Image-Text-to-Text
Transformers
PyTorch
English
molmo
text-generation
multimodal
Mixture of Experts
olmo
olmoe
molmoe
custom_code
Instructions to use philipp-zettl/MolmoE-1B-0924 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use philipp-zettl/MolmoE-1B-0924 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="philipp-zettl/MolmoE-1B-0924", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("philipp-zettl/MolmoE-1B-0924", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use philipp-zettl/MolmoE-1B-0924 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "philipp-zettl/MolmoE-1B-0924" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "philipp-zettl/MolmoE-1B-0924", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/philipp-zettl/MolmoE-1B-0924
- SGLang
How to use philipp-zettl/MolmoE-1B-0924 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 "philipp-zettl/MolmoE-1B-0924" \ --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": "philipp-zettl/MolmoE-1B-0924", "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 "philipp-zettl/MolmoE-1B-0924" \ --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": "philipp-zettl/MolmoE-1B-0924", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use philipp-zettl/MolmoE-1B-0924 with Docker Model Runner:
docker model run hf.co/philipp-zettl/MolmoE-1B-0924
| { | |
| "auto_map": { | |
| "AutoImageProcessor": "image_preprocessing_molmo.MolmoImageProcessor", | |
| "AutoProcessor": "preprocessing_molmo.MolmoProcessor" | |
| }, | |
| "base_image_input_size": [ | |
| 336, | |
| 336 | |
| ], | |
| "do_normalize": true, | |
| "image_mean": [ | |
| 0.48145466, | |
| 0.4578275, | |
| 0.40821073 | |
| ], | |
| "image_padding_mask": true, | |
| "image_patch_size": 14, | |
| "image_processor_type": "MolmoImageProcessor", | |
| "image_std": [ | |
| 0.26862954, | |
| 0.26130258, | |
| 0.27577711 | |
| ], | |
| "image_token_length_h": 12, | |
| "image_token_length_w": 12, | |
| "max_crops": 12, | |
| "overlap_margins": [ | |
| 4, | |
| 4 | |
| ], | |
| "processor_class": "MolmoProcessor" | |
| } | |