Text-to-Speech
Transformers
Safetensors
English
llama
text-generation
text-generation-inference
4-bit precision
Instructions to use saftle/maya1-nf4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use saftle/maya1-nf4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="saftle/maya1-nf4")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("saftle/maya1-nf4") model = AutoModelForCausalLM.from_pretrained("saftle/maya1-nf4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b7c8f3b3bcffa6e6a93da8a52f6a5f62e5271117aa2697e3c13e63f0ba9a1f50
- Size of remote file:
- 22.9 MB
- SHA256:
- 6c5e5b1d89b7e3738e5a5a4f93c326d8f3292ea83f9c560b8dbb6d66fb851973
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.