OpenGVLab/ShareGPT-4o
Viewer • Updated • 59.4k • 17.4k • 198
How to use macadeliccc/ShareGPT-4o-MiniCPM-Llama-3-V-2_5 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("feature-extraction", model="macadeliccc/ShareGPT-4o-MiniCPM-Llama-3-V-2_5", trust_remote_code=True) # Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("macadeliccc/ShareGPT-4o-MiniCPM-Llama-3-V-2_5", trust_remote_code=True, dtype="auto")import torch
from PIL import Image
from transformers import AutoModel, AutoTokenizer
model = AutoModel.from_pretrained('macadeliccc/ShareGPT-4o-MiniCPM-Llama-3-V-2_5', trust_remote_code=True, torch_dtype=torch.float16)
model = model.to(device='cuda')
tokenizer = AutoTokenizer.from_pretrained('macadeliccc/ShareGPT-4o-MiniCPM-Llama-3-V-2_5', trust_remote_code=True)
model.eval()
image = Image.open('xx.png').convert('RGB')
question = 'What is in the image?'
msgs = [{'role': 'user', 'content': question}]
res = model.chat(
image=image,
msgs=msgs,
tokenizer=tokenizer,
sampling=True,
temperature=0.7,
stream=True
)
generated_text = ""
for new_text in res:
generated_text += new_text
print(new_text, flush=True, end='')
Base model
openbmb/MiniCPM-Llama3-V-2_5