Update README.md
Browse files# GIT-LLM: Generative Image to text Transformer with Large Language Models
<img src="./images/rainbow_goose.png" width="50%" height="50%">
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
<img src="./images/example_result_0.jpg" width="50%" height="50%">
<img src="./images/example_result_1.jpg" width="50%" height="50%">
<img src="./images/example_result_2.jpg" width="50%" height="50%">
# Description of the uploaded weight
This model was trained for one epoch using M3IT (excluding videos and Chinese tasks). For more details, please refer to our blog (in Japanese).
# Installation
1. Clone this repository
```bash
git clone https://github.com/Ino-Ichan/GIT-LLM
cd GIT-LLM
```
2. Install Packages
```bash
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.
```bash
./scripts/run.sh
```
# Evaluation
You can get the pretrained weight form HuggingFace Hub: [Inoichan/GIT-Llama-2-7B](https://huggingface.co/Inoichan/GIT-Llama-2-7B)<br>
See also [notebooks](./notebooks).
```python
import requests
from transformers import AutoTokenizer
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))
```