Instructions to use ravilution/MolmoWeb-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ravilution/MolmoWeb-4B with Transformers:

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

pipe = pipeline("image-text-to-text", model="ravilution/MolmoWeb-4B", 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("ravilution/MolmoWeb-4B", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use ravilution/MolmoWeb-4B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ravilution/MolmoWeb-4B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ravilution/MolmoWeb-4B",
		"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/ravilution/MolmoWeb-4B

SGLang

How to use ravilution/MolmoWeb-4B 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 "ravilution/MolmoWeb-4B" \
    --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": "ravilution/MolmoWeb-4B",
		"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 "ravilution/MolmoWeb-4B" \
        --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": "ravilution/MolmoWeb-4B",
		"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 ravilution/MolmoWeb-4B with Docker Model Runner:
```
docker model run hf.co/ravilution/MolmoWeb-4B
```

ravilution/MolmoWeb-4B

This is a full-precision Hugging Face– and vLLM-compatible release of allenai/MolmoWeb-4B, a vision-based web agent model by Ai2 capable of navigating and interacting with web browsers.

It follows the same idea as ravilution/MolmoWeb-8B-8bit-mlx: a personal Hub copy with a clear description and practical loading notes—here for the 4B dense checkpoint rather than an MLX quantization.

Note: This is a 4B parameter model (four safetensors shards). A few post-download patches were applied locally so tokenization and generation metadata match what downstream stacks (including vLLM) expect: eos_token_id / bos_token_id / pad_token_id, transformers_version in config.json and generation_config.json, and the tokenizer pretokenizer regex (Mistral-style (?i:...) fix). Patches are idempotent if you re-run them on a fresh download.

Refer to the original model card for benchmarks, architecture, training data, and intended use.

Use with Transformers

pip install -U transformers accelerate torch pillow

from transformers import AutoProcessor, AutoModelForImageTextToText
import torch

model_id = "ravilution/MolmoWeb-4B"

model = AutoModelForImageTextToText.from_pretrained(
    model_id,
    trust_remote_code=True,
    torch_dtype=torch.float32,
    attn_implementation="sdpa",
    device_map="auto",
)
processor = AutoProcessor.from_pretrained(
    model_id,
    trust_remote_code=True,
    padding_side="left",
)

Provenance

Upstream weights: allenai/MolmoWeb-4B
Changes on top: compatibility patches only (config / generation_config / tokenizer metadata as above); no retraining or architectural edits.

License

Apache 2.0 — see the original model for details. Please review Ai2’s Responsible Use Guidelines for intended use.

Downloads last month: 6

Safetensors

Model size

5B params

Tensor type

F32

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