README.md

---
license: apache-2.0
language:
- en
library_name: transformers
pipeline_tag: image-text-to-text
tags:
- multimodal
- aria
base_model:
- rhymes-ai/Aria-Base-8K
---
<!-- <p align="center">
  <br>Aria</br>
</p>  -->


# Aria-Base-64K Model Card

<p align="center">
🔗 <a href="https://rhymes.ai/" target="_blank"> Try Aria!</a> · 📖 <a href="https://www.rhymes.ai/blog-details/aria-first-open-multimodal-native-moe-model" target="_blank">Blog</a> · 📌 <a href="https://arxiv.org/pdf/2410.05993" target="_blank">Paper</a> 
 · ⭐ <a href="https://github.com/rhymes-ai/Aria" target="_blank">GitHub</a> · 🟣 <a href="https://discord.com/invite/u8HxU23myj" target="_blank"> Discord </a>
</p> 


This checkpoint is one of base models of [Aria](https://huggingface.co/rhymes-ai/Aria), designed for research purposes as well as continue training. Specifically, Aria-Base-64K corresponds to the model checkpoint after the long-context pre-training stage (boxed in purple).

<img src="./aria-stages.png" alt="Aria Training Stages" style="width: 100%;">

Aria-Base-64K is fine-tuned from [Aria-Base-8K](https://huggingface.co/rhymes-ai/Aria-Base-8K).

<!-- 
- Aria is the **first open multimodal native MoE** model, capable of seamlessly handling various input modalities within a MoE architecture.
- 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.
- 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.
 -->
 
## Aria-Base-64K

- **Base Model After Long-Context Pre-training**: This model corresponds to the model checkpoint after the long-context pre-training stage, with 33B tokens (21B multimodal, 12B language, 69% in long-form) trained in this stage. This stage lasts 1,000 iterations, with all sequences packed to 65536 with Megatron-LM, with global batch size 512. During this training stage, the learning rate keeps constant at `3.5e-5`.
- **Appropriate for Video and Long-document Fine-tuning**: This model is recommended for long-form continue pre-training or fine-tuning, e.g. on video QA datasets or long-document QA datasets. While resource is limited, it is also possible to post-train this model with short instruction tuning datasets and transfer to long-form QA scenarios.
- **Understanding on Hundreds of Images**: This model is capable of understanding up to 250 high-resolution images or up to 500 mid-resolution images.
- **Strong Base Performance on Language and Multimodal Scenarios**: This model retains strong base performance as [Aria-Base-8K](https://huggingface.co/rhymes-ai/Aria-Base-8K).
- ***Limited Chat Template Availability***: This model is trained with a very low percentage of data (around 3%) re-formatted with the chat template. Hence, it might not be optimal to be directly used with chat templates.

<!-- # Model Info

| Model  | Download  | Parameter | Context Length |
| :---- | :------- | :------------ | :------ |
| Aria | < HF link - TBD> | • Activation: 3.9B (3.5B MoE + 0.4B Visual Encoder) <br> • Total: 25.3B | 64K           | -->


## 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

You can use the same method as the final [Aria](https://huggingface.co/rhymes-ai/Aria) model to load this checkpoint. However, as the base model, it might not be able to yield optimal chat performance.

```python
import requests
import torch
from PIL import Image
from transformers import AutoModelForCausalLM, AutoProcessor

model_id_or_path = "rhymes-ai/Aria-Base-64K"

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](https://github.com/rhymes-ai/Aria) for more advanced usage of Aria,
including vllm inference, cookbooks, and fine-tuning on custom datasets.

As it shares the same structure with the final model, 
you may just replace the `rhymes-ai/Aria` to this model path for any advanced inference and fine-tuning.


## 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},
}
```