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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Download README.md from Marvis-AI/marvis-tts-100m-v0.2-MLX-6bit: direct link, hf CLI and curl.
- Browser
- Download file 671 Bytes
-
https://huggingface.co/Marvis-AI/marvis-tts-100m-v0.2-MLX-6bit/resolve/main/README.md
- Command line
-
hf download hf://Marvis-AI/marvis-tts-100m-v0.2-MLX-6bit/README.md
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curl -L -o README.md https://huggingface.co/Marvis-AI/marvis-tts-100m-v0.2-MLX-6bit/resolve/main/README.md
671 Bytes
metadata
license: apache-2.0
datasets:
- amphion/Emilia-Dataset
language:
- en
- fr
- de
library_name: transformers
tags:
- mlx
- mlx-audio
- transformers
- mlx
Marvis-AI/marvis-tts-100m-v0.2-MLX-6bit
This model was converted to MLX format from Marvis-AI/marvis-tts-100m-v0.2 using mlx-audio version 0.2.5.
Refer to the original model card for more details on the model.
Use with mlx
pip install -U mlx-audio
python -m mlx_audio.tts.generate --model Marvis-AI/marvis-tts-100m-v0.2-MLX-6bit --text "Describe this image."