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Running
on
Zero
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import torch
from transformers import AutoModelForCausalLM, AutoProcessor, VisionEncoderDecoderModel
from huggingface_hub import snapshot_download
from qwen_vl_utils import process_vision_info
def load_model(model_name):
"""
Load the specified model and its processor based on the model name.
Args:
model_name (str): Name of the model ("dots.ocr" or "Dolphin").
Returns:
tuple: (model, processor) for the specified model.
"""
if model_name == "dots.ocr":
model_id = "rednote-hilab/dots.ocr"
model_path = "./models/dots-ocr-local"
snapshot_download(
repo_id=model_id,
local_dir=model_path,
local_dir_use_symlinks=False,
)
model = AutoModelForCausalLM.from_pretrained(
model_path,
attn_implementation="flash_attention_2",
torch_dtype=torch.bfloat16,
device_map="auto",
trust_remote_code=True
)
processor = AutoProcessor.from_pretrained(model_path, trust_remote_code=True)
elif model_name == "Dolphin":
model_id = "ByteDance/Dolphin"
processor = AutoProcessor.from_pretrained(model_id)
model = VisionEncoderDecoderModel.from_pretrained(model_id)
model.eval()
device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device)
model = model.half() # Use half precision
else:
raise ValueError(f"Unknown model: {model_name}")
return model, processor
def inference_dots_ocr(model, processor, image, prompt, max_new_tokens):
"""
Perform inference using the dots.ocr model.
Args:
model: The loaded dots.ocr model.
processor: The corresponding processor.
image (PIL.Image): Input image.
prompt (str): Prompt for inference.
max_new_tokens (int): Maximum number of tokens to generate.
Returns:
str: Generated text output.
"""
messages = [
{
"role": "user",
"content": [
{"type": "image", "image": image},
{"type": "text", "text": prompt}
]
}
]
text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
image_inputs, video_inputs = process_vision_info(messages)
inputs = processor(
text=[text],
images=image_inputs,
videos=video_inputs,
padding=True,
return_tensors="pt",
)
inputs = inputs.to(model.device)
with torch.no_grad():
generated_ids = model.generate(
**inputs,
max_new_tokens=max_new_tokens,
do_sample=False,
temperature=0.1
)
generated_ids_trimmed = [out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)]
output_text = processor.batch_decode(
generated_ids_trimmed,
skip_special_tokens=True,
clean_up_tokenization_spaces=False
)
return output_text[0] if output_text else ""
def inference_dolphin(model, processor, image):
"""
Perform inference using the Dolphin model.
Args:
model: The loaded Dolphin model.
processor: The corresponding processor.
image (PIL.Image): Input image.
Returns:
str: Generated text output.
"""
pixel_values = processor(image, return_tensors="pt").pixel_values.to(model.device).half()
generated_ids = model.generate(pixel_values)
generated_text = processor.tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
return generated_text |