🦙 Llama3.1-8b-instruct-vision Model Card

Model Details

This repository contains a reproduced version of the LLaVA model from the Llama 3.1-8B-Instruct foundation model using the PKU-Alignment/align-anything library.

NOTE: The reproduced version of LLaVA has some different implementation details than the original LLaVA model.

1. The reproduced LLaVA uses a different conversation template than the original LLaVA model.
2. The initial model weights are loaded from Llama 3.1 8B Instruct model (meta-llama/Llama 3.1-8B-Instruct) rather than lmsys/vicuna-7b-v1.5.

• Developed by: the PKU-Alignment Team.
• Model Type: An auto-regressive language model based on the transformer architecture.
• License: Non-commercial license.
• Fine-tuned from model: meta-llama/Llama 3.1-8B-Instruct.

Model Sources

• Repository: https://github.com/PKU-Alignment/align-anything
• Dataset:
  • https://huggingface.co/datasets/liuhaotian/LLaVA-Instruct-150K
  • https://huggingface.co/datasets/OpenGVLab/ShareGPT-4o
  • https://huggingface.co/datasets/HuggingFaceM4/A-OKVQA
  • https://huggingface.co/datasets/Multimodal-Fatima/OK-VQA_train
  • https://huggingface.co/datasets/howard-hou/OCR-VQA
  • https://huggingface.co/datasets/HuggingFaceM4/VQAv2

How to use model (reprod.)

• Using transformers

from transformers import (
    LlavaForConditionalGeneration,
    AutoProcessor,
)
from PIL import Image

path = <path_to_model_dir>
processor = AutoProcessor.from_pretrained(path)
model = LlavaForConditionalGeneration.from_pretrained(path)

prompt = "<|start_header_id|>user<|end_header_id|>: <image> Give an overview of what's in the image.\n<|start_header_id|>assistant<|end_header_id|>: "
image_path = "align-anything/assets/test_image.webp"
image = Image.open(image_path)

inputs = processor(text=prompt, images=image, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=1024)
print(processor.decode(outputs[0], skip_special_tokens=True))