molmoact-7b-d-awq

19
1
2 languages
license:apache-2.0
by
ronantakizawa
Image Model
OTHER
7B params
New
19 downloads
Early-stage
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Edge AI:
Mobile
Laptop
Server
16GB+ RAM
Mobile
Laptop
Server
Quick Summary

This is a 4-bit AWQ quantized version of allenai/MolmoAct-7B-D-0812 using LLM Compressor.

Device Compatibility

Mobile
4-6GB RAM
Laptop
16GB RAM
Server
GPU
Minimum Recommended
7GB+ RAM

Code Examples

Usagepythontransformers
from transformers import AutoModelForImageTextToText, AutoProcessor, GenerationConfig
from PIL import Image
import requests

# Load model and processor
processor = AutoProcessor.from_pretrained(
    "ronantakizawa/molmoact-7b-d-awq-w4a16",
    trust_remote_code=True,
    torch_dtype='auto',
    device_map='auto'
)

model = AutoModelForCausalLM.from_pretrained(
    "ronantakizawa/molmoact-7b-d-awq-w4a16",
    trust_remote_code=True,
    torch_dtype='auto',
    device_map='auto'
)

# Process the image and text
inputs = processor.process(
    images=[Image.open(requests.get("https://picsum.photos/id/237/536/354", stream=True).raw)],
    text="What actions can be performed with the objects in this image?"
)

# Move inputs to the correct device and make a batch of size 1
inputs = {k: v.to(model.device).unsqueeze(0) for k, v in inputs.items()}

# Generate output
output = model.generate_from_batch(
    inputs,
    GenerationConfig(max_new_tokens=200, stop_strings="<|endoftext|>"),
    tokenizer=processor.tokenizer
)

# Decode the generated tokens
generated_tokens = output[0, inputs['input_ids'].size(1):]
generated_text = processor.tokenizer.decode(generated_tokens, skip_special_tokens=True)
print(generated_text)
Usagepythontransformers
from transformers import AutoModelForImageTextToText, AutoProcessor, GenerationConfig
from PIL import Image
import requests

# Load model and processor
processor = AutoProcessor.from_pretrained(
    "ronantakizawa/molmoact-7b-d-awq-w4a16",
    trust_remote_code=True,
    torch_dtype='auto',
    device_map='auto'
)

model = AutoModelForCausalLM.from_pretrained(
    "ronantakizawa/molmoact-7b-d-awq-w4a16",
    trust_remote_code=True,
    torch_dtype='auto',
    device_map='auto'
)

# Process the image and text
inputs = processor.process(
    images=[Image.open(requests.get("https://picsum.photos/id/237/536/354", stream=True).raw)],
    text="What actions can be performed with the objects in this image?"
)

# Move inputs to the correct device and make a batch of size 1
inputs = {k: v.to(model.device).unsqueeze(0) for k, v in inputs.items()}

# Generate output
output = model.generate_from_batch(
    inputs,
    GenerationConfig(max_new_tokens=200, stop_strings="<|endoftext|>"),
    tokenizer=processor.tokenizer
)

# Decode the generated tokens
generated_tokens = output[0, inputs['input_ids'].size(1):]
generated_text = processor.tokenizer.decode(generated_tokens, skip_special_tokens=True)
print(generated_text)
Licensebibtex
@misc{molmoact-7b-d-awq,
  title={MolmoAct-7B-D AWQ 4-bit},
  author={Quantized by ronantakizawa},
  year={2025},
  url={https://huggingface.co/ronantakizawa/molmoact-7b-d-awq-w4a16}
}
Licensebibtex
@misc{molmoact-7b-d-awq,
  title={MolmoAct-7B-D AWQ 4-bit},
  author={Quantized by ronantakizawa},
  year={2025},
  url={https://huggingface.co/ronantakizawa/molmoact-7b-d-awq-w4a16}
}

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