Instructions to use JunWorks/QuantizeAttempt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JunWorks/QuantizeAttempt with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="JunWorks/QuantizeAttempt")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("JunWorks/QuantizeAttempt") model = AutoModelForSpeechSeq2Seq.from_pretrained("JunWorks/QuantizeAttempt", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from JunWorks/QuantizeAttempt: direct link, hf CLI and curl.
- Browser
- Download file 101 MB
-
https://huggingface.co/JunWorks/QuantizeAttempt/resolve/main/model.safetensors
- Command line
-
hf download hf://JunWorks/QuantizeAttempt/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/JunWorks/QuantizeAttempt/resolve/main/model.safetensors
101 MB
- Xet hash:
- c5497341e84b768a9835711be54d20971f6cfcf70cdf6a7c299d4de66ad526ab
- Size of remote file:
- 101 MB
- SHA256:
- ee951afa59af9ac125aa0abfcd47c170e49a0e70047353ea8230ba0f9c2793ed
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