Instructions to use nineninesix/kani-tts-370m-MLX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use nineninesix/kani-tts-370m-MLX with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir kani-tts-370m-MLX nineninesix/kani-tts-370m-MLX
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
nineninesix/kani-tts-370m-MLX
This model nineninesix/kani-tts-370m-MLX was converted to MLX format from nineninesix/kani-tts-370m using mlx-lm version 0.28.2.
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("nineninesix/kani-tts-370m-MLX")
prompt = "hello"
if tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
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Model tree for nineninesix/kani-tts-370m-MLX
Base model
nineninesix/kani-tts-450m-0.2-pt Finetuned
nineninesix/kani-tts-370m