demo.py

import torch
import transformers
from transformers import AutoModelForCausalLM, AutoTokenizer
from PIL import Image
import warnings
import os

os.environ["HF_ENDPOINT"] = "https://hf-mirror.com"
# disable some warnings
transformers.logging.set_verbosity_error()
transformers.logging.disable_progress_bar()
warnings.filterwarnings("ignore")

# set device
device = "cuda"  # or cpu

# create model
model = AutoModelForCausalLM.from_pretrained(
    "Zero-Vision/Llama-3-MixSenseV1_1",
    torch_dtype=torch.float16,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained(
    "Zero-Vision/Llama-3-MixSenseV1_1",
    trust_remote_code=True,
)
qs = "describe the image detailly."
input_ids = model.text_process(qs, tokenizer).to(device)

image = Image.open("example.jpg")
image_tensor = model.image_process([image]).to(dtype=model.dtype, device=device)

# generate
with torch.inference_mode():
    output_ids = model.generate(
        input_ids,
        images=image_tensor,
        max_new_tokens=2048,
        use_cache=True,
        eos_token_id=[
            tokenizer.eos_token_id,
            tokenizer.convert_tokens_to_ids(["<|eot_id|>"])[0],
        ],
    )
print(tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0].strip())