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license: apache-2.0 |
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pipeline_tag: image-text-to-text |
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new_version: moondream/moondream3-preview |
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--- |
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⚠️ This repository contains the latest version of Moondream 2, our previous generation model. The latest version of Moondream is [Moondream 3 (Preview)](https://huggingface.co/moondream/moondream3-preview). |
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--- |
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Moondream is a small vision language model designed to run efficiently everywhere. |
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[Website](https://moondream.ai/) / [Demo](https://moondream.ai/playground) / [GitHub](https://github.com/vikhyat/moondream) |
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This repository contains the latest (**2025-06-21**) release of Moondream 2, as well as [historical releases](https://huggingface.co/vikhyatk/moondream2/blob/main/versions.txt). The model is updated frequently, so we recommend specifying a revision as shown below if you're using it in a production application. |
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### Usage |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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from PIL import Image |
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model = AutoModelForCausalLM.from_pretrained( |
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"vikhyatk/moondream2", |
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revision="2025-06-21", |
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trust_remote_code=True, |
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device_map={"": "cuda"} # ...or 'mps', on Apple Silicon |
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) |
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# Captioning |
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print("Short caption:") |
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print(model.caption(image, length="short")["caption"]) |
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print("\nNormal caption:") |
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for t in model.caption(image, length="normal", stream=True)["caption"]: |
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# Streaming generation example, supported for caption() and detect() |
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print(t, end="", flush=True) |
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print(model.caption(image, length="normal")) |
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# Visual Querying |
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print("\nVisual query: 'How many people are in the image?'") |
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print(model.query(image, "How many people are in the image?")["answer"]) |
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# Object Detection |
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print("\nObject detection: 'face'") |
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objects = model.detect(image, "face")["objects"] |
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print(f"Found {len(objects)} face(s)") |
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# Pointing |
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print("\nPointing: 'person'") |
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points = model.point(image, "person")["points"] |
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print(f"Found {len(points)} person(s)") |
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``` |
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### Changelog |
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**2025-06-21** ([full release notes](https://moondream.ai/blog/moondream-2025-06-21-release)) |
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* **Grounded Reasoning** |
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Introduces a new step-by-step reasoning mode that explicitly grounds reasoning in spatial positions within the image before answering, leading to more precise visual interpretation (e.g., chart median calculations, accurate counting). Enable with `reasoning=True` in the `query` skill to trade off speed vs. accuracy. |
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* **Sharper Object Detection** |
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Uses reinforcement learning on higher-quality bounding-box annotations to reduce object clumping and improve fine-grained detections (e.g., distinguishing “blue bottle” vs. “bottle”). |
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* **Faster Text Generation** |
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Yields 20–40 % faster response generation via a new “superword” tokenizer and lightweight tokenizer transfer hypernetwork, which reduces the number of tokens emitted without loss in accuracy and eases future multilingual extensions. |
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* **Improved UI Understanding** |
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Boosts ScreenSpot (UI element localization) performance from an F1\@0.5 of 60.3 to 80.4, making Moondream more effective for UI-focused applications. |
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* **Reinforcement Learning Enhancements** |
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RL fine-tuning applied across 55 vision-language tasks to reinforce grounded reasoning and detection capabilities, with a roadmap to expand to \~120 tasks in the next update. |
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**2025-04-15** ([full release notes](https://moondream.ai/blog/moondream-2025-04-14-release)) |
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1. Improved chart understanding (ChartQA up from 74.8 to 77.5, 82.2 with PoT) |
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2. Added temperature and nucleus sampling to reduce repetitive outputs |
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3. Better OCR for documents and tables (prompt with “Transcribe the text” or “Transcribe the text in natural reading order”) |
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4. Object detection supports document layout detection (figure, formula, text, etc) |
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5. UI understanding (ScreenSpot F1\@0.5 up from 53.3 to 60.3) |
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6. Improved text understanding (DocVQA up from 76.5 to 79.3, TextVQA up from 74.6 to 76.3) |
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**2025-03-27** ([full release notes](https://moondream.ai/blog/moondream-2025-03-27-release)) |
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1. Added support for long-form captioning |
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2. Open vocabulary image tagging |
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3. Improved counting accuracy (e.g. CountBenchQA increased from 80 to 86.4) |
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4. Improved text understanding (e.g. OCRBench increased from 58.3 to 61.2) |
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5. Improved object detection, especially for small objects (e.g. COCO up from 30.5 to 51.2) |
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6. Fixed token streaming bug affecting multi-byte unicode characters |
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7. gpt-fast style `compile()` now supported in HF Transformers implementation |
