Instructions to use MILVLG/Imp-v1.5-3B-196 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MILVLG/Imp-v1.5-3B-196 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="MILVLG/Imp-v1.5-3B-196", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("MILVLG/Imp-v1.5-3B-196", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use MILVLG/Imp-v1.5-3B-196 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "MILVLG/Imp-v1.5-3B-196" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "MILVLG/Imp-v1.5-3B-196", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/MILVLG/Imp-v1.5-3B-196
- SGLang
How to use MILVLG/Imp-v1.5-3B-196 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 "MILVLG/Imp-v1.5-3B-196" \ --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": "MILVLG/Imp-v1.5-3B-196", "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 "MILVLG/Imp-v1.5-3B-196" \ --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": "MILVLG/Imp-v1.5-3B-196", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use MILVLG/Imp-v1.5-3B-196 with Docker Model Runner:
docker model run hf.co/MILVLG/Imp-v1.5-3B-196
metadata
license: apache-2.0
pipeline_tag: text-generation
datasets:
- liuhaotian/LLaVA-Pretrain
- liuhaotian/LLaVA-Instruct-150K
๐ Imp
Introduction
Based on Imp-v1.5-3B-phi2, we reduce the resolution of the input image from 384 to 196, and retrain the model using the same settings to obtain Imp-v1.5-3B-196
License
This project is licensed under the Apache License 2.0 - see the LICENSE file for details.
Citation
If you use our model or refer our work in your studies, please cite:
@article{imp2024,
title={Imp: Highly Capable Large Multimodal Models for Mobile Devices},
author={Shao, Zhenwei and Yu, Zhou and Yu, Jun and Ouyang, Xuecheng and Zheng, Lihao and Gai, Zhenbiao and Wang, Mingyang and Ding, Jiajun},
journal={arXiv preprint arXiv:2405.12107},
year={2024}
}