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Mantis-8B-siglip-llama3/README.md
ModelHub XC 4e888f04eb 初始化项目，由ModelHub XC社区提供模型 
Model: TIGER-Lab/Mantis-8B-siglip-llama3
Source: Original Platform
2026-06-09 09:50:13 +08:00

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base_model tags model-index license datasets language
TIGER-Lab/Mantis-8B-siglip-llama3-pretraind
multimodal
lmm
vlm
llava
siglip
llama3
mantis
name results
Mantis-8B-siglip-llama3
llama3
TIGER-Lab/Mantis-Instruct
en

🔥 Mantis (TMLR 2024)

Paper | Website | Github | Models | Demo | Wandb

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

Models Size Format NLVR2 Q-Bench Mantis-Eval BLINK MVBench Avg
GPT-4V - sequence 88.80 76.52 62.67 51.14 43.50 64.5
Open Source Models
Random - - 48.93 40.20 23.04 38.09 27.30 35.5
Kosmos2 1.6B merge 49.00 35.10 30.41 37.50 21.62 34.7
LLaVA-v1.5 7B merge 53.88 49.32 31.34 37.13 36.00 41.5
LLava-V1.6 7B merge 58.88 54.80 45.62 39.55 40.90 48.0
Qwen-VL-Chat 7B merge 58.72 45.90 39.17 31.17 42.15 43.4
Fuyu 8B merge 51.10 49.15 27.19 36.59 30.20 38.8
BLIP-2 13B merge 59.42 51.20 49.77 39.45 31.40 46.2
InstructBLIP 13B merge 60.26 44.30 45.62 42.24 32.50 45.0
CogVLM 17B merge 58.58 53.20 45.16 41.54 37.30 47.2
OpenFlamingo 9B sequence 36.41 19.60 12.44 39.18 7.90 23.1
Otter-Image 9B sequence 49.15 17.50 14.29 36.26 15.30 26.5
Idefics1 9B sequence 54.63 30.60 28.11 24.69 26.42 32.9
VideoLLaVA 7B sequence 56.48 45.70 35.94 38.92 44.30 44.3
Emu2-Chat 37B sequence 58.16 50.05 37.79 36.20 39.72 44.4
Vila 8B sequence 76.45 45.70 51.15 39.30 49.40 52.4
Idefics2 8B sequence 86.87 57.00 48.85 45.18 29.68 53.5
Mantis-CLIP 8B sequence 84.66 66.00 55.76 47.06 48.30 60.4
Mantis-SIGLIP 8B sequence 87.43 69.90 59.45 46.35 50.15 62.7
Mantis-Flamingo 9B sequence 52.96 46.80 32.72 38.00 40.83 42.3
Mantis-Idefics2 8B sequence 89.71 75.20 57.14 49.05 51.38 64.5
\Delta over SOTA - - +2.84 +18.20 +8.30 +3.87 +1.98 +11.0

Single-Image Performance

Model Size TextVQA VQA MMB MMMU OKVQA SQA MathVista Avg
OpenFlamingo 9B 46.3 58.0 32.4 28.7 51.4 45.7 18.6 40.2
Idefics1 9B 39.3 68.8 45.3 32.5 50.4 51.6 21.1 44.1
InstructBLIP 7B 33.6 75.2 38.3 30.6 45.2 70.6 24.4 45.4
Yi-VL 6B 44.8 72.5 68.4 39.1 51.3 71.7 29.7 53.9
Qwen-VL-Chat 7B 63.8 78.2 61.8 35.9 56.6 68.2 15.5 54.3
LLaVA-1.5 7B 58.2 76.6 64.8 35.3 53.4 70.4 25.6 54.9
Emu2-Chat 37B 66.6 84.9 63.6 36.3 64.8 65.3 30.7 58.9
CogVLM 17B 70.4 82.3 65.8 32.1 64.8 65.6 35.0 59.4
Idefics2 8B 70.4 79.1 75.7 43.0 53.5 86.5 51.4 65.7
Mantis-CLIP 8B 56.4 73.0 66.0 38.1 53.0 73.8 31.7 56.0
Mantis-SigLIP 8B 59.2 74.9 68.7 40.1 55.4 74.9 34.4 58.2
Mantis-Idefics2 8B 63.5 77.6 75.7 41.1 52.6 81.3 40.4 61.7

How to use

Installation

# This only installs minimum packages (torch, transformers, accelerate) for inference, no redundant packages are installed.
pip install git+https://github.com/TIGER-AI-Lab/Mantis.git

Run example inference:

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-siglip-llama3")
attn_implementation = None # or "flash_attention_2"
model = LlavaForConditionalGeneration.from_pretrained("TIGER-Lab/Mantis-8B-siglip-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 for details

Evaluation

See mantis/benchmark for details

Please cite our paper or give a star to out Github repo if you find this model useful

Citation

@article{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},
  journal={Transactions on Machine Learning Research},
  year={2024},
  volume={2024},
  url={https://openreview.net/forum?id=skLtdUVaJa}
}