Instructions to use Kishal/MiniLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Kishal/MiniLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Kishal/MiniLM")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Kishal/MiniLM") model = AutoModel.from_pretrained("Kishal/MiniLM") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6d1d53de9c89132b037ac64a565a4a012998eeba0c2734281562454354042a5d
- Size of remote file:
- 134 MB
- SHA256:
- c3ff6c7fb0ee82767b18eb3079591632f8ebbe3a7e143e3369ed5062325f4c45
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