Molmo2-4B

<img src="molmo_2_logo_RGB.png" alt="Logo for the Molmo2 Project" style="width: auto; height: 50px;">

Molmo2-4B

Molmo2 is a family of open vision-language models developed by the Allen Institute for AI (Ai2) that support image, video and multi-image understanding and grounding. Molmo2 models are trained on publicly available third party datasets as referenced in our technical report and Molmo2 data, a collection of datasets with highly-curated image-text and video-text pairs. It has state-of-the-art performance among multimodal models with a similar size. You can find all models in the Molmo2 family here.

Learn more about the Molmo2 family in our announcement blog post.

Molmo2-4B is based on Qwen3-4B-Instruct and uses SigLIP 2 as vision backbone. It outperforms others in the class of open weight and data models on short videos, counting, and captioning, and is competitive on long-videos.

Ai2 is commited to open science. The Molmo2 datasets are available here. All other artifacts used in creating Molmo2 (training code, evaluations, intermediate checkpoints) will be made available at a later date, furthering our commitment to open-source AI development and reproducibility.

Quick links:

• 📂 All Models
• 📃 Paper
• 🎥 Blog with Videos

Quick Start

Setup Conda Environment

conda create --name transformers4571 python=3.11
conda activate transformers4571
pip install transformers==4.57.1
pip install torch pillow einops torchvision accelerate decord2 molmo_utils

General Video QA

from transformers import AutoProcessor, AutoModelForImageTextToText
import torch

model_id="allenai/Molmo2-4B"

# load the processor
processor = AutoProcessor.from_pretrained(
    model_id,
    trust_remote_code=True,
    dtype="auto",
    device_map="auto"
)

# load the model
model = AutoModelForImageTextToText.from_pretrained(
     model_id,
     trust_remote_code=True,
     dtype="auto",
     device_map="auto"
)

# process the video and text
messages = [
    {
        "role": "user",
        "content": [
            dict(type="text", text="Which animal appears in the video?"),
            dict(type="video", video="https://storage.googleapis.com/oe-training-public/demo_videos/many_penguins.mp4"),
        ],
    }
]

inputs = processor.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_tensors="pt",
    return_dict=True,
)

inputs = {k: v.to(model.device) for k, v in inputs.items()}

# generate output
with torch.inference_mode():
    generated_ids = model.generate(**inputs, max_new_tokens=2048)

# only get generated tokens; decode them to text
generated_tokens = generated_ids[0, inputs['input_ids'].size(1):]
generated_text = processor.tokenizer.decode(generated_tokens, skip_special_tokens=True)

# print the generated text
print(generated_text)

Pointing Video QA

from transformers import AutoProcessor, AutoModelForImageTextToText
import torch
from molmo_utils import process_vision_info
import re

model_id="allenai/Molmo2-4B"

# load the processor
processor = AutoProcessor.from_pretrained(
    model_id,
    trust_remote_code=True,
    dtype="auto",
    device_map="auto"
)

# load the model
model = AutoModelForImageTextToText.from_pretrained(
    model_id,
    trust_remote_code=True,
    dtype="auto",
    device_map="auto"
)

COORD_REGEX = re.compile(rf"<(?:points|tracks).*? coords=\"([0-9\t:;, .]+)\"/?>")
FRAME_REGEX = re.compile(rf"(?:^|\t|:|,|;)([0-9\.]+) ([0-9\. ]+)")
POINTS_REGEX = re.compile(r"([0-9]+) ([0-9]{3,4}) ([0-9]{3,4})")

def _points_from_num_str(text, image_w, image_h, extract_ids=False):
    all_points = []
    for points in POINTS_REGEX.finditer(text):
        ix, x, y = points.group(1), points.group(2), points.group(3)
        # our points format assume coordinates are scaled by 1000
        x, y = float(x)/1000*image_w, float(y)/1000*image_h
        if 0 <= x <= image_w and 0 <= y <= image_h:
            yield ix, x, y


def extract_video_points(text, image_w, image_h, extract_ids=False):
    """Extract video pointing coordinates as a flattened list of (t, x, y) triplets from model output text."""
    all_points = []
    for coord in COORD_REGEX.finditer(text):
        for point_grp in FRAME_REGEX.finditer(coord.group(1)):
            frame_id = float(point_grp.group(1))
            w, h = (image_w, image_h)
            for idx, x, y in _points_from_num_str(point_grp.group(2), w, h):
                if extract_ids:
                    all_points.append((frame_id, idx, x, y))
                else:
                    all_points.append((frame_id, x, y))
    return all_points

messages = [
    {
        "role": "user",
        "content": [
            dict(type="text", text="Point to the penguins."),
            dict(type="video", video="https://storage.googleapis.com/oe-training-public/demo_videos/many_penguins.mp4"),
        ],
    }
]

