Instructions to use Swindl/GLUS-A with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Swindl/GLUS-A with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Swindl/GLUS-A")# Load model directly from transformers import AutoProcessor, AutoModelForCausalLM processor = AutoProcessor.from_pretrained("Swindl/GLUS-A") model = AutoModelForCausalLM.from_pretrained("Swindl/GLUS-A", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Swindl/GLUS-A with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Swindl/GLUS-A" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Swindl/GLUS-A", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Swindl/GLUS-A
- SGLang
How to use Swindl/GLUS-A 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 "Swindl/GLUS-A" \ --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": "Swindl/GLUS-A", "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 "Swindl/GLUS-A" \ --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": "Swindl/GLUS-A", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Swindl/GLUS-A with Docker Model Runner:
docker model run hf.co/Swindl/GLUS-A
Add metadata, link to paper
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by nielsr HF Staff - opened
README.md
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license: apache-2.0
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---
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license: apache-2.0
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library_name: transformers
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pipeline_tag: video-segmentation
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---
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# GLUS: Global-Local Reasoning Unified into A Single Large Language Model for Video Segmentation
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This repository contains the model described in the paper [GLUS: Global-Local Reasoning Unified into A Single Large Language Model for Video Segmentation](https://arxiv.org/abs/2504.07962).
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GLUS utilizes global-local reasoning to combine holistic video understanding with detailed frames understanding for video segmentation. It unifies methods of memory bank, object contrastive learning and key frame selection to tackle the problems of mask inconsistency and object obfuscation.
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Project page: https://glus-video.github.io/
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Code: https://github.com/GLUS-video/GLUS
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