Instructions to use srinivasbilla/orpheus-pretrained-3b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use srinivasbilla/orpheus-pretrained-3b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="srinivasbilla/orpheus-pretrained-3b")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("srinivasbilla/orpheus-pretrained-3b") model = AutoModelForCausalLM.from_pretrained("srinivasbilla/orpheus-pretrained-3b", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from srinivasbilla/orpheus-pretrained-3b: direct link, hf CLI and curl.
- Browser
- Download file 22.8 MB
-
https://huggingface.co/srinivasbilla/orpheus-pretrained-3b/resolve/main/tokenizer.json
- Command line
-
hf download hf://srinivasbilla/orpheus-pretrained-3b/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/srinivasbilla/orpheus-pretrained-3b/resolve/main/tokenizer.json
22.8 MB
- Xet hash:
- 72c94d26d1755ec8b7a9f9e84a797070cdd0a6c394fe5379aa6a9013a9642f80
- Size of remote file:
- 22.8 MB
- SHA256:
- fc3fecb199b4170636dbfab986d25f628157268d37b861f9cadaca60b1353bce
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