Aria-Chat Model Card

Key features

• Especially Optimized For Multimodal Chat: Aria-Chat is especially optimized for open-ended and multi-round dialogs. We hope this version can provide seamless open-source multimodal chat experience.
• Improved Reliability: We have improved its reliability for generating long outputs, reducing probabilities for previously-reported bad cases like incomplete responses on Markdown tables, or endless responses on listwise outputs.
• Better Multi-Lingual Abilities: We have optimized its ability on non-English scenarios (Chinese, Spanish, French, Japanese, etc), including both multi-lingual OCR and multi-lingual dialogs.

🔗 Try Aria! · 📖 Blog · 📌 Paper · ⭐ GitHub · 🟣 Discord

Benchmark

This checkpoint is not designed for benchmarks, but for real-world open-ended applications. To this end, we evaluated on WildVision-Bench and noticed non-trivial improvements on it:

Model	Score
gpt-4o	89.15
Aria-Chat	81.3
gpt-4-vision-preview	79.78
Aria	74.1
Reka-Flash	64.65
claude-3-opus-20240229	62.03
yi-vl-plus	55.05
liuhaotian/llava-v1.6-34b	51.89
claude-3-sonnet-20240229	50.0
claude-3-haiku-20240307	37.83

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-Chat"

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},
}