huggingface--transformers/docs/source/en/model_doc/aria.md

This model was published in HF papers on 2024-10-08 and contributed to Hugging Face Transformers on 2024-12-06.

Aria

Aria is a multimodal mixture-of-experts (MoE) model. The goal of this model is to open-source a training recipe for creating a multimodal native model from scratch. Aria has 3.9B and 3.5B activated parameters per visual and text token respectively. Text is handled by a MoE decoder and visual inputs are handled by a lightweight visual encoder. It is trained in 4 stages, language pretraining, multimodal pretraining, multimodal long-context pretraining, and multimodal post-training.

You can find all the original Aria checkpoints under the Aria organization.

Tip

Click on the Aria models in the right sidebar for more examples of how to apply Aria to different multimodal tasks.

The example below demonstrates how to generate text based on an image with [Pipeline] or the [AutoModel] class.

from transformers import pipeline


pipeline = pipeline(
    "image-to-text",
    model="rhymes-ai/Aria",
    device=0,
)
pipeline(
    "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg",
    text="What is shown in this image?"
)

import torch

from transformers import AutoModelForCausalLM, AutoProcessor


model = AutoModelForCausalLM.from_pretrained(
    "rhymes-ai/Aria",
    device_map="auto",
    attn_implementation="sdpa"
)

processor = AutoProcessor.from_pretrained("rhymes-ai/Aria")

messages = [
    {
        "role": "user", "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"},
            {"type": "text", "text": "What is shown in this image?"},
        ]
    },
]

inputs = processor.apply_chat_template(messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt").to(model.device)
inputs = inputs.to(model.device, torch.bfloat16)

output = model.generate(
    **inputs,
    max_new_tokens=15,
    stop_strings=["<|im_end|>"],
    tokenizer=processor.tokenizer,
    do_sample=True,
    temperature=0.9,
)
output_ids = output[0][inputs["input_ids"].shape[1]:]
response = processor.decode(output_ids, skip_special_tokens=True)
print(response)

Quantization reduces the memory burden of large models by representing the weights in a lower precision. Refer to the Quantization overview for more available quantization backends.

The example below uses torchao to only quantize the weights to int4 and the rhymes-ai/Aria-sequential_mlp checkpoint. This checkpoint replaces grouped GEMM with torch.nn.Linear layers for easier quantization.

# pip install torchao
import torch

from transformers import AutoModelForCausalLM, AutoProcessor, TorchAoConfig


quantization_config = TorchAoConfig("int4_weight_only", group_size=128)
model = AutoModelForCausalLM.from_pretrained(
    "rhymes-ai/Aria-sequential_mlp",
    device_map="auto",
    quantization_config=quantization_config
)
processor = AutoProcessor.from_pretrained(
    "rhymes-ai/Aria-sequential_mlp",
)

messages = [
    {
        "role": "user", "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"},
            {"type": "text", "text": "What is shown in this image?"},
        ]
    },
]

inputs = processor.apply_chat_template(messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt").to(model.device)
inputs = inputs.to(model.device, torch.bfloat16)

output = model.generate(
    **inputs,
    max_new_tokens=15,
    stop_strings=["<|im_end|>"],
    tokenizer=processor.tokenizer,
    do_sample=True,
    temperature=0.9,
)
output_ids = output[0][inputs["input_ids"].shape[1]:]
response = processor.decode(output_ids, skip_special_tokens=True)
print(response)

AriaImageProcessor

autodoc AriaImageProcessor - preprocess

AriaImageProcessorPil

autodoc AriaImageProcessorPil - preprocess

AriaProcessor

autodoc AriaProcessor - call

AriaTextConfig

autodoc AriaTextConfig

AriaConfig

autodoc AriaConfig

AriaTextModel

autodoc AriaTextModel

AriaModel

autodoc AriaModel

AriaTextForCausalLM

autodoc AriaTextForCausalLM

AriaForConditionalGeneration

autodoc AriaForConditionalGeneration - forward - get_image_features