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--- |
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license: apache-2.0 |
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language: |
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- en |
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library_name: transformers |
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tags: |
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- multimodal |
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- aria |
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--- |
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<!-- <p align="center"> |
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<br>Aria</br> |
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</p> |
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<p align="center"> |
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🔗 <a href="https://huggingface.co" target="_blank"> Try Aria!</a> · 📖 <a href="https://huggingface.co" target="_blank">Blog</a> · 📌 <a href="https://huggingface.co" target="_blank">Paper</a> · |
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·🖤 <a href="https://huggingface.co" target="_blank">GitHub</a> 💜 <a href="https://huggingface.co" target="_blank">Discord</a> |
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· 💙 <a href="https://huggingface.co" target="_blank">Twitter</a> |
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</p> |
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--> |
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# Aria Model Card |
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<!-- |
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- Aria is the **first open multimodal native MoE** model, capable of seamlessly handling various input modalities within a MoE architecture. |
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- Aria performs **on par with GPT-4o mini and Gemini 1.5 Flash** across a range of multimodal tasks while maintaining strong performance on **text**-only tasks. |
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- Compared to similar or even larger models, Aria boasts **faster speeds** and **lower costs**. This high efficiency stems from its ability to activate only 3.9B parameters during inference – the **fewest** among models with comparable performance. |
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--> |
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## Key features |
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- **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. |
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- **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. |
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- **Long Multimodal Context Window**: Aria supports multimodal input of up to 64K tokens. It can caption a 256-frame video in 10 seconds. |
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<!-- # Model Info |
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| Model | Download | Parameter | Context Length | |
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| :---- | :------- | :------------ | :------ | |
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| Aria | < HF link - TBD> | • Activation: 3.9B (3.5B MoE + 0.4B Visual Encoder) <br> • Total: 25.3B | 64K | --> |
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## Benchmark |
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| Category | Benchmark | Aria | Pixtral 12B | Llama3.2 11B | GPT-4o mini | Gemini-1.5 Flash | |
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|-------------------------------------|-------------------|--------|-------------|--------------|-------------|------------------| |
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| **Knowledge (Multimodal)** | MMMU | 54.9 | 52.5 | 49.6 | 59.4 | 56.1 | |
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| **Math (Multimodal)** | MathVista | 66.1 | 58.0 | 51.5 | - | 63.8 | |
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| **Document** | DocQA | 92.6 | 90.7 | 84.4 | - | 89.9 | |
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| **Chart** | ChartQA | 86.4 | 81.8 | 78.7 | - | 85.4 | |
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| **Scene Text** | TextVQA | 81.1 | - | - | - | 78.7 | |
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| **General Visual QA** | MMBench-1.1 | 80.3 | - | - | 76.0 | - | |
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| **Video Understanding** | LongVideoBench | 66.6 | 47.4 | 45.7 | 58.8 | 62.4 | |
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| **Knowledge (Language)** | MMLU (5-shot) | 73.3 | 69.2 | 69.4 | - | 78.9 | |
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| **Math (Language)** | MATH | 50.8 | 48.1 | 51.9 | 70.2 | - | |
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| **Reasoning (Language)** | ARC Challenge | 91.0 | - | 83.4 | 96.4 | - | |
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| **Coding** | HumanEval | 73.2 | 72.0 | 72.6 | 87.2 | 74.3 | |
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## Quick Start |
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### Installation |
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``` |
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pip install git+github.com/rhymes-ai/Aria.git |
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pip install flash-attn --no-build-isolation |
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``` |
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### Inference |
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Aria has 25.3B total parameters, it can be loaded in one A100 (80GB) GPU with bfloat16 precision. |
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Here is a code snippet to show you how to use Aria. |
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```python |
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import requests |
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import torch |
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from PIL import Image |
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from transformers import AutoModelForCausalLM, AutoProcessor |
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model_id_or_path = "rhymes-ai/Aria" |
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model = AutoModelForCausalLM.from_pretrained(model_id_or_path, device_map="auto", torch_dtype=torch.bfloat16, trust_remote_code=True) |
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processor = AutoProcessor.from_pretrained(model_id_or_path, trust_remote_code=True) |
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image_path = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png" |
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image = Image.open(requests.get(image_path, stream=True).raw) |
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messages = [ |
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{ |
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"role": "user", |
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"content": [ |
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{"text": None, "type": "image"}, |
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{"text": "what is the image?", "type": "text"}, |
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], |
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} |
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] |
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text = processor.apply_chat_template(messages, add_generation_prompt=True) |
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inputs = processor(text=text, images=image, return_tensors="pt") |
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inputs["pixel_values"] = inputs["pixel_values"].to(model.dtype) |
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inputs = {k: v.to(model.device) for k, v in inputs.items()} |
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with torch.inference_mode(), torch.cuda.amp.autocast(dtype=torch.bfloat16): |
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output = model.generate( |
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**inputs, |
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max_new_tokens=500, |
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stop_strings=["<|im_end|>"], |
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tokenizer=processor.tokenizer, |
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do_sample=True, |
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temperature=0.9, |
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) |
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output_ids = output[0][inputs["input_ids"].shape[1]:] |
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result = processor.decode(output_ids, skip_special_tokens=True) |
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print(result) |
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``` |
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### Advanced Inference and Fine-tuning |
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We provide a [codebase](https://github.com/rhymes-ai/Aria) for more advanced usage of Aria, |
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including vllm inference, cookbooks, and fine-tuning on custom datasets. |
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## Citation |
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If you find our work helpful, please consider citing. |
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``` |
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@article{aria, |
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title={}, |
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author={}, |
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year={2024}, |
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journal={} |
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} |
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``` |
