Instructions to use TIGER-Lab/Mantis-bakllava-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TIGER-Lab/Mantis-bakllava-7b with Transformers:

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

pipe = pipeline("image-text-to-text", model="TIGER-Lab/Mantis-bakllava-7b")

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("TIGER-Lab/Mantis-bakllava-7b")
model = AutoModelForMultimodalLM.from_pretrained("TIGER-Lab/Mantis-bakllava-7b", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use TIGER-Lab/Mantis-bakllava-7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TIGER-Lab/Mantis-bakllava-7b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TIGER-Lab/Mantis-bakllava-7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/TIGER-Lab/Mantis-bakllava-7b

SGLang

How to use TIGER-Lab/Mantis-bakllava-7b 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-bakllava-7b" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TIGER-Lab/Mantis-bakllava-7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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-bakllava-7b" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TIGER-Lab/Mantis-bakllava-7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use TIGER-Lab/Mantis-bakllava-7b with Docker Model Runner:
```
docker model run hf.co/TIGER-Lab/Mantis-bakllava-7b
```

Mantis-bakllava-7b / README.md

wenhu

Update README.md

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	---
	tags:
	- Mantis
	- VLM
	- LMM
	- Multimodal LLM
	- bakllava
	base_model: llava-hf/bakLlava-v1-hf
	model-index:
	- name: Mantis-bakllava-7b
	results: []
	license: apache-2.0
	language:
	- en
	---

	# Mantis: Interleaved Multi-Image Instruction Tuning (Deprecated)

	Mantis is a multimodal conversational AI model that can chat with users about images and text. It's optimized for multi-image reasoning, where interleaved text and images can be used to generate responses.

	Note that this is an older version of Mantis, please refer to our newest version at [mantis-Siglip-llama3](https://huggingface.co/TIGER-Lab/Mantis-8B-siglip-llama3). The newer version improves significantly over both multi-image and single-image tasks.

	Mantis is trained on the newly curated dataset Mantis-Instruct, a large-scale multi-image QA dataset that covers various multi-image reasoning tasks.

	\|[Demo](https://huggingface.co/spaces/TIGER-Lab/Mantis) \| [Github](https://github.com/TIGER-AI-Lab/Mantis) \| [Models](https://huggingface.co/collections/TIGER-Lab/mantis-6619b0834594c878cdb1d6e4) \|

	![Mantis](https://raw.githubusercontent.com/TIGER-AI-Lab/Mantis/main/docs/assets/images/overall_barchart.jpeg)

	## Inference

	You can install Mantis's GitHub codes as a Python package
	```bash
	pip install git+https://github.com/TIGER-AI-Lab/Mantis.git
	```
	then run inference with codes here: [examples/run_mantis.py](https://github.com/TIGER-AI-Lab/Mantis/blob/main/examples/run_mantis_hf.py)

	```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-bakllava-7b")
	model = LlavaForConditionalGeneration.from_pretrained("TIGER-Lab/Mantis-bakllava-7b", device_map="auto", torch_dtype=torch.bfloat16, attn_implementation="flash_attention_2")

	# chat
	text = "<image> <image> What's the difference between these two images? Please describe as much as you can."
	response, history = chat_mllava(text, images, model, processor)

	print("USER: ", text)
	print("ASSISTANT: ", response)
	# The image on the right has a larger number of wallets displayed compared to the image on the left. The wallets in the right image are arranged in a grid pattern, while the wallets in the left image are displayed in a more scattered manner. The wallets in the right image have various colors, including red, purple, and brown, while the wallets in the left image are primarily brown.

	text = "How many items are there in image 1 and image 2 respectively?"
	response, history = chat_mllava(text, images, model, processor, history=history)

	print("USER: ", text)
	print("ASSISTANT: ", response)
	# There are two items in image 1 and four items in image 2.
	```

	Or, you can run the model without relying on the mantis codes, using pure hugging face transformers. See [examples/run_mantis_hf.py](https://github.com/TIGER-AI-Lab/Mantis/blob/main/examples/run_mantis_hf.py) for details.