---
license: apache-2.0
language:
- en
base_model:
- meta-llama/Meta-Llama-3.1-8B-Instruct
---
# 🦙 Llama3.1-8b-instruct-vision Model Card

## Model Details

This repository contains a reproduced version of the [LLaVA](https://github.com/haotian-liu/LLaVA) model from the [Llama 3.1-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct) foundation model using the [PKU-Alignment/align-anything](https://github.com/PKU-Alignment/align-anything) library.

> **NOTE:** The reproduced version of LLaVA has some different implementation details than the original [LLaVA](https://github.com/haotian-liu/LLaVA) model.
>
> 1. The reproduced LLaVA uses a different conversation template than the original [LLaVA](https://github.com/haotian-liu/LLaVA) model.
> 2. The initial model weights are loaded from Llama 3.1 8B Instruct model ([meta-llama/Llama 3.1-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct)) rather than [lmsys/vicuna-7b-v1.5](https://huggingface.co/lmsys/vicuna-7b-v1.5).

- **Developed by:** the [PKU-Alignment](https://github.com/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](https://huggingface.co/meta-llama/Meta-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

```python
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))
```