Image-Text-to-Text
Transformers
Safetensors
English
multimodal
vision-language-model
embodied-reasoning
spatial-reasoning
molmo
molmo2
Instructions to use allenai/Molmo2-ER with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use allenai/Molmo2-ER with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="allenai/Molmo2-ER")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("allenai/Molmo2-ER", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use allenai/Molmo2-ER with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "allenai/Molmo2-ER" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "allenai/Molmo2-ER", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/allenai/Molmo2-ER
- SGLang
How to use allenai/Molmo2-ER 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 "allenai/Molmo2-ER" \ --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": "allenai/Molmo2-ER", "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 "allenai/Molmo2-ER" \ --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": "allenai/Molmo2-ER", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use allenai/Molmo2-ER with Docker Model Runner:
docker model run hf.co/allenai/Molmo2-ER
| { | |
| "auto_map": { | |
| "AutoProcessor": "processing_molmo2.Molmo2Processor", | |
| "AutoVideoProcessor": "video_processing_molmo2.Molmo2VideoProcessor" | |
| }, | |
| "crop_size": null, | |
| "data_format": "channels_first", | |
| "default_to_square": true, | |
| "device": null, | |
| "do_center_crop": null, | |
| "do_convert_rgb": true, | |
| "do_normalize": true, | |
| "do_pad": null, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "do_sample_frames": true, | |
| "fps": null, | |
| "frame_sample_mode": "uniform_last_frame", | |
| "image_mean": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "image_std": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "input_data_format": null, | |
| "max_fps": 2.0, | |
| "num_frames": 128, | |
| "patch_size": 14, | |
| "pooling_size": [ | |
| 3, | |
| 3 | |
| ], | |
| "processor_class": "Molmo2Processor", | |
| "resample": 2, | |
| "rescale_factor": 0.00392156862745098, | |
| "return_metadata": false, | |
| "sampling_fps": 2, | |
| "size": { | |
| "height": 378, | |
| "width": 378 | |
| }, | |
| "size_divisor": null, | |
| "video_metadata": null, | |
| "video_processor_type": "Molmo2VideoProcessor" | |
| } | |