Instructions to use gokaygokay/moondream-prompt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use gokaygokay/moondream-prompt with Transformers:

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

pipe = pipeline("image-text-to-text", model="gokaygokay/moondream-prompt", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("gokaygokay/moondream-prompt", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use gokaygokay/moondream-prompt with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/gokaygokay/moondream-prompt

SGLang

How to use gokaygokay/moondream-prompt 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 "gokaygokay/moondream-prompt" \
    --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": "gokaygokay/moondream-prompt",
		"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 "gokaygokay/moondream-prompt" \
        --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": "gokaygokay/moondream-prompt",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use gokaygokay/moondream-prompt with Docker Model Runner:
```
docker model run hf.co/gokaygokay/moondream-prompt
```

Fine tuned version of moondream2 for prompt generation from images. Moondream 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!

Usage

pip install transformers timm einops bitsandbytes accelerate

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
from PIL import Image

DEVICE = "cuda"
DTYPE = torch.float32 if DEVICE == "cpu" else torch.float16 # CPU doesn't support float16
revision = "ac6c8fc0ba757c6c4d7d541fdd0e63618457350c"
tokenizer = AutoTokenizer.from_pretrained("gokaygokay/moondream-prompt", revision=revision)
moondream = AutoModelForCausalLM.from_pretrained("gokaygokay/moondream-prompt",trust_remote_code=True,
    torch_dtype=DTYPE, device_map={"": DEVICE}, revision=revision)
moondream.eval()

image_path = "<image_path>"
image = Image.open(image_path).convert("RGB")
md_answer = moondream.answer_question(
        moondream.encode_image(image),
        "Describe this image and its style in a very detailed manner",
        tokenizer=tokenizer,
    )

print(md_answer)

Example

"a very angry old man with white hair and a mustache, in the style of a Pixar movie, hyperrealistic, white background, 8k"

Citation and attribution

This model release is maintained by Gökay Aydoğan. If you reference this repository in academic work, please cite it as follows and also cite the upstream models, datasets, or projects it builds upon.

@software{aydogan2024moondream_prompt,
  author = {Aydoğan, Gökay},
  title = {{moondream-prompt}},
  year = {2024},
  publisher = {Hugging Face},
  url = {https://huggingface.co/gokaygokay/moondream-prompt},
  note = {Model repository; cite the base model and upstream datasets as required.}
}

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Tensor type

F16

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moondream-prompt

Citation and attribution

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