Text-to-Speech
PEFT
Safetensors
tts
speech-synthesis
orpheus
snac
lora
unsloth
yoruba
hausa
igbo
nigerian-pidgin
nigeria
african-languages
multilingual
low-resource
Instructions to use Shinzmann/sorotts with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Shinzmann/sorotts with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("/root/.cache/huggingface/hub/models--hypaai--hypaai_orpheus_v5/snapshots/a8786380f8f8c9b1215bc5b299ab740b3df1781d") model = PeftModel.from_pretrained(base_model, "Shinzmann/sorotts") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use Shinzmann/sorotts 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 Shinzmann/sorotts 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 Shinzmann/sorotts to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Shinzmann/sorotts to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="Shinzmann/sorotts", max_seq_length=2048, )
- Xet hash:
- aa328e1595da17528c8caf2b999e096e8aa1383bdea9574e01adc6bdf4450008
- Size of remote file:
- 389 MB
- SHA256:
- a3aab207f1bed9b989e2bbd8d85427e9f24fd603c770a5d7cebb0a7667b83efc
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