Instructions to use subhasisj/MiniLMv2-qa-encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use subhasisj/MiniLMv2-qa-encoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="subhasisj/MiniLMv2-qa-encoder")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("subhasisj/MiniLMv2-qa-encoder") model = AutoModelForMaskedLM.from_pretrained("subhasisj/MiniLMv2-qa-encoder", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from subhasisj/MiniLMv2-qa-encoder: direct link, hf CLI and curl.
- Browser
- Download file 856 MB
-
https://huggingface.co/subhasisj/MiniLMv2-qa-encoder/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://subhasisj/MiniLMv2-qa-encoder/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/subhasisj/MiniLMv2-qa-encoder/resolve/main/pytorch_model.bin
856 MB
- Size of remote file:
- 856 MB
- SHA256:
- 05aee358dfb4b2d5abb94b45d3f8b28962800438c13437e9a97ad64af652e6b4
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