# process the video using `molmo_utils.process_vision_info`
_, videos, video_kwargs = process_vision_info(messages)
videos, video_metadatas = zip(*videos)
videos, video_metadatas = list(videos), list(video_metadatas)

# apply the chat template to the input messages
text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)

# process the video and text
inputs = processor(
    videos=videos,
    video_metadata=video_metadatas,
    text=text,
    padding=True,
    return_tensors="pt",
    **video_kwargs,
)

inputs = {k: v.to(model.device) for k, v in inputs.items()}

# generate output
with torch.inference_mode():
    generated_ids = model.generate(**inputs, max_new_tokens=2048)

# only get generated tokens; decode them to text
generated_tokens = generated_ids[0, inputs['input_ids'].size(1):]
generated_text = processor.tokenizer.decode(generated_tokens, skip_special_tokens=True)

# decode video pointing outputs
points = extract_video_points(generated_text, image_w=video_metadatas[0]["width"], image_h=video_metadatas[0]["height"])
print(points)

Tracking Video QA

from transformers import AutoProcessor, AutoModelForImageTextToText
import torch
from molmo_utils import process_vision_info
import re

model_id="allenai/Molmo2-4B"

# load the processor
processor = AutoProcessor.from_pretrained(
    model_id,
    trust_remote_code=True,
    dtype="auto",
    device_map="auto"
)

# load the model
model = AutoModelForImageTextToText.from_pretrained(
    model_id,
    trust_remote_code=True,
    dtype="auto",
    device_map="auto"
)

COORD_REGEX = re.compile(rf"<(?:points|tracks).*? coords=\"([0-9\t:;, .]+)\"/?>")
FRAME_REGEX = re.compile(rf"(?:^|\t|:|,|;)([0-9\.]+) ([0-9\. ]+)")
POINTS_REGEX = re.compile(r"([0-9]+) ([0-9]{3,4}) ([0-9]{3,4})")

def _points_from_num_str(text, image_w, image_h, extract_ids=False):
    all_points = []
    for points in POINTS_REGEX.finditer(text):
        ix, x, y = points.group(1), points.group(2), points.group(3)
        # our points format assume coordinates are scaled by 1000
        x, y = float(x)/1000*image_w, float(y)/1000*image_h
        if 0 <= x <= image_w and 0 <= y <= image_h:
            yield ix, x, y


def extract_video_points(text, image_w, image_h, extract_ids=False):
    """Extract video pointing coordinates as a flattened list of (t, x, y) triplets from model output text."""
    all_points = []
    for coord in COORD_REGEX.finditer(text):
        for point_grp in FRAME_REGEX.finditer(coord.group(1)):
            frame_id = float(point_grp.group(1))
            w, h = (image_w, image_h)
            for idx, x, y in _points_from_num_str(point_grp.group(2), w, h):
                if extract_ids:
                    all_points.append((frame_id, idx, x, y))
                else:
                    all_points.append((frame_id, x, y))
    return all_points

# use higher max fps for tracking
messages = [
    {
        "role": "user",
        "content": [
            dict(type="text", text="Track the player who is dunking"),
            dict(type="video", video="https://storage.googleapis.com/oe-training-public/demo_videos/arena_basketball.mp4", max_fps=8),
        ],
    }
]

# process the video using `molmo_utils.process_vision_info`
_, videos, video_kwargs = process_vision_info(messages)
videos, video_metadatas = zip(*videos)
videos, video_metadatas = list(videos), list(video_metadatas)

# apply the chat template to the input messages
text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)

# process the video and text
inputs = processor(
    videos=videos,
    video_metadata=video_metadatas,
    text=text,
    padding=True,
    return_tensors="pt",
    **video_kwargs,
)

inputs = {k: v.to(model.device) for k, v in inputs.items()}

# generate output
with torch.inference_mode():
    generated_ids = model.generate(**inputs, max_new_tokens=2048)

# only get generated tokens; decode them to text
generated_tokens = generated_ids[0, inputs['input_ids'].size(1):]
generated_text = processor.tokenizer.decode(generated_tokens, skip_special_tokens=True)

# decode video pointing outputs
points = extract_video_points(generated_text, image_w=video_metadatas[0]["width"], image_h=video_metadatas[0]["height"])
print(points)

Multi-image QA

from transformers import AutoProcessor, AutoModelForImageTextToText
import torch
import requests
from PIL import Image

model_id="allenai/Molmo2-4B"

# load the processor
processor = AutoProcessor.from_pretrained(
    model_id,
    trust_remote_code=True,
    dtype="auto",
    device_map="auto",
)

# load the model
model = AutoModelForImageTextToText.from_pretrained(
    model_id,
    trust_remote_code=True,
    dtype="auto",
    device_map="auto",
)

# process the image and text
messages = [
    {
        "role": "user",
        "content": [
            dict(type="text", text="Compare these images."),
            dict(type="image", image=Image.open(requests.get("https://picsum.photos/id/237/536/354", stream=True).raw)),
            dict(type="image", image=Image.open(requests.get("https://vllm-public-assets.s3.us-west-2.amazonaws.com/vision_model_images/cherry_blossom.jpg", stream=True).raw))
        ],
    }
]

inputs = processor.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_tensors="pt",
    return_dict=True,
)

inputs = {k: v.to(model.device) for k, v in inputs.items()}

# generate output
with torch.inference_mode():
    generated_ids = model.generate(**inputs, max_new_tokens=448)

# only get generated tokens; decode them to text
generated_tokens = generated_ids[0, inputs['input_ids'].size(1):]
generated_text = processor.tokenizer.decode(generated_tokens, skip_special_tokens=True)

# print the generated text
print(generated_text)

Multi-Image Point QA

from transformers import AutoProcessor, AutoModelForImageTextToText
import torch
import re
from PIL import Image
import requests

model_id="allenai/Molmo2-4B"

# load the processor
processor = AutoProcessor.from_pretrained(
    model_id,
    trust_remote_code=True,
    dtype="auto",
    device_map="auto",
    token=True
)

# load the model
model = AutoModelForImageTextToText.from_pretrained(
    model_id,
    trust_remote_code=True,
    dtype="auto",
    device_map="auto",
    token=True
)

COORD_REGEX = re.compile(rf"<(?:points|tracks).*? coords=\"([0-9\t:;, .]+)\"/?>")
FRAME_REGEX = re.compile(rf"(?:^|\t|:|,|;)([0-9\.]+) ([0-9\. ]+)")
POINTS_REGEX = re.compile(r"([0-9]+) ([0-9]{3,4}) ([0-9]{3,4})")

def _points_from_num_str(text, image_w, image_h, extract_ids=False):
    all_points = []
    for points in POINTS_REGEX.finditer(text):
        ix, x, y = points.group(1), points.group(2), points.group(3)
        # our points format assume coordinates are scaled by 1000
        x, y = float(x)/1000*image_w, float(y)/1000*image_h
        if 0 <= x <= image_w and 0 <= y <= image_h:
            yield ix, x, y


def extract_multi_image_points(text, image_w, image_h, extract_ids=False):
    """Extract pointing coordinates as a flattened list of (frame_id, x, y) triplets from model output text."""
    all_points = []
    if isinstance(image_w, (list, tuple)) and isinstance(image_h, (list, tuple)):
        assert len(image_w) == len(image_h)
        diff_res = True
    else:
        diff_res = False
    for coord in COORD_REGEX.finditer(text):
        for point_grp in FRAME_REGEX.finditer(coord.group(1)):
            frame_id = int(point_grp.group(1)) if diff_res else float(point_grp.group(1))
            w, h = (image_w[frame_id-1], image_h[frame_id-1]) if diff_res else (image_w, image_h)
            for idx, x, y in _points_from_num_str(point_grp.group(2), w, h):
                if extract_ids:
                    all_points.append((frame_id, idx, x, y))
                else:
                    all_points.append((frame_id, x, y))
    return all_points

# process the image and text
images = [
    Image.open(requests.get("https://storage.googleapis.com/oe-training-public/demo_images/boat1.jpeg", stream=True).raw),
    Image.open(requests.get("https://storage.googleapis.com/oe-training-public/demo_images/boat2.jpeg", stream=True).raw)
]

messages = [
    {
        "role": "user",
        "content": [
            dict(type="text", text="Point to the boats"),
            dict(type="image", image=images[0]),
            dict(type="image", image=images[1]),
        ],
    }
]

inputs = processor.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_tensors="pt",
    return_dict=True,
)

inputs = {k: v.to(model.device) for k, v in inputs.items()}

# generate output
with torch.inference_mode():
    generated_ids = model.generate(**inputs, max_new_tokens=2048)

# only get generated tokens; decode them to text
generated_tokens = generated_ids[0, inputs['input_ids'].size(1):]
generated_text = processor.tokenizer.decode(generated_tokens, skip_special_tokens=True)

points = extract_multi_image_points(
    generated_text,
    [images[0].width, images[1].width],
    [images[0].height, images[1].height],
)
print(points)

Evaluations

We report the Average Score on 15 Academic Benchmarks here. For details on the evals, refer to the main video results table in our technical report.

License and Use

This model is licensed under Apache 2.0. It is intended for research and educational use in accordance with Ai2’s Responsible Use Guidelines. This model is trained on third party datasets that are subject to academic and non-commercial research use only. Please review the sources to determine if this model is appropriate for your use case.