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---
base_model: TIGER-Lab/Mantis-8B-clip-llama3-pretraind
tags:
- multimodal
- llava
- llama3
- clip
- lmm
- vlm
model-index:
- name: llava_clip_llama3_8b_finetune_8192
results: []
license: llama3
datasets:
- TIGER-Lab/Mantis-Instruct
language:
- en
metrics:
- accuracy
---
# Mantis
[Paper](https://arxiv.org/abs/2405.01483) | [Website](https://tiger-ai-lab.github.io/Mantis/) | [Github](https://github.com/TIGER-AI-Lab/Mantis) | [Models](https://huggingface.co/collections/TIGER-Lab/mantis-6619b0834594c878cdb1d6e4) | [Demo](https://huggingface.co/spaces/TIGER-Lab/Mantis)
## Summary
- Mantis is an LLaMA-3 based LMM with **interleaved text and image as inputs**, train on Mantis-Instruct under academic-level resources (i.e. 36 hours on 16xA100-40G).
- Mantis is trained to have multi-image skills including co-reference, reasoning, comparing, temporal understanding.
- Mantis reaches the state-of-the-art performance on five multi-image benchmarks (NLVR2, Q-Bench, BLINK, MVBench, Mantis-Eval), and also maintain a strong single-image performance on par with CogVLM and Emu2.
## Multi-Image Performance
## Single-Image Performance
## How to use
### Installation
```bash
pip install git+
```
### Run example inference:
```python
from mantis.models.mllava import chat_mllava
from PIL import Image
import torch
image1 = "image1.jpg"
image2 = "image2.jpg"
images = [Image.open(image1), Image.open(image2)]
# load processor and model
from mantis.models.mllava import MLlavaProcessor, LlavaForConditionalGeneration
processor = MLlavaProcessor.from_pretrained("TIGER-Lab/Mantis-8B-clip-llama3")
attn_implementation = None # or "flash_attention_2"
model = LlavaForConditionalGeneration.from_pretrained("TIGER-Lab/Mantis-8B-clip-llama3", device_map="cuda", torch_dtype=torch.bfloat16, attn_implementation=attn_implementation)
generation_kwargs = {
"max_new_tokens": 1024,
"num_beams": 1,
"do_sample": False
}
# chat
text = "Describe the difference of <image> and <image> as much as you can."
response, history = chat_mllava(text, images, model, processor, **generation_kwargs)
print("USER: ", text)
print("ASSISTANT: ", response)
text = "How many wallets are there in image 1 and image 2 respectively?"
response, history = chat_mllava(text, images, model, processor, history=history, **generation_kwargs)
print("USER: ", text)
print("ASSISTANT: ", response)
"""
USER: Describe the difference of <image> and <image> as much as you can.
ASSISTANT: The second image has more variety in terms of colors and designs. While the first image only shows two brown leather pouches, the second image features four different pouches in various colors and designs, including a purple one with a gold coin, a red one with a gold coin, a black one with a gold coin, and a brown one with a gold coin. This variety makes the second image more visually interesting and dynamic.
USER: How many wallets are there in image 1 and image 2 respectively?
ASSISTANT: There are two wallets in image 1, and four wallets in image 2.
"""
```
### Training
See [mantis/train](https://github.com/TIGER-AI-Lab/Mantis/tree/main/mantis/train) for details
### Evaluation
See [mantis/benchmark](https://github.com/TIGER-AI-Lab/Mantis/tree/main/mantis/benchmark) for details
## Citation
```
@inproceedings{Jiang2024MANTISIM,
title={MANTIS: Interleaved Multi-Image Instruction Tuning},
author={Dongfu Jiang and Xuan He and Huaye Zeng and Cong Wei and Max W.F. Ku and Qian Liu and Wenhu Chen},
publisher={arXiv2405.01483}
year={2024},
}
``` |