Instructions to use Mharbulous/moondream2-syncopaid with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Mharbulous/moondream2-syncopaid with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf Mharbulous/moondream2-syncopaid:F16 # Run inference directly in the terminal: llama cli -hf Mharbulous/moondream2-syncopaid:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Mharbulous/moondream2-syncopaid:F16 # Run inference directly in the terminal: llama cli -hf Mharbulous/moondream2-syncopaid:F16
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf Mharbulous/moondream2-syncopaid:F16 # Run inference directly in the terminal: ./llama-cli -hf Mharbulous/moondream2-syncopaid:F16
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf Mharbulous/moondream2-syncopaid:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf Mharbulous/moondream2-syncopaid:F16
Use Docker
docker model run hf.co/Mharbulous/moondream2-syncopaid:F16
- LM Studio
- Jan
- vLLM
How to use Mharbulous/moondream2-syncopaid with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Mharbulous/moondream2-syncopaid" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Mharbulous/moondream2-syncopaid", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Mharbulous/moondream2-syncopaid:F16
- Ollama
How to use Mharbulous/moondream2-syncopaid with Ollama:
ollama run hf.co/Mharbulous/moondream2-syncopaid:F16
- Unsloth Studio
How to use Mharbulous/moondream2-syncopaid with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for Mharbulous/moondream2-syncopaid to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for Mharbulous/moondream2-syncopaid to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Mharbulous/moondream2-syncopaid to start chatting
- Atomic Chat new
- Docker Model Runner
How to use Mharbulous/moondream2-syncopaid with Docker Model Runner:
docker model run hf.co/Mharbulous/moondream2-syncopaid:F16
- Lemonade
How to use Mharbulous/moondream2-syncopaid with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Mharbulous/moondream2-syncopaid:F16
Run and chat with the model
lemonade run user.moondream2-syncopaid-F16
List all available models
lemonade list
| license: apache-2.0 | |
| pipeline_tag: image-text-to-text | |
| Moondream is a small vision language model designed to run efficiently everywhere. | |
| [Website](https://moondream.ai/) / [Demo](https://moondream.ai/playground) / [GitHub](https://github.com/vikhyat/moondream) | |
| 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. | |
| ### Usage | |
| ```python | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| from PIL import Image | |
| model = AutoModelForCausalLM.from_pretrained( | |
| "vikhyatk/moondream2", | |
| revision="2025-06-21", | |
| trust_remote_code=True, | |
| # Uncomment to run on GPU. | |
| # device_map={"": "cuda"} | |
| ) | |
| # Captioning | |
| print("Short caption:") | |
| print(model.caption(image, length="short")["caption"]) | |
| print("\nNormal caption:") | |
| for t in model.caption(image, length="normal", stream=True)["caption"]: | |
| # Streaming generation example, supported for caption() and detect() | |
| print(t, end="", flush=True) | |
| print(model.caption(image, length="normal")) | |
| # Visual Querying | |
| print("\nVisual query: 'How many people are in the image?'") | |
| print(model.query(image, "How many people are in the image?")["answer"]) | |
| # Object Detection | |
| print("\nObject detection: 'face'") | |
| objects = model.detect(image, "face")["objects"] | |
| print(f"Found {len(objects)} face(s)") | |
| # Pointing | |
| print("\nPointing: 'person'") | |
| points = model.point(image, "person")["points"] | |
| print(f"Found {len(points)} person(s)") | |
| ``` | |
| ### Changelog | |
| **2025-06-21** | |
| (release notes coming soon) | |
| **2025-04-15** ([full release notes](https://moondream.ai/blog/moondream-2025-04-14-release)) | |
| 1. Improved chart understanding (ChartQA up from 74.8 to 77.5, 82.2 with PoT) | |
| 2. Added temperature and nucleus sampling to reduce repetitive outputs | |
| 3. Better OCR for documents and tables (prompt with “Transcribe the text” or “Transcribe the text in natural reading order”) | |
| 4. Object detection supports document layout detection (figure, formula, text, etc) | |
| 5. UI understanding (ScreenSpot F1\@0.5 up from 53.3 to 60.3) | |
| 6. Improved text understanding (DocVQA up from 76.5 to 79.3, TextVQA up from 74.6 to 76.3) | |
| **2025-03-27** ([full release notes](https://moondream.ai/blog/moondream-2025-03-27-release)) | |
| 1. Added support for long-form captioning | |
| 2. Open vocabulary image tagging | |
| 3. Improved counting accuracy (e.g. CountBenchQA increased from 80 to 86.4) | |
| 4. Improved text understanding (e.g. OCRBench increased from 58.3 to 61.2) | |
| 5. Improved object detection, especially for small objects (e.g. COCO up from 30.5 to 51.2) | |
| 6. Fixed token streaming bug affecting multi-byte unicode characters | |
| 7. gpt-fast style `compile()` now supported in HF Transformers implementation | |