Instructions to use mrhacker7599/moondream1.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mrhacker7599/moondream1.0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="mrhacker7599/moondream1.0", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("mrhacker7599/moondream1.0", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use mrhacker7599/moondream1.0 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "mrhacker7599/moondream1.0" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "mrhacker7599/moondream1.0", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/mrhacker7599/moondream1.0
- SGLang
How to use mrhacker7599/moondream1.0 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 "mrhacker7599/moondream1.0" \ --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": "mrhacker7599/moondream1.0", "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 "mrhacker7599/moondream1.0" \ --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": "mrhacker7599/moondream1.0", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use mrhacker7599/moondream1.0 with Docker Model Runner:
docker model run hf.co/mrhacker7599/moondream1.0
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("mrhacker7599/moondream1.0", trust_remote_code=True, dtype="auto")Quick Links
π moondream1
1.6B parameter model built by @vikhyatk using SigLIP, Phi-1.5 and the LLaVa training dataset. The model is release for research purposes only, commercial use is not allowed.
Try it out on Huggingface Spaces!
Usage
pip install transformers timm einops
from transformers import AutoModelForCausalLM, CodeGenTokenizerFast as Tokenizer
from PIL import Image
model_id = "mrhacker7599/moondream1.0"
model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True)
tokenizer = Tokenizer.from_pretrained(model_id)
image = Image.open('<IMAGE_PATH>')
enc_image = model.encode_image(image)
print(model.answer_question(enc_image, "<QUESTION>", tokenizer))
Benchmarks
| Model | Parameters | VQAv2 | GQA | TextVQA |
|---|---|---|---|---|
| LLaVA-1.5 | 13.3B | 80.0 | 63.3 | 61.3 |
| LLaVA-1.5 | 7.3B | 78.5 | 62.0 | 58.2 |
| moondream1 | 1.6B | 74.7 | 57.9 | 35.6 |
Examples
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="mrhacker7599/moondream1.0", trust_remote_code=True)