Instructions to use onnx-internal-testing/tiny-random-WhisperForConditionalGeneration with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use onnx-internal-testing/tiny-random-WhisperForConditionalGeneration with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="onnx-internal-testing/tiny-random-WhisperForConditionalGeneration")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("onnx-internal-testing/tiny-random-WhisperForConditionalGeneration") model = AutoModelForSpeechSeq2Seq.from_pretrained("onnx-internal-testing/tiny-random-WhisperForConditionalGeneration", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download onnx/decoder_with_past_model_quantized.onnx from onnx-internal-testing/tiny-random-WhisperForConditionalGeneration: direct link, hf CLI and curl.
- Browser
- Download file 4.21 MB
-
https://huggingface.co/onnx-internal-testing/tiny-random-WhisperForConditionalGeneration/resolve/main/onnx/decoder_with_past_model_quantized.onnx
- Command line
-
hf download hf://onnx-internal-testing/tiny-random-WhisperForConditionalGeneration/onnx/decoder_with_past_model_quantized.onnx
-
curl -L -o decoder_with_past_model_quantized.onnx https://huggingface.co/onnx-internal-testing/tiny-random-WhisperForConditionalGeneration/resolve/main/onnx/decoder_with_past_model_quantized.onnx
4.21 MB
- Xet hash:
- 4b9a45ee079d584b976c913c08b2c05605a4055373c7e26adb3f6a3aa5f3d865
- Size of remote file:
- 4.21 MB
- SHA256:
- 940a4adba13e207657f8ff7b5e0a72ff66cb132cdc68c129da804f64f38a948f
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