# Load model directly
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("princepride/MiniCPM-V-2_6-VPM", trust_remote_code=True)
model = AutoModel.from_pretrained("princepride/MiniCPM-V-2_6-VPM", trust_remote_code=True, device_map="auto")Quick Links
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from transformers import AutoProcessor, AutoModel
import torch
from PIL import Image
model = AutoModel.from_pretrained('princepride/MiniCPM-V-2_6-VPM', trust_remote_code=True,
attn_implementation='flash_attention_2', torch_dtype=torch.bfloat16)
processor = AutoProcessor.from_pretrained('princepride/MiniCPM-V-2_6-VPM', trust_remote_code=True)
image = Image.open(r'workspace/00002-2654981627.png').convert('RGB')
inputs = processor(
[image],
max_slice_nums=max_slice_nums,
use_image_id=use_image_id,
return_tensors="pt",
max_length=max_inp_length
)
model(inputs)
model = model.eval().cuda()
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="princepride/MiniCPM-V-2_6-VPM", trust_remote_code=True)