metadata
license: apache-2.0
base_model:
- rhymes-ai/Aria
This repository offers int8 quantized weights of the rhymes-ai/Aria model utilizing the TorchAO quantization framework. It now supports inference within 30GB of GPU memory.
Quick Start
Installation
pip install transformers==4.45.0 accelerate==0.34.1 sentencepiece==0.2.0 torch==2.5.0 torchao==0.6.1 torchvision requests Pillow
pip install flash-attn --no-build-isolation
Inference
import requests
import torch
from PIL import Image
from transformers import AutoModelForCausalLM, AutoProcessor
model_id_or_path = "rhymes-ai/Aria-torchao-int8wo"
model = AutoModelForCausalLM.from_pretrained(
model_id_or_path,
device_map="auto",
torch_dtype=torch.bfloat16,
trust_remote_code=True,
attn_implementation="flash_attention_2",
)
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)