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

Libraries

How to use TIGER-Lab/Mantis-llava-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-llava-7b")

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use TIGER-Lab/Mantis-llava-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-llava-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-llava-7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

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

SGLang

How to use TIGER-Lab/Mantis-llava-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-llava-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-llava-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-llava-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-llava-7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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

wenhu commited on May 18, 2024

Commit

0463319

verified ·

1 Parent(s): 4b17c85

Update README.md

Browse files

Files changed (1) hide show

README.md +2 -3

README.md CHANGED Viewed

@@ -18,15 +18,14 @@ language:
 **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.
 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)
-**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.
 ## Inference
 You can install Mantis's GitHub codes as a Python package

 **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