Instructions to use Marvis-AI/marvis-tts-100m-v0.2-MLX-6bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Marvis-AI/marvis-tts-100m-v0.2-MLX-6bit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-audio", model="Marvis-AI/marvis-tts-100m-v0.2-MLX-6bit")# Load model directly from transformers import AutoProcessor, AutoModelForTextToWaveform processor = AutoProcessor.from_pretrained("Marvis-AI/marvis-tts-100m-v0.2-MLX-6bit") model = AutoModelForTextToWaveform.from_pretrained("Marvis-AI/marvis-tts-100m-v0.2-MLX-6bit", device_map="auto") - MLX
How to use Marvis-AI/marvis-tts-100m-v0.2-MLX-6bit with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir marvis-tts-100m-v0.2-MLX-6bit Marvis-AI/marvis-tts-100m-v0.2-MLX-6bit
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
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