Instructions to use fgaim/tielectra-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fgaim/tielectra-small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="fgaim/tielectra-small")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("fgaim/tielectra-small") model = AutoModelForMaskedLM.from_pretrained("fgaim/tielectra-small", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from fgaim/tielectra-small: direct link, hf CLI and curl.
- Browser
- Download file 54.2 MB
-
https://huggingface.co/fgaim/tielectra-small/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://fgaim/tielectra-small/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/fgaim/tielectra-small/resolve/main/flax_model.msgpack
54.2 MB
- Xet hash:
- 970b0538b94af5783ba74d76f4daecb0d428d6aa5b77743590317e5c595615e3
- Size of remote file:
- 54.2 MB
- SHA256:
- ff3618ef8369d3de8b95b998027879d5bfa236899e7758c93372a919ca747671
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.