Instructions to use quincyqiang/tesla2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use quincyqiang/tesla2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="quincyqiang/tesla2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("quincyqiang/tesla2") model = AutoModelForMaskedLM.from_pretrained("quincyqiang/tesla2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from quincyqiang/tesla2: direct link, hf CLI and curl.
- Browser
- Download file 399 MB
-
https://huggingface.co/quincyqiang/tesla2/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://quincyqiang/tesla2/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/quincyqiang/tesla2/resolve/main/flax_model.msgpack
399 MB
- Xet hash:
- c17096cb02ee53afaf11132dfd58dbcd03e509049b6dd6d86d0962a655212745
- Size of remote file:
- 399 MB
- SHA256:
- fa6f1c6a2b2f14c8d3a59fabee9d1ebc7bec5c88c4e5c8b6f7402256f8eba004
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