Aria-Base-8K Model Card

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This checkpoint is one of base models of 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).

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 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 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.

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 model to load this checkpoint. However, as the base model, it might not be able to yield optimal chat performance.

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 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},
}