Model Card
This is Owlet-Phi-2.
Owlet is a family of lightweight but powerful multimodal models.
We provide Owlet-phi-2, which is built upon SigLIP and Phi-2.
Quickstart
Here we show a code snippet to show you how to use the model with transformers.
Before running the snippet, you need to install the following dependencies:
pip install torch transformers accelerate pillow decord
import torch
import transformers
from transformers import AutoModelForCausalLM, AutoTokenizer
from PIL import Image
import warnings
# disable some warnings
transformers.logging.set_verbosity_error()
transformers.logging.disable_progress_bar()
warnings.filterwarnings('ignore')
# set device
device = 'cuda' # or cpu
torch.set_default_device(device)
# create model
print('Loading the model...')
model = AutoModelForCausalLM.from_pretrained(
'phronetic-ai/owlet-phi-2',
torch_dtype=torch.float16, # float32 for cpu
device_map='auto',
trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained(
'phronetic-ai/owlet-phi-2',
trust_remote_code=True)
print('Model loaded. Processing the query...')
# text prompt
prompt = 'What is happening in the video?'
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).to(device)
# image or video file path
file_path = 'sample.mp4'
input_tensor = model.process(file_path, model.config).to(model.device, dtype=model.dtype)
# generate
output_ids = model.generate(
input_ids,
images=input_tensor,
max_new_tokens=100,
use_cache=True)[0]
print(f'Response: {tokenizer.decode(output_ids[input_ids.shape[1]:], skip_special_tokens=True).strip()}')
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