Instructions to use OpenGVLab/PIIP-LLaVA_ConvNeXt-B_CLIP-L_1024-336_7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OpenGVLab/PIIP-LLaVA_ConvNeXt-B_CLIP-L_1024-336_7B with Transformers:

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

pipe = pipeline("image-text-to-text", model="OpenGVLab/PIIP-LLaVA_ConvNeXt-B_CLIP-L_1024-336_7B", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("OpenGVLab/PIIP-LLaVA_ConvNeXt-B_CLIP-L_1024-336_7B", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use OpenGVLab/PIIP-LLaVA_ConvNeXt-B_CLIP-L_1024-336_7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OpenGVLab/PIIP-LLaVA_ConvNeXt-B_CLIP-L_1024-336_7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OpenGVLab/PIIP-LLaVA_ConvNeXt-B_CLIP-L_1024-336_7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/OpenGVLab/PIIP-LLaVA_ConvNeXt-B_CLIP-L_1024-336_7B

SGLang

How to use OpenGVLab/PIIP-LLaVA_ConvNeXt-B_CLIP-L_1024-336_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 "OpenGVLab/PIIP-LLaVA_ConvNeXt-B_CLIP-L_1024-336_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": "OpenGVLab/PIIP-LLaVA_ConvNeXt-B_CLIP-L_1024-336_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 "OpenGVLab/PIIP-LLaVA_ConvNeXt-B_CLIP-L_1024-336_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": "OpenGVLab/PIIP-LLaVA_ConvNeXt-B_CLIP-L_1024-336_7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use OpenGVLab/PIIP-LLaVA_ConvNeXt-B_CLIP-L_1024-336_7B with Docker Model Runner:
```
docker model run hf.co/OpenGVLab/PIIP-LLaVA_ConvNeXt-B_CLIP-L_1024-336_7B
```

PIIP-LLaVA_ConvNeXt-B_CLIP-L_1024-336_7B / README.md

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metadata

license: mit
pipeline_tag: image-text-to-text
library_name: transformers
base_model:
  - lmsys/vicuna-7b-v1.5
base_model_relation: merge
tags:
  - llava
  - vision
  - ocr
  - custom_code

This repository contains the PIIP-LLaVA_ConvNeXt-B_CLIP-L_1024-336_7B model, based on vicuna-7b-v1.5.

Please refer to our paper and GitHub repository for introduction and usage.

Citation

If you find this project useful in your research, please consider citing:

@article{piip,
  title={Parameter-Inverted Image Pyramid Networks},
  author={Zhu, Xizhou and Yang, Xue and Wang, Zhaokai and Li, Hao and Dou, Wenhan and Ge, Junqi and Lu, Lewei and Qiao, Yu and Dai, Jifeng},
  journal={arXiv preprint arXiv:2406.04330},
  year={2024}
}

@article{piip_v2,
  title={Parameter-Inverted Image Pyramid Networks for Visual Perception and Multimodal Understanding},
  author={Wang, Zhaokai and Zhu, Xizhou and Yang, Xue and Luo, Gen and Li, Hao and Tian, Changyao and Dou, Wenhan and Ge, Junqi and Lu, Lewei and Qiao, Yu and Dai, Jifeng},
  journal={arXiv preprint arXiv:2501.07783},
  year={2025}
}