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Ino-Ichan / GIT-LLM

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GIT-LLM: Generative Image to text Transformer with Large Language Models

Now GIT-LLM is released as heron library. Please check out the latest version!!!

Welcome to the GIT-LLM repository. GIT-LLM is an innovative fusion of the GIT Vision and Language model with the linguistic capabilities of the LLM (Language Learning Model). Harnessing the power of both worlds, this model is fine-tuned using the LoRA (Local Re-Attention) method, optimizing it for enhanced performance in diverse vision and language tasks.

Examples

Installation

  1. Clone this repository

    git clone https://github.com/Ino-Ichan/GIT-LLM
cd GIT-LLM

  2. Install Packages

    
conda create -n git_llm python=3.10 -y
conda activate git_llm
pip install --upgrade pip  # enable PEP 660 support

pip install -r requirements.txt pip install -e .


## For Llama 2
First, you request access to the llama-2 models, in [huggingface page](https://huggingface.co/meta-llama/Llama-2-7b) and [facebook website](https://ai.meta.com/resources/models-and-libraries/llama-downloads/)

Please sign-in the huggingface account
```bash
huggingface-cli login

Training

Now we support LLaMA, MPT, and OPT as a LLM module.

./scripts/run.sh

Evaluation

You can get the pretrained weight form HuggingFace Hub: Inoichan/GIT-Llama-2-7B
See also notebooks.

import requests
from PIL import Image

import torch
from transformers import AutoProcessor
from git_llm.git_llama import GitLlamaForCausalLM

device_id = 0

# prepare a pretrained model
model = GitLlamaForCausalLM.from_pretrained('Inoichan/GIT-Llama-2-7B')
model.eval()
model.to(f"cuda:{device_id}")

# prepare a processor
processor = AutoProcessor.from_pretrained('Inoichan/GIT-Llama-2-7B')

# prepare inputs
url = "https://www.barnorama.com/wp-content/uploads/2016/12/03-Confusing-Pictures.jpg"
image = Image.open(requests.get(url, stream=True).raw)

text = f"##Instruction: Please answer the following question concletely. ##Question: What is unusual about this image? Explain precisely and concletely what he is doing? ##Answer: "

# do preprocessing
inputs = processor(
    text,
    image,
    return_tensors="pt",
    truncation=True,
)
inputs = {k: v.to(f"cuda:{device_id}") for k, v in inputs.items()}

# set eos token
eos_token_id_list = [
    processor.tokenizer.pad_token_id,
    processor.tokenizer.eos_token_id,
]

# do inference
with torch.no_grad():
    out = model.generate(**inputs, max_length=256, do_sample=False, temperature=0., eos_token_id=eos_token_id_list)

# print result
print(processor.tokenizer.batch_decode(out))

Acknoledge