Aria Model Card
Key features
- SoTA Multimodal Native Performance: Aria achieves strong performance on a wide range of multimodal, language, and coding tasks. It is superior in video and document understanding.
- Lightweight and Fast: Aria is a mixture-of-expert model with 3.9B activated parameters per token. It efficently encodes visual input of variable sizes and aspect ratios.
- Long Multimodal Context Window: Aria supports multimodal input of up to 64K tokens. It can caption a 256-frame video in 10 seconds.
🔗 Try Aria! · 📖 Blog · 📌 Paper · ⭐ GitHub · 🟣 Discord
Benchmark
Category | Benchmark | Aria | Pixtral 12B | Llama3.2 11B | GPT-4o mini | Gemini-1.5 Flash |
---|---|---|---|---|---|---|
Knowledge (Multimodal) | MMMU | 54.9 | 52.5 | 50.7 | 59.4 | 56.1 |
Math (Multimodal) | MathVista | 66.1 | 58.0 | 51.5 | - | 58.4 |
Document | DocQA | 92.6 | 90.7 | 84.4 | - | 89.9 |
Chart | ChartQA | 86.4 | 81.8 | 83.4 | - | 85.4 |
Scene Text | TextVQA | 81.1 | - | - | - | 78.7 |
General Visual QA | MMBench-1.1 | 80.3 | - | - | 76.0 | - |
Video Understanding | LongVideoBench | 65.3 | 47.4 | 45.7 | 58.8 | 62.4 |
Knowledge (Language) | MMLU (5-shot) | 73.3 | 69.2 | 69.4 | - | 78.9 |
Math (Language) | MATH | 50.8 | 48.1 | 51.9 | 70.2 | - |
Reasoning (Language) | ARC Challenge | 91.0 | - | 83.4 | 96.4 | - |
Coding | HumanEval | 73.2 | 72.0 | 72.6 | 87.2 | 74.3 |
Quick Start
Installation
pip install transformers==4.45.0 accelerate==0.34.1 sentencepiece==0.2.0 torchvision requests torch Pillow
pip install flash-attn --no-build-isolation
# For better inference performance, you can install grouped-gemm, which may take 3-5 minutes to install
pip install grouped_gemm==0.1.6
Inference
Aria has 25.3B total parameters, it can be loaded in one A100 (80GB) GPU with bfloat16 precision.
Here is a code snippet to show you how to use Aria.
import requests
import torch
from PIL import Image
from transformers import AutoModelForCausalLM, AutoProcessor
model_id_or_path = "rhymes-ai/Aria"
model = AutoModelForCausalLM.from_pretrained(model_id_or_path, device_map="auto", torch_dtype=torch.bfloat16, trust_remote_code=True)
processor = AutoProcessor.from_pretrained(model_id_or_path, trust_remote_code=True)
image_path = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png"
image = Image.open(requests.get(image_path, stream=True).raw)
messages = [
{
"role": "user",
"content": [
{"text": None, "type": "image"},
{"text": "what is the image?", "type": "text"},
],
}
]
text = processor.apply_chat_template(messages, add_generation_prompt=True)
inputs = processor(text=text, images=image, return_tensors="pt")
inputs["pixel_values"] = inputs["pixel_values"].to(model.dtype)
inputs = {k: v.to(model.device) for k, v in inputs.items()}
with torch.inference_mode(), torch.cuda.amp.autocast(dtype=torch.bfloat16):
output = model.generate(
**inputs,
max_new_tokens=500,
stop_strings=["<|im_end|>"],
tokenizer=processor.tokenizer,
do_sample=True,
temperature=0.9,
)
output_ids = output[0][inputs["input_ids"].shape[1]:]
result = processor.decode(output_ids, skip_special_tokens=True)
print(result)
Advanced Inference and Fine-tuning
We provide a codebase for more advanced usage of Aria, including vllm inference, cookbooks, and fine-tuning on custom datasets.
Citation
If you find our work helpful, please consider citing.
@article{aria,
title={Aria: An Open Multimodal Native Mixture-of-Experts Model},
author={Dongxu Li and Yudong Liu and Haoning Wu and Yue Wang and Zhiqi Shen and Bowen Qu and Xinyao Niu and Guoyin Wang and Bei Chen and Junnan Li},
year={2024},
journal={arXiv preprint arXiv:2410.05993},
}
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