# Maestro1-9B

**Repository Path**: hf-models/Maestro1-9B

## Basic Information

- **Project Name**: Maestro1-9B
- **Description**: Mirror of https://huggingface.co/vectionlabs/Maestro1-9B
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2026-07-03
- **Last Updated**: 2026-07-03

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

---
license: apache-2.0
language:
  - en
pipeline_tag: image-text-to-text
library_name: transformers
tags:
  - multimodal
  - vision-language
  - reasoning
  - thinking
  - math
  - code
  - long-context
model-index:
  - name: Maestro1-9B
    results: []
new_version: vectionlabs/Salience-1-9B
---

<div align="center">

# Maestro1-9B
<p align="center">
  <img src="MaestroBanner.png" alt="VectionLabs Maestro 1 Banner" width="100%">
</p>

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

*Vection Labs*

[Weights](https://huggingface.co/vectionlabs/Maestro1-9B) ·
[Benchmarks](#benchmarks) ·
[Quickstart](#quickstart) ·
[Limitations](#limitations--responsible-use)

</div>

---

## 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

```python
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

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

---

<div align="center"><sub>© 2026 Vection Labs · Apache-2.0</sub></div>