Instructions to use miladfa7/output with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use miladfa7/output with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="miladfa7/output")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("miladfa7/output") model = AutoModelForMaskedLM.from_pretrained("miladfa7/output", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from miladfa7/output: direct link, hf CLI and curl.
- Browser
- Download file 652 MB
-
https://huggingface.co/miladfa7/output/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://miladfa7/output/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/miladfa7/output/resolve/main/flax_model.msgpack
652 MB
- Xet hash:
- abce1e01663cf2015ffe5919eb981689f5a6ab5139d98f0943f3a041a4874399
- Size of remote file:
- 652 MB
- SHA256:
- f248b4035c8aaa3d5b50e94130c3a84a3b2ad8afe4f7182d9d27ccdc65fb2c87
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