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

Libraries

How to use RahulPx/moondream2-inferencefix with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="RahulPx/moondream2-inferencefix",
	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 RahulPx/moondream2-inferencefix with llama.cpp:

Install from brew

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

Install from WinGet (Windows)

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

Use Docker

docker model run hf.co/RahulPx/moondream2-inferencefix:F16

LM Studio
Jan

vLLM

How to use RahulPx/moondream2-inferencefix with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/RahulPx/moondream2-inferencefix:F16

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

Unsloth Studio new

How to use RahulPx/moondream2-inferencefix 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 RahulPx/moondream2-inferencefix 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 RahulPx/moondream2-inferencefix to start chatting

Using HuggingFace Spaces for Unsloth

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

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

Lemonade

How to use RahulPx/moondream2-inferencefix with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull RahulPx/moondream2-inferencefix:F16

Run and chat with the model

lemonade run user.moondream2-inferencefix-F16

List all available models

lemonade list

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This repository is publicly accessible, but you have to accept the conditions to access its files and content.

Fork of the original project to deploy on HF Inference Endpoint without any issues in one click. 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	DocVQA	TallyQA (simple/full)	POPE (rand/pop/adv)
2024-07-23 (latest)	79.4	64.9	60.2	61.9	82.0 / 76.8	91.3 / 89.7 / 86.9
2024-05-20	79.4	63.1	57.2	30.5	82.1 / 76.6	91.5 / 89.6 / 86.2
2024-05-08	79.0	62.7	53.1	30.5	81.6 / 76.1	90.6 / 88.3 / 85.0
2024-04-02	77.7	61.7	49.7	24.3	80.1 / 74.2	-
2024-03-13	76.8	60.6	46.4	22.2	79.6 / 73.3	-
2024-03-06	75.4	59.8	43.1	20.9	79.5 / 73.2	-
2024-03-04	74.2	58.5	36.4	-	-	-

Usage

pip install transformers einops

from transformers import AutoModelForCausalLM, AutoTokenizer
from PIL import Image

model_id = "vikhyatk/moondream2"
revision = "2024-07-23"
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: -

Safetensors

Model size

2B params

Tensor type

F16

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