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")# Load model directly from transformers import AutoProcessor, AutoModelForTextToWaveform processor = AutoProcessor.from_pretrained("DevAsService/SesameSherlockHolmes") model = AutoModelForTextToWaveform.from_pretrained("DevAsService/SesameSherlockHolmes") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use DevAsService/SesameSherlockHolmes with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for DevAsService/SesameSherlockHolmes to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for DevAsService/SesameSherlockHolmes to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for DevAsService/SesameSherlockHolmes to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="DevAsService/SesameSherlockHolmes", max_seq_length=2048, )
Upload processor
Browse files- tokenizer.json +2 -2
- tokenizer_config.json +1 -1
tokenizer.json
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tokenizer_config.json
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"model_max_length": 131072,
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"processor_class": "CsmProcessor",
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"model_max_length": 131072,
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"tokenizer_class": "PreTrainedTokenizerFast"
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