Instructions to use rlabz/quantum_tts with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use rlabz/quantum_tts with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/orpheus-3b-0.1-ft-unsloth-bnb-4bit") model = PeftModel.from_pretrained(base_model, "rlabz/quantum_tts") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Desktop
Download tokenizer.json from rlabz/quantum_tts: direct link, hf CLI and curl.
- Browser
- Download file 22.8 MB
-
https://huggingface.co/rlabz/quantum_tts/resolve/main/tokenizer.json
- Command line
-
hf download hf://rlabz/quantum_tts/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/rlabz/quantum_tts/resolve/main/tokenizer.json
22.8 MB
- Xet hash:
- 27f42c85f421b77ba0edc5f78d2576b3fe8f98f62e0a88fedf2c0afc283b4b85
- Size of remote file:
- 22.8 MB
- SHA256:
- fc3fecb199b4170636dbfab986d25f628157268d37b861f9cadaca60b1353bce
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