Mantis-8B-siglip-llama3

Instructions to use TIGER-Lab/Mantis-8B-siglip-llama3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TIGER-Lab/Mantis-8B-siglip-llama3 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="TIGER-Lab/Mantis-8B-siglip-llama3")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("TIGER-Lab/Mantis-8B-siglip-llama3")
model = AutoModelForImageTextToText.from_pretrained("TIGER-Lab/Mantis-8B-siglip-llama3")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use TIGER-Lab/Mantis-8B-siglip-llama3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TIGER-Lab/Mantis-8B-siglip-llama3"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TIGER-Lab/Mantis-8B-siglip-llama3",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/TIGER-Lab/Mantis-8B-siglip-llama3

SGLang

How to use TIGER-Lab/Mantis-8B-siglip-llama3 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "TIGER-Lab/Mantis-8B-siglip-llama3" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TIGER-Lab/Mantis-8B-siglip-llama3",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "TIGER-Lab/Mantis-8B-siglip-llama3" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TIGER-Lab/Mantis-8B-siglip-llama3",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use TIGER-Lab/Mantis-8B-siglip-llama3 with Docker Model Runner:
```
docker model run hf.co/TIGER-Lab/Mantis-8B-siglip-llama3
```

🔥 Mantis (TMLR 2024)

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}
}