README.md

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


# Aria-Base-8K 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-8K corresponds to the model checkpoint after the multimodal pre-training stage (boxed in gray).

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

<!-- 
- 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-8K

- **Base Model After Pre-training**: This model corresponds to the model checkpoint after the multimodal pre-training stage, with 1.4T tokens (1T language + 400B multimodal) trained in this stage. This stage lasts 43,000 iterations, with all sequences packed to 8192 with Megatron-LM, with global batch size 4096. During this training stage, the learning rate decays from `8.75e-5` to `3.5e-5`.
- **Appropriate for Continue Pre-training**: This model is recommended for continue pre-training, *e.g.* on domain-specific pre-training data (OCR, agent, multi-lingual), while the targeted scenario does not involve long-context inputs. Please consider fine-tuning [Aria-Base-64K](https://huggingface.co/rhymes-ai/Aria-Base-64K) for long-context scenarios.
- **Strong Base Performance on Language and Multimodal Scenarios**: This model shows excellent base performance on knowledge-related evaluations on both pure language and multimodal scenarios (MMLU 70+, MMMU 50+, *etc*).
- ***Limited Ability on Long-context Scenarios***: This model is only trained with 8K context length, and is not expected to show best performance with context length especially longer than 8K (e.g. a video with >100 frames). [Aria-Base-64K](https://huggingface.co/rhymes-ai/Aria-Base-64K) is more appropriate for longer sequence understanding.
- ***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 for chatting.



<!-- # 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-8K"

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},
}
```