Instructions to use allenai/MolmoE-1B-0924 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use allenai/MolmoE-1B-0924 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="allenai/MolmoE-1B-0924", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("allenai/MolmoE-1B-0924", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use allenai/MolmoE-1B-0924 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "allenai/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": "allenai/MolmoE-1B-0924", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/allenai/MolmoE-1B-0924
- SGLang
How to use allenai/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 "allenai/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": "allenai/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 "allenai/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": "allenai/MolmoE-1B-0924", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use allenai/MolmoE-1B-0924 with Docker Model Runner:
docker model run hf.co/allenai/MolmoE-1B-0924
Is it possible to group coordinates per object type using "Point to <something>"?
Is it possible to group the coordinates per object type or do we need to run one inference step per object type?
I tried:
- "Point to object 1 then to object 2 etc"
- "Point to object 1. Point to object 2. etc"
But none of those works.
The model is able to point to multiple things at the same - can you share the exact prompt you tried & result you got?
"Point to all the black stones on the main go board. Point to all the white stones on the main go board."
I tried many different prompts. Can you share with me one that you know works?
Sorry I should have been more clear. By grouping I mean instead of getting one single <points x1="8.9" y1="92.0" ></points>, I would get one for every objects:
<points x1="8.9" y1="92.0" alt="object1">object1</points><points x1="8.9" y1="92.0" alt="object2">object2</points>
point_qa: seems to works well. Thank you!

