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
File size: 2,891 Bytes
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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
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