Instructions to use aloxatel/AVG with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aloxatel/AVG with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="aloxatel/AVG")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("aloxatel/AVG") model = AutoModelