README.md

metadata

license: apache-2.0
language:
  - en
pipeline_tag: image-text-to-text

Cerule - A Tiny Mighty Vision Model

Based on Google's - Gemma-2b + SigLIP

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We train and release "Cerule", a tiny yet powerful Vision Lanuage Model based on the newly released Google's Gemma-2b and Google's SigLIP.

We utilise highly efficient data selection techniques with:

- Pretraining stage : 650K images (A LAION 2M Subset)
- Finetuning stage : 695K images (SVIT-mix-665K modified for finetuning(Dataset SOON!))

The training setup was 4xA100's 80GB and took ~6 hours to pretrain and ~13 hours to finetune. We modify and adapt the training code from LLaVA.

🚨 Training code, Data and more details to release soon!

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Image	Example
	Describe the image The image is a playful and surreal depiction of a man in a space suit, sitting on a chair and holding a green beer bottle. The man is wearing a white space suit, complete with a helmet and gloves. His feet are clad in black and white shoes, and he is placed on a sandy surface. The background features a large, blue planet, with a moon and a star visible in the sky.
	Who are the characters in the image? The image features three characters, two of them are Mario and Luigi, and the third one is Yoshi. Describe the actions of the characters The Mario and Luigi characters are holding their arms out, as if they are waving. Yoshi is standing on its own, with its arms folded.
	What's funny about this image? The image is quite humorous as it depicts a man ironing clothes on the back of a yellow taxi cab. This is not a typical sight you'd expect to see in everyday life.

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Training and Inference:

We will release the training code in some time.

Inference:

Please note that running the inference code at this stage may result in errors. The proper code for training and inference shall be released soon! Before running the snippet, you need to install the following dependencies:

pip install torch transformers accelerate pillow

import torch
import transformers
from transformers import AutoModelForCausalLM, AutoTokenizer
from PIL import Image
import warnings

transformers.logging.set_verbosity_error()
transformers.logging.disable_progress_bar()
warnings.filterwarnings('ignore')

torch.set_default_device('cuda')  # or 'cpu'

model = AutoModelForCausalLM.from_pretrained(
    'Tensoic/Cerule',
    torch_dtype=torch.float16,
    device_map='auto',
    trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained(
    'Tensoic/Cerule',
    trust_remote_code=True)

# text prompt
prompt = 'Who are these charecters?'
text = f"A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: <image>\n{prompt} ASSISTANT:"
text_chunks = [tokenizer(chunk).input_ids for chunk in text.split('<image>')]
input_ids = torch.tensor(text_chunks[0] + [-200] + text_chunks[1], dtype=torch.long).unsqueeze(0)

image = Image.open('examples/mario.png')
image_tensor = model.process_images([image], model.config).to(dtype=model.dtype)

# generate
output_ids = model.generate(
    input_ids,
    images=image_tensor,
    max_new_tokens=100,
    use_cache=False)[0] #keep use_cache=False or else it might run into some torch dim error

print(tokenizer.decode(output_ids[input_ids.shape[1]:], skip_special_tokens=False).strip())

License

Apache 2.0? Maybe... idk