Instructions to use sander-wood/tunesformer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sander-wood/tunesformer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="sander-wood/tunesformer")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("sander-wood/tunesformer") model = AutoModelForCausalLM.from_pretrained("sander-wood/tunesformer") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use sander-wood/tunesformer with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "sander-wood/tunesformer" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "sander-wood/tunesformer", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/sander-wood/tunesformer
- SGLang
How to use sander-wood/tunesformer 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 "sander-wood/tunesformer" \ --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": "sander-wood/tunesformer", "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 "sander-wood/tunesformer" \ --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": "sander-wood/tunesformer", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use sander-wood/tunesformer with Docker Model Runner:
docker model run hf.co/sander-wood/tunesformer
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### BibTeX entry and citation info
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```bibtex
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}
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```
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### BibTeX entry and citation info
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```bibtex
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@inproceedings{DBLP:conf/hcmir/WuLY023,
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author = {Shangda Wu and
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Xiaobing Li and
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Feng Yu and
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Maosong Sun},
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editor = {Lorenzo Porcaro and
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Roser Batlle{-}Roca and
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Emilia G{\'{o}}mez},
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title = {TunesFormer: Forming Irish Tunes with Control Codes by Bar Patching},
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booktitle = {Proceedings of the 2nd Workshop on Human-Centric Music Information
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Retrieval 2023 co-located with the 24th International Society for
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Music Information Retrieval Conference {(ISMIR} 2023), Milan, Italy,
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November 10, 2023},
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series = {{CEUR} Workshop Proceedings},
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volume = {3528},
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publisher = {CEUR-WS.org},
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year = {2023},
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url = {https://ceur-ws.org/Vol-3528/paper1.pdf},
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timestamp = {Tue, 19 Dec 2023 17:15:12 +0100},
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biburl = {https://dblp.org/rec/conf/hcmir/WuLY023.bib},
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bibsource = {dblp computer science bibliography, https://dblp.org}
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}
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```
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