Instructions to use pranay-ar/moondream2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use pranay-ar/moondream2 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="pranay-ar/moondream2", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("pranay-ar/moondream2", trust_remote_code=True, dtype="auto")

llama-cpp-python

How to use pranay-ar/moondream2 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="pranay-ar/moondream2",
	filename="moondream2-mmproj-f16.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use pranay-ar/moondream2 with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf pranay-ar/moondream2:F16
# Run inference directly in the terminal:
llama-cli -hf pranay-ar/moondream2:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf pranay-ar/moondream2:F16
# Run inference directly in the terminal:
llama-cli -hf pranay-ar/moondream2: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 pranay-ar/moondream2:F16
# Run inference directly in the terminal:
./llama-cli -hf pranay-ar/moondream2: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 pranay-ar/moondream2:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf pranay-ar/moondream2:F16

Use Docker

docker model run hf.co/pranay-ar/moondream2:F16

LM Studio
Jan

vLLM

How to use pranay-ar/moondream2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "pranay-ar/moondream2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "pranay-ar/moondream2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/pranay-ar/moondream2:F16

SGLang

How to use pranay-ar/moondream2 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "pranay-ar/moondream2" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "pranay-ar/moondream2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "pranay-ar/moondream2" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "pranay-ar/moondream2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use pranay-ar/moondream2 with Ollama:
```
ollama run hf.co/pranay-ar/moondream2:F16
```

Unsloth Studio new

How to use pranay-ar/moondream2 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 pranay-ar/moondream2 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 pranay-ar/moondream2 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for pranay-ar/moondream2 to start chatting

Docker Model Runner
How to use pranay-ar/moondream2 with Docker Model Runner:
```
docker model run hf.co/pranay-ar/moondream2:F16
```

Lemonade

How to use pranay-ar/moondream2 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull pranay-ar/moondream2:F16

Run and chat with the model

lemonade run user.moondream2-F16

List all available models

lemonade list

moondream2 is a small vision language model designed to run efficiently on edge devices. Check out the GitHub repository for details, or try it out on the Hugging Face Space!

Benchmarks

Release	VQAv2	GQA	TextVQA	TallyQA (simple)	TallyQA (full)
2024-03-04	74.2	58.5	36.4	-	-
2024-03-06	75.4	59.8	43.1	79.5	73.2
2024-03-13	76.8	60.6	46.4	79.6	73.3
2024-04-02	77.7	61.7	49.7	80.1	74.2
2024-05-08	79.0	62.7	53.1	81.6	76.1
2024-05-20 (latest)	79.4	63.1	57.2	82.1	76.6

Usage

pip install transformers einops

from transformers import AutoModelForCausalLM, AutoTokenizer
from PIL import Image

model_id = "vikhyatk/moondream2"
revision = "2024-05-20"
model = AutoModelForCausalLM.from_pretrained(
    model_id, trust_remote_code=True, revision=revision
)
tokenizer = AutoTokenizer.from_pretrained(model_id, revision=revision)

image = Image.open('<IMAGE_PATH>')
enc_image = model.encode_image(image)
print(model.answer_question(enc_image, "Describe this image.", tokenizer))

The model is updated regularly, so we recommend pinning the model version to a specific release as shown above.

Downloads last month: 580

Safetensors

Model size

2B params

Tensor type

F16

Inference Providers NEW

Image-Text-to-Text

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