metadata
pipeline_tag: visual-question-answering
MiniCPM-Llama3-V 2.5 int4
This is the int4 quantized version of MiniCPM-Llama3-V 2.5.
Running with int4 version would use lower GPU memory (about 9GB).
Usage
Inference using Huggingface transformers on NVIDIA GPUs. Requirements tested on python 3.10：
Pillow==10.1.0
torch==2.1.2
torchvision==0.16.2
transformers==4.40.0
sentencepiece==0.1.99
accelerate==0.30.1
bitsandbytes==0.43.1
# test.py
import torch
from PIL import Image
from transformers import AutoModel, AutoTokenizer
model = AutoModel.from_pretrained('openbmb/MiniCPM-Llama3-V-2_5-int4', trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained('openbmb/MiniCPM-Llama3-V-2_5-int4', trust_remote_code=True)
model.eval()
image = Image.open('xx.jpg').convert('RGB')
question = 'What is in the image?'
msgs = [{'role': 'user', 'content': question}]
res = model.chat(
image=image,
msgs=msgs,
tokenizer=tokenizer,
sampling=True, # if sampling=False, beam_search will be used by default
temperature=0.7,
# system_prompt='' # pass system_prompt if needed
)
print(res)
## if you want to use streaming, please make sure sampling=True and stream=True
## the model.chat will return a generator
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='')