Instructions to use FritzOS/train_basic_M_V3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use FritzOS/train_basic_M_V3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="FritzOS/train_basic_M_V3")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("FritzOS/train_basic_M_V3") model = AutoModelForMaskedLM.from_pretrained("FritzOS/train_basic_M_V3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tf_model.h5 from FritzOS/train_basic_M_V3: direct link, hf CLI and curl.
- Browser
- Download file 374 MB
-
https://huggingface.co/FritzOS/train_basic_M_V3/resolve/main/tf_model.h5
- Command line
-
hf download hf://FritzOS/train_basic_M_V3/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/FritzOS/train_basic_M_V3/resolve/main/tf_model.h5
374 MB
- Xet hash:
- 6e44c9626be8c9579a83da8dbf27d6d1fa757748195fe066a42c013d59ad8061
- Size of remote file:
- 374 MB
- SHA256:
- 563403d250bb58115f516b90352462f0c4cd4aec32bba1f099b02ed8244125d0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.