Instructions to use philipp-zettl/MolmoE-1B-0924 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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
```

MolmoE-1B-0924

File size: 634 Bytes

56ba74f

{
  "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"
}