Instructions to use allenai/Molmo2-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use allenai/Molmo2-8B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="allenai/Molmo2-8B", trust_remote_code=True)
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoModelForImageTextToText
model = AutoModelForImageTextToText.from_pretrained("allenai/Molmo2-8B", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use allenai/Molmo2-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "allenai/Molmo2-8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "allenai/Molmo2-8B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/allenai/Molmo2-8B

SGLang

How to use allenai/Molmo2-8B 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/Molmo2-8B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "allenai/Molmo2-8B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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/Molmo2-8B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "allenai/Molmo2-8B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use allenai/Molmo2-8B with Docker Model Runner:
```
docker model run hf.co/allenai/Molmo2-8B
```

Why are the released weights in fp32?

by BootsofLagrangian - opened Jan 26

Discussion

BootsofLagrangian

Jan 26

The paper mentions using PyTorch AMP (bfloat16) for training, but the model is released in float32.

Is there a specific reason for this? I assume you released the master weights directly (maybe for exact reproducibility or referencing OLMo's checkpointing style)?

Just curious since most recent models usually release 2-byte tensors (bf16/fp16).

P.S. Thanks for extending the max context length to 36k! I previously asked about the 4k limit in Molmo - https://huggingface.co/allenai/Molmo-72B-0924/discussions/15 -, so I'm really happy to see this massive upgrade.

sanghol

Ai2 org Feb 11

Sorry for the delayed response.
As in Molmo, we kept the model weights in full precision while performing computations in half precision.
We uploaded the original checkpoint without modification.
For more details, please refer to Figure 6 in the appendix of the Molmo paper: https://arxiv.org/pdf/2409.17146

Additionally, we evaluated loading the model weights in bfloat16 and observed only negligible differences in the outputs when using SDPA attention (attn_implementation).
Therefore, loading the model in bfloat16 is also safe in practice.

sanghol changed discussion status to closed Feb 24

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