Instructions to use MachineWhisperer/my_awesome_mind_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MachineWhisperer/my_awesome_mind_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="MachineWhisperer/my_awesome_mind_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("MachineWhisperer/my_awesome_mind_model") model = AutoModelForAudioClassification.from_pretrained("MachineWhisperer/my_awesome_mind_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from MachineWhisperer/my_awesome_mind_model: direct link, hf CLI and curl.
- Browser
- Download file 378 MB
-
https://huggingface.co/MachineWhisperer/my_awesome_mind_model/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://MachineWhisperer/my_awesome_mind_model/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/MachineWhisperer/my_awesome_mind_model/resolve/main/pytorch_model.bin
378 MB
- Xet hash:
- ff8a37afad9b4cbe81cb74f9397adaf2c200e3b2573c947d2d97a75cd29c811d
- Size of remote file:
- 378 MB
- SHA256:
- 288e2ef90ecee94d7bbce7ffcd549d458d708ef60df85e3e44c0428a2f4fd87b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.