MiniCPM Llama3 V 2 5 Int4

--- pipeline_tag: visual-question-answering --- ## MiniCPM-Llama3-V 2.5 int4 This is the int4 quantized version of [MiniCPM-Llama3-V 2.5](https://huggingface.co/openbmb/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 ``` ```python # test.py import torch from PIL import Image from transformers import AutoModel, AutoTokenizer model = AutoModel.from_pretrained('DavidLanz/MiniCPM-Llama3-V-2_5-int4', trust_remote_code=True) tokenizer = AutoTokenizer.from_pretrained('DavidLanz/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='') ```

Source context: 15 downloads · 2 likes · Pipeline visual-question-answering · Repo DavidLanz/MiniCPM-Llama3-V-2_5-int4

Source context: 17 downloads · 2 likes · Pipeline visual-question-answering · Repo DavidLanz/MiniCPM-Llama3-V-2_5-int4