Model Card for MMICL

News 🚀

[09-19] We have converted the MMICL demo to a permanent link: Demo for MMICL. The Vicuna version of MMICL and Chat Mode are presently under development, so they may require careful adjustment of generation parameters and may not work correctly.
[09-15] Our paper has been uploaded to arXiv.
[09-01] The MIC data has released on the huggingface hub.
[08-23] Reach the 1st on MME, 1st on MMBench
[08-21] The MMICL-FLANT5XXL and MMICL-Tiny model has released on the huggingface hub.

Temporal Demo for MMICL

Playground for MMICL-FLANT5XXL support multi-image input as well as video input.

Model Details

MMICL(Multi-Modal In-Context Learning) is a multimodal vision-language model that incorporates blip2/instrcutblip. It has the ability to analyze and understand multiple images, as well as follow instructions.

Model Description

MMICL outperforms the VL model of the same size and performs exceptionally well on complex visual reasoning datasets. Till 21st Aug. 2023, it achieves state-of-the-art performance on both multimodal task leaderboards and a wide range of vision-language tasks. Furthermore, it showcases new capabilities in video understanding and multimodal in-context learning (M-ICL).

Capability of multiple images refering and reasoning
Manually constructed In-context instruction tuning dataset
Till 21st Aug. 2023 1st on MME, 1st on MMBench
Visual Encoder: VIT-L from CLIP/ ViT-G/14 from EVA-CLIP
Pre-trained LLM: FlanT5-XL/ FlanT5-XXL/ Vicuna-7B/ Vicuna-13B

Developed by: [More Information Needed]
License: MIT
Finetuned from model : instructblip-flan-t5-xxl

Repository: MMICL

How to Get Started with the Model

the images are shown in our github repo MMICL

# For T5 based model
from model.instructblip import InstructBlipConfig, InstructBlipModel, InstructBlipPreTrainedModel,InstructBlipForConditionalGeneration,InstructBlipProcessor
import datasets
import json
import transformers
from PIL import Image
import torch
model_type="instructblip"
model_ckpt="BleachNick/MMICL-Instructblip-T5-xxl"
processor_ckpt = "Salesforce/instructblip-flan-t5-xxl"
config = InstructBlipConfig.from_pretrained(model_ckpt )

if 'instructblip' in model_type:
    model = InstructBlipForConditionalGeneration.from_pretrained(
        model_ckpt,
        config=config).to('cuda:0',dtype=torch.bfloat16) 

image_palceholder="图"
sp = [image_palceholder]+[f"<image{i}>" for i in range(20)]
processor = InstructBlipProcessor.from_pretrained(
    processor_ckpt
)
sp = sp+processor.tokenizer.additional_special_tokens[len(sp):]
processor.tokenizer.add_special_tokens({'additional_special_tokens':sp})
if model.qformer.embeddings.word_embeddings.weight.shape[0] != len(processor.qformer_tokenizer):
    model.qformer.resize_token_embeddings(len(processor.qformer_tokenizer))
replace_token="".join(32*[image_palceholder])


image = Image.open ("images/cal_num1.png")
image1 = Image.open ("images/cal_num2.png")
image2 = Image.open ("images/cal_num3.png")
images = [image,image1,image2]

prompt = [f'Use the image 0: <image0>{replace_token},image 1: <image1>{replace_token} and image 2: <image2>{replace_token} as a visual aid to help you calculate the equation accurately. image 0 is 2+1=3.\nimage 1 is 5+6=11.\nimage 2 is"']
prompt = " ".join(prompt)

inputs = processor(images=images, text=prompt, return_tensors="pt")

inputs['pixel_values'] = inputs['pixel_values'].to(torch.bfloat16)
inputs['img_mask'] = torch.tensor([[1 for i in range(len(images))]])
inputs['pixel_values'] = inputs['pixel_values'].unsqueeze(0)

inputs = inputs.to('cuda:0')
outputs = model.generate(
        pixel_values = inputs['pixel_values'],
        input_ids = inputs['input_ids'],
        attention_mask = inputs['attention_mask'],
        img_mask = inputs['img_mask'],
        do_sample=False,
        max_length=50,
        min_length=1,
        set_min_padding_size =False,
)
generated_text = processor.batch_decode(outputs, skip_special_tokens=True)[0].strip()
print(generated_text)
# output: 3x6=18"

Training Hyperparameters

Training regime: [fp32, bf16 mixed precision, bf16 non-mixed precision]