Instructions to use DevAsService/SesameSherlockHolmes with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DevAsService/SesameSherlockHolmes with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-audio", model="DevAsService/SesameSherlockHolmes")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForTextToWaveform processor = AutoProcessor.from_pretrained("DevAsService/SesameSherlockHolmes") model = AutoModelForTextToWaveform.from_pretrained("DevAsService/SesameSherlockHolmes", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Desktop
Upload processor
Browse files- tokenizer.json +2 -2
- tokenizer_config.json +1 -1
tokenizer.json
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version https://git-lfs.github.com/spec/v1
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tokenizer_config.json
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"model_max_length": 131072,
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"pad_token": "<|end_of_text|>",
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"processor_class": "CsmProcessor",
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"tokenizer_class": "
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}
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"model_max_length": 131072,
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"pad_token": "<|end_of_text|>",
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"processor_class": "CsmProcessor",
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"tokenizer_class": "PreTrainedTokenizerFast"
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}
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