---
inference: false
license: cc-by-4.0
---

# Model Card

<p align="center">
  <img src="./icon.png" alt="Logo" width="350">
</p>

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](https://huggingface.co/google/siglip-so400m-patch14-384) and [Phi-2](https://huggingface.co/microsoft/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:

```shell
pip install torch transformers accelerate pillow decord
```

```python
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()}')

```