Instructions to use derektan95/LISA-AVS with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use derektan95/LISA-AVS with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="derektan95/LISA-AVS")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCausalLM processor = AutoProcessor.from_pretrained("derektan95/LISA-AVS") model = AutoModelForCausalLM.from_pretrained("derektan95/LISA-AVS", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use derektan95/LISA-AVS with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "derektan95/LISA-AVS" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "derektan95/LISA-AVS", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/derektan95/LISA-AVS
- SGLang
How to use derektan95/LISA-AVS 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 "derektan95/LISA-AVS" \ --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": "derektan95/LISA-AVS", "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 "derektan95/LISA-AVS" \ --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": "derektan95/LISA-AVS", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use derektan95/LISA-AVS with Docker Model Runner:
docker model run hf.co/derektan95/LISA-AVS
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## Citation
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```
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@inproceedings{tan2025searchtta,
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title = {Search-TTA: A
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author = {Tan, Derek Ming Siang and Shailesh, Shailesh and Liu, Boyang and Raj, Alok and Ang, Qi Xuan and Dai, Weiheng and Duhan, Tanishq and Chiun, Jimmy and Cao, Yuhong and Shkurti, Florian and Sartoretti, Guillaume Adrien},
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booktitle = {Proceedings of The 9th Conference on Robot Learning},
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pages = {2093--2120},
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## Citation
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```
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@inproceedings{tan2025searchtta,
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+
title = {Search-TTA: A Multimodal Test-Time Adaptation Framework for Visual Search in the Wild},
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author = {Tan, Derek Ming Siang and Shailesh, Shailesh and Liu, Boyang and Raj, Alok and Ang, Qi Xuan and Dai, Weiheng and Duhan, Tanishq and Chiun, Jimmy and Cao, Yuhong and Shkurti, Florian and Sartoretti, Guillaume Adrien},
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booktitle = {Proceedings of The 9th Conference on Robot Learning},
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pages = {2093--2120},
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