| | ---
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| | license: apache-2.0
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| | pipeline_tag: image-text-to-text
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| | ---
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| |
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| | Moondream is a small vision language model designed to run efficiently everywhere.
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| |
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| | [Website](https://moondream.ai/) / [Demo](https://moondream.ai/playground) / [GitHub](https://github.com/vikhyat/moondream)
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| |
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| | This repository contains the latest (**2025-06-21**) release of Moondream, 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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| |
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| |
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| | ### Usage
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| |
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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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| |
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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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| | # Uncomment to run on GPU.
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| | # device_map={"": "cuda"}
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| | )
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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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| |
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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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| |
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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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| |
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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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| |
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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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| |
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| | ### Changelog
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| |
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| | **2025-06-21**
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| |
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| | (release notes coming soon)
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| |
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| | **2025-04-15** ([full release notes](https://moondream.ai/blog/moondream-2025-04-14-release))
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| |
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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
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| |
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