Aria-Base-64K / README.md
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---
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},
}
```