AIN

Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI), UAE

       Amid the swift progress of large language models (LLMs) and their evolution into large multimodal models (LMMs), significant strides have been made in high-resource languages such as English and Chinese. While Arabic LLMs have seen notable progress, Arabic LMMs remain largely unexplored, often narrowly focusing on a few specific aspects of the language and visual understanding. To bridge this gap, we introduce AIN - the Arabic Inclusive Multimodal Model- designed to excel across diverse domains.
       AIN is an English-Arabic bilingual LMM designed to excel in English and Arabic, leveraging carefully constructed 3.6 million high-quality Arabic-English multimodal data samples. AIN demonstrates state-of-the-art Arabic performance, while also possessing strong English-language visual capabilities.

🌟 Key Features

• The first Arabic-centric inclusive Large Multimodal Model (LMM) trained on 3.6M samples.
• Includes 35% authentic Arabic data within its Arabic data subset.
• Achieves superior performance compared to open- and closed-source models (e.g., GPT-4o) and open-source models (e.g., Qwen2-VL-7B) across tasks such as OCR and specialized domains.
• Demonstrates robust bilingual capabilities (Arabic/English), validated through comprehensive testing and human evaluation across 17 Arab countries.
• Exhibits advanced cultural understanding and domain expertise in fields such as medical imaging, agriculture, and scientific visualization.

⚖️ Quick Start

Please install the qwen vision kit. This includes base64, URLs, and interleaved images and videos. You can install it using the following command:

pip install qwen-vl-utils

Here we show a code snippet to show you how to use the chat model with transformers and qwen_vl_utils:

from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor
from qwen_vl_utils import process_vision_info
# default: Load the model on the available device(s)
model = Qwen2VLForConditionalGeneration.from_pretrained(
    "MBZUAI/AIN", torch_dtype="auto", device_map="auto"
)
# We recommend enabling flash_attention_2 for better acceleration and memory saving, especially in multi-image and video scenarios.
# model = Qwen2VLForConditionalGeneration.from_pretrained(
#     "MBZUAI/AIN",
#     torch_dtype=torch.bfloat16,
#     attn_implementation="flash_attention_2",
#     device_map="auto",
# )
# default processer
processor = AutoProcessor.from_pretrained("MBZUAI/AIN")
# The default range for the number of visual tokens per image in the model is 4-16384. You can set min_pixels and max_pixels according to your needs, such as a token count range of 256-1280, to balance speed and memory usage.
# min_pixels = 256*28*28
# max_pixels = 1280*28*28
# processor = AutoProcessor.from_pretrained("MBZUAI/AIN", min_pixels=min_pixels, max_pixels=max_pixels)
messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "image",
                "image": "https://huggingface.co/MBZUAI/AIN/resolve/main/assets_hf/demo_image.jpeg",
            },
            {"type": "text", "text": "يرجى وصف هذه الصورة."},
        ],
    }
]
# Preparation for inference
text = processor.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True
)
image_inputs, video_inputs = process_vision_info(messages)
inputs = processor(
    text=[text],
    images=image_inputs,
    videos=video_inputs,
    padding=True,
    return_tensors="pt",
)
inputs = inputs.to("cuda")
# Inference: Generation of the output
generated_ids = model.generate(**inputs, max_new_tokens=128)
generated_ids_trimmed = [
    out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
output_text = processor.batch_decode(
    generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
print(output_text)

from PIL import Image
import requests
import torch
from torchvision import io
from typing import Dict
from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor
# Load the model in half-precision on the available device(s)
model = Qwen2VLForConditionalGeneration.from_pretrained(
    "MBZUAI/AIN", torch_dtype="auto", device_map="auto"
)
processor = AutoProcessor.from_pretrained("MBZUAI/AIN")
# Image
url = "https://huggingface.co/MBZUAI/AIN/resolve/main/assets_hf/demo_image.jpeg"
image = Image.open(requests.get(url, stream=True).raw)
conversation = [
    {
        "role": "user",
        "content": [
            {
                "type": "image",
            },
            {"type": "text", "text": "Describe this image in Arabic."},
        ],
    }
]
# Preprocess the inputs
text_prompt = processor.apply_chat_template(conversation, add_generation_prompt=True)
# Excepted output: 'system\nYou are a helpful assistant.\nuser\nDescribe this image.\nassistant\n'
inputs = processor(
    text=[text_prompt], images=[image], padding=True, return_tensors="pt"
)
inputs = inputs.to("cuda")
# Inference: Generation of the output
output_ids = m