MolmoAct-7B-D-Pretrain-0812

pip install einops torchvision accelerate
pip install transformers==4.52

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

ckpt = "allenai/MolmoAct-7B-D-Pretrain-0812"

# load the processor
processor = AutoProcessor.from_pretrained(
    ckpt,
    trust_remote_code=True,
    torch_dtype="bfloat16",
    device_map="auto",
    padding_side="left",
)

# load the model
model = AutoModelForImageTextToText.from_pretrained(
    ckpt,
    trust_remote_code=True,
    torch_dtype="bfloat16",
    device_map="auto",
)

# task instruction
instruction = "pick orange can"

# strictly follow this reasoning prompt
prompt = (
    f"The task is {instruction}. "
    "What is the action that the robot should take. "
    f"To figure out the action that the robot should take to {instruction}, "
    "let's think through it step by step. "
    "First, what is the depth map for this image? "
    "Second, what is the trajectory of the end effector? "
    "Based on the depth map of the image and the trajectory of the end effector, "
    "what is the action that the robot should take?"
)

# apply chat template
text = processor.apply_chat_template(
    [
        {
            "role": "user",
            "content": [dict(type="text", text=prompt)]
        }
    ], 
    tokenize=False, 
    add_generation_prompt=True,
)

# image observation
url = "https://huggingface.co/allenai/MolmoAct-7B-D-Pretrain-0812/resolve/main/example.png"
r = requests.get(url, headers={"User-Agent": "python-requests"}, timeout=30)
r.raise_for_status()
img = Image.open(BytesIO(r.content)).convert("RGB")
imgs = [img]

# process the image and text
inputs = processor(
    images=[imgs],
    text=text,
    padding=True,
    return_tensors="pt",
)

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

# generate output
with torch.inference_mode():
    with torch.autocast("cuda", enabled=True, dtype=torch.bfloat16):
        generated_ids = model.generate(**inputs, max_new_tokens=256)

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

# print the generated text
print(f"generated text: {generated_text}")

# >>>  The depth map of the image is ... The trajectory of the end effector is ...
#      Based on these information, the action that the robot should take is ...

# parse out all depth perception tokens
depth = model.parse_depth(generated_text)
print(f"generated depth perception tokens: {depth}")

# >>>  [ "<DEPTH_START><DEPTH_1><DEPTH_2>...<DEPTH_END>" ]

# parse out all visual reasoning traces
trace = model.parse_trace(generated_text)
print(f"generated visual reasoning trace: {trace}")

# >>>  [ [[242, 115], [140, 77], [94, 58], [140, 44], [153, 26]]] ]

# parse out all actions, unnormalizing with key of fractal20220817_data
action = model.parse_action(generated_text, unnorm_key="fractal20220817_data")
print(f"generated action: {action}")

# >>>  [ [0.0732076061122558, 0.08228153779226191, -0.027760173818644346, 
#         0.15932856272248652, -0.09686601126895233, 0.043916773912953344, 
#         0.996078431372549] ]

@misc{molmoact2025,
      title={MolmoAct: Action Reasoning Models that can Reason in Space}, 
      author={Jason Lee and Jiafei Duan and Haoquan Fang and Yuquan Deng and Shuo Liu and Boyang Li and Bohan Fang and Jieyu Zhang and Yi Ru Wang and Sangho Lee and Winson Han and Wilbert Pumacay and Angelica Wu and Rose Hendrix and Karen Farley and Eli VanderBilt and Ali Farhadi and Dieter Fox and Ranjay Krishna},
      year={2025},
      eprint={2508.07917},
      archivePrefix={arXiv},
      primaryClass={cs.RO},
      url={https://arxiv.org/abs/2508.07917}
}