloat16
Architecture	Qwen3.5 (dense) + native vision encoder
License	Apache-2.0
Library	transformers (`AutoModelForImageTextToText`)

Larger MoE siblings: Maestro-2-9B-Preview (35B-A3B) and Salience-1.5-Flash (30B-A3B).

Thinking

Thinking is on by default — the model reasons inside <think>...</think> before answering, and serving stacks surface it as reasoning_content. Pass enable_thinking=False to apply_chat_template for direct answers.

Tool calling

Maestro 2 emits XML-style tool calls (<tool_call><function=...><parameter=...>), parsed natively by the vLLM and SGLang tool parsers for this model family. Provide schemas through the chat template tools argument.

Quickstart

from transformers import AutoModelForImageTextToText, AutoProcessor

repo = "vectionlabs/Maestro-2-9B-Preview"
proc = AutoProcessor.from_pretrained(repo)
model = AutoModelForImageTextToText.from_pretrained(repo, dtype="auto", device_map="auto")

messages = [{
    "role": "user",
    "content": [{"type": "text", "text": "Build a responsive pricing card in React + Tailwind."}],
}]
text = proc.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = proc(text=[text], return_tensors="pt").to(model.device)
out = model.generate(**inputs, max_new_tokens=2048)
print(proc.batch_decode(out[:, inputs.input_ids.shape[1]:], skip_special_tokens=True)[0])

Requires a recent transformers (>= 5.8). Images work the same way with {"type": "image", "image": ...} content items.

Running locally

Dense 9B quantizes gracefully, unlike the sparse-MoE tiers of this family:

quant	size	fits
Q8_0	~10 GB	12 GB card, best quality
Q5_K_M	~6.5 GB	8 GB card [recommended]
Q4_K_M	~5.6 GB	6 GB card, still coherent

Prompting tips

Code: state language, constraints, and the exact I/O contract.
Frontend: name the stack and the look you want; it returns tokens, semantics and structure.
SVG: ask for an explicit viewBox and a stated grid; it plans geometry before drawing.
Vision: put the image before the question in the message content.
Sampling: temperature=0.7, top_p=0.95, top_k=20, presence_penalty=1.1. Lower the temperature to ~0.3 for precise patches.

Limitations & responsible use

Maestro 2 can be confidently wrong. Verify factual and mathematical claims.
Generated code may be insecure or incorrect - review before running, never execute untrusted output.
9B is a small model: it will lose to frontier-scale models on the hardest reasoning.
Preview: known issues are being collected for Maestro 2.1. Pin a revision if you need reproducibility.
Long-context and video inputs increase latency and memory substantially.
Do not use it for surveillance, manipulation, or any use violating applicable law or Apache-2.0.
No audio modality.

Citation

@misc{vectionlabs2026maestro2,
  title  = {Maestro 2 Preview: A 9B Multimodal Engineering Model},
  author = {Vection Labs},
  year   = {2026},
  url    = {https://huggingface.co/vectionlabs/Maestro-2-9B-Preview}
}

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Safetensors

Model size

10B params

Tensor type

BF16

F32

Collection including vectionlabs/Maestro-2-9B-Preview

Maestro

Collection

Our most powerful mathematical model to date; we will also include GGUFs and Heretic variants in the collection. • 5 items • Updated about 4 hours ago • 4