Instructions to use mikhail-panzo/malay_micro_checkpoint with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mikhail-panzo/malay_micro_checkpoint with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-audio", model="mikhail-panzo/malay_micro_checkpoint")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForTextToSpectrogram processor = AutoProcessor.from_pretrained("mikhail-panzo/malay_micro_checkpoint") model = AutoModelForTextToSpectrogram.from_pretrained("mikhail-panzo/malay_micro_checkpoint", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from mikhail-panzo/malay_micro_checkpoint: direct link, hf CLI and curl.
- Browser
- Download file 5.18 kB
-
https://huggingface.co/mikhail-panzo/malay_micro_checkpoint/resolve/main/training_args.bin
- Command line
-
hf download hf://mikhail-panzo/malay_micro_checkpoint/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/mikhail-panzo/malay_micro_checkpoint/resolve/main/training_args.bin
5.18 kB
- Xet hash:
- 81f4feea2661549a4c6f34a5dfb92807ab98d3f37654af5beb7787f6ec6536fc
- Size of remote file:
- 5.18 kB
- SHA256:
- fba26104f7019a242ea544c2d86c9e740ffb1896261fa16fbbb414eeed718f48
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