|
---
|
|
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> -->
|
|
|
|
This is a fork of the [rhymes-ai/Aria](https://huggingface.co/rhymes-ai/Aria) model. The only modification is replacing [grouped GEMM](https://github.com/tgale96/grouped_gemm) with a sequential MLP. In this configuration, each expert is implemented as a `torch.nn.Linear` layer executed in sequence. This adjustment simplifies quantization with current open-source libraries, which are optimized for `nn.Linear` layers.
|
|
|
|
While the sequential MLP approach aids in easier quantization, using grouped GEMM provides the advantage of faster inference speed.
|
|
|
|
|
|
## 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
|
|
```
|
|
|
|
### Inference
|
|
|
|
```python
|
|
import requests
|
|
import torch
|
|
from PIL import Image
|
|
from transformers import AutoModelForCausalLM, AutoProcessor
|
|
|
|
model_id_or_path = "rhymes-ai/Aria-sequential_mlp"
|
|
|
|
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)
|
|
``` |