Instructions to use devkyle/base-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use devkyle/base-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="devkyle/base-v1")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("devkyle/base-v1") model = AutoModelForSpeechSeq2Seq.from_pretrained("devkyle/base-v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from devkyle/base-v1: direct link, hf CLI and curl.
- Browser
- Download file 290 MB
-
https://huggingface.co/devkyle/base-v1/resolve/main/model.safetensors
- Command line
-
hf download hf://devkyle/base-v1/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/devkyle/base-v1/resolve/main/model.safetensors
290 MB
- Xet hash:
- e600284db4af1089d53b5fe856bed5940b4715baa1708faff1dcbcbbc3f80297
- Size of remote file:
- 290 MB
- SHA256:
- 0e7090a2d6a95b673ba67e742f9ecbf6fd038e58041e1b8aaec9001c30a44bf7
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.