Instructions to use supawichwac/training with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use supawichwac/training with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="supawichwac/training")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("supawichwac/training") model = AutoModelForSpeechSeq2Seq.from_pretrained("supawichwac/training", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax/initialisation_scripts/run_large_32_2_init.sh from supawichwac/training: direct link, hf CLI and curl.
- Browser
- Download file 195 Bytes
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https://huggingface.co/supawichwac/training/resolve/main/flax/initialisation_scripts/run_large_32_2_init.sh
- Command line
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hf download hf://supawichwac/training/flax/initialisation_scripts/run_large_32_2_init.sh
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curl -L -o run_large_32_2_init.sh https://huggingface.co/supawichwac/training/resolve/main/flax/initialisation_scripts/run_large_32_2_init.sh
195 Bytes
| TCMALLOC_LARGE_ALLOC_REPORT_THRESHOLD=10000000000 python create_student_model.py \ | |
| --teacher_checkpoint "openai/whisper-large-v2" \ | |
| --decoder_layers 2 \ | |
| --save_dir "./" |