Mplug Owl Llama 7b

🚀 MplugOwl Image-to-Text Model

MplugOwl is an image-to-text model that can generate text descriptions based on input images. It offers a seamless way to integrate image understanding into text generation tasks.

🚀 Quick Start

Get the latest codebase from Github

git clone https://github.com/X-PLUG/mPLUG-Owl.git

✨ Features

• Image-to-Text Conversion: Generate text descriptions for given images.
• Multi - turn Conversation Support: Organize context as multi - turn conversations for more interactive responses.

📦 Installation

The installation mainly involves cloning the repository from GitHub. The codebase can be obtained using the following command:

git clone https://github.com/X-PLUG/mPLUG-Owl.git

💻 Usage Examples

Basic Usage

Model initialization

from mplug_owl.modeling_mplug_owl import MplugOwlForConditionalGeneration
from mplug_owl.tokenization_mplug_owl import MplugOwlTokenizer
from mplug_owl.processing_mplug_owl import MplugOwlImageProcessor, MplugOwlProcessor

pretrained_ckpt = 'MAGAer13/mplug-owl-llama-7b'
model = MplugOwlForConditionalGeneration.from_pretrained(
    pretrained_ckpt,
    torch_dtype=torch.bfloat16,
)
image_processor = MplugOwlImageProcessor.from_pretrained(pretrained_ckpt)
tokenizer = MplugOwlTokenizer.from_pretrained(pretrained_ckpt)
processor = MplugOwlProcessor(image_processor, tokenizer)

Model inference

Prepare model inputs.

# We use a human/AI template to organize the context as a multi-turn conversation.
# <image> denotes an image placehold.
prompts = [
'''The following is a conversation between a curious human and AI assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.
Human: <image>
Human: Explain why this meme is funny.
AI: ''']

# The image paths should be placed in the image_list and kept in the same order as in the prompts.
# We support urls, local file paths and base64 string. You can custom the pre-process of images by modifying the mplug_owl.modeling_mplug_owl.ImageProcessor
image_list = ['https://xxx.com/image.jpg']

Get response.

# generate kwargs (the same in transformers) can be passed in the do_generate()
generate_kwargs = {
    'do_sample': True,
    'top_k': 5,
    'max_length': 512
}
from PIL import Image
images = [Image.open(_) for _ in image_list]
inputs = processor(text=prompts, images=images, return_tensors='pt')
inputs = {k: v.bfloat16() if v.dtype == torch.float else v for k, v in inputs.items()}
inputs = {k: v.to(model.device) for k, v in inputs.items()}
with torch.no_grad():
    res = model.generate(**inputs, **generate_kwargs)
sentence = tokenizer.decode(res.tolist()[0], skip_special_tokens=True)
print(sentence)

📄 License

This project is licensed under the Apache-2.0 license.