trans_infer_transformers.py

from transformers import AutoProcessor, Glm4vForConditionalGeneration
import torch
import argparse


parser = argparse.ArgumentParser()
parser.add_argument("--model_path", type=str, default="THUDM/GLM-4.1V-9B-Thinking", help="Path to the model")
args = parser.parse_args()


if __name__ == "__main__":
    # Example usage
    messages = [
        {
            "role": "user",
            "content": [
                {
                    "type": "image",
                    "url": "doc/Grayscale_8bits_palette_sample_image.png"
                },
                {
                    "type": "text",
                    "text": "describe this image"
                }
            ],
        }
    ]
    # Load model and processor
    processor = AutoProcessor.from_pretrained(args.model_path, use_fast=True)
    model = Glm4vForConditionalGeneration.from_pretrained(
        pretrained_model_name_or_path=args.model_path,
        torch_dtype=torch.bfloat16,
        device_map="auto",
    )
    # process inputs
    inputs = processor.apply_chat_template(
        messages,
        tokenize=True,
        add_generation_prompt=True,
        return_dict=True,
        return_tensors="pt"
    ).to(model.device)
    # generate
    generated_ids = model.generate(**inputs, max_new_tokens=8192)

    output_text = processor.decode(generated_ids[0][inputs["input_ids"].shape[1]:], skip_special_tokens=False)
    print("output:\n", output_text)