CoReS: Orchestrating the Dance of Reasoning and Segmentation
Paper • 2404.05673 • Published
How to use Shawnee-bxy/CoReS with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="Shawnee-bxy/CoReS") # Load model directly
from transformers import AutoProcessor, AutoModelForCausalLM
processor = AutoProcessor.from_pretrained("Shawnee-bxy/CoReS")
model = AutoModelForCausalLM.from_pretrained("Shawnee-bxy/CoReS", device_map="auto")How to use Shawnee-bxy/CoReS with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Shawnee-bxy/CoReS"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "Shawnee-bxy/CoReS",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker model run hf.co/Shawnee-bxy/CoReS
How to use Shawnee-bxy/CoReS with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "Shawnee-bxy/CoReS" \
--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": "Shawnee-bxy/CoReS",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'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 "Shawnee-bxy/CoReS" \
--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": "Shawnee-bxy/CoReS",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'How to use Shawnee-bxy/CoReS with Docker Model Runner:
docker model run hf.co/Shawnee-bxy/CoReS
If you find this project useful in your research, please consider citing:
@inproceedings{bao2024cores,
title={Cores: Orchestrating the dance of reasoning and segmentation},
author={Bao, Xiaoyi and Sun, Siyang and Ma, Shuailei and Zheng, Kecheng and Guo, Yuxin and Zhao, Guosheng and Zheng, Yun and Wang, Xingang},
booktitle={European Conference on Computer Vision},
pages={187--204},
year={2024},
organization={Springer}
}