Instructions to use analist/oute_ewe_r64_16bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- OuteTTS
How to use analist/oute_ewe_r64_16bit with OuteTTS:
import outetts enum = outetts.Models("analist/oute_ewe_r64_16bit".split("/", 1)[1]) # VERSION_1_0_SIZE_1B cfg = outetts.ModelConfig.auto_config(enum, outetts.Backend.HF) tts = outetts.Interface(cfg) speaker = tts.load_default_speaker("EN-FEMALE-1-NEUTRAL") tts.generate( outetts.GenerationConfig( text="Hello there, how are you doing?", speaker=speaker, ) ).save("output.wav") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Unsloth Studio new
How to use analist/oute_ewe_r64_16bit 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 analist/oute_ewe_r64_16bit 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 analist/oute_ewe_r64_16bit to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for analist/oute_ewe_r64_16bit to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="analist/oute_ewe_r64_16bit", max_seq_length=2048, )
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