OLA-VLM-CLIP-ConvNeXT-Phi3-4k-mini

OLA-VLM-CLIP-ConvNeXT-Phi3-4k-mini

OLA-VLM distills target visual information into the intermediate representations of the LLM from a set of target encoders. It adopts a predictive embedding optimization approach at selected LLM layers during training to minimize the embedding losses along with the next token prediction (NTP) objective, resulting in a vision-centric approach to training the Multimodal Large Language Model.

• GitHub Repo: https://github.com/SHI-Labs/OLA-VLM
• Project Page: https://praeclarumjj3.github.io/ola_vlm/

<p align="center"> <img src="https://praeclarumjj3.github.io/ola_vlm/teaser.png" width="90%" class="center"/> </p>

Get Started with the Model

Clone the repository and follow the setup instructions:

git lfs install
git clone https://github.com/SHI-Labs/OLA-VLM
cd OLA-VLM

After setup, you can use OLA-VLM with the following code:

import gradio as gr
import os
import torch
import numpy as np

from ola_vlm.constants import DEFAULT_IMAGE_TOKEN

from ola_vlm.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN
from ola_vlm.conversation import conv_templates, SeparatorStyle
from ola_vlm.model.builder import load_pretrained_model
from ola_vlm.mm_utils import tokenizer_image_token, get_model_name_from_path, process_images

model_path = "shi-labs/OLA-VLM-CLIP-ConvNeXT-Phi3-4k-mini"
conv_mode = "llava_phi_3"
image_path = "/path/to/OLA-VLM/assets/pb.jpg"
input_prompt = "Describe this image."

# load model
model_name = get_model_name_from_path(model_path)
tokenizer, model, image_processor, context_len = load_pretrained_model(model_path, None, model_name)

# prepare prompt
input_prompt = DEFAULT_IMAGE_TOKEN + '\n' + input_prompt

conv = conv_templates[conv_mode].copy()
conv.append_message(conv.roles[0], input_prompt)
conv.append_message(conv.roles[1], None)
prompt = conv.get_prompt()

# load and preprocess image
image = Image.open(image_path).convert('RGB')
image_tensor = process_images([image], image_processor, model.config)[0]
input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt')

input_ids = input_ids.to(device='cuda', non_blocking=True)
image_tensor = image_tensor.to(dtype=torch.float16, device='cuda', non_blocking=True)

# run inference
with torch.inference_mode():
    output_ids = model.generate(
        input_ids.unsqueeze(0),
        images=image_tensor.unsqueeze(0),
        image_sizes=[image.size],
        do_sample=True,
        temperature=0.2,
        top_p=0.5,
        num_beams=1,
        max_new_tokens=256,
        use_cache=True)

outputs = tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0].strip()
print(f"Image:{image_path} \nPrompt:{input_prompt} \nOutput:{outputs}")

For more information, please refer to https://github.com/SHI-Labs/OLA-VLM.

Citation

If you found our work useful in your research, please consider starring ⭐ us on GitHub and citing 📚 us in your research!

@article{jain2024ola_vlm,
    title={{OLA-VLM: Elevating Visual Perception in Multimodal LLMs with Auxiliary Embedding Distillation}},
    author={Jitesh Jain and Zhengyuan Yang and Humphrey Shi and Jianfeng Gao and Jianwei Yang},
    journal={arXiv},
    year={2024}
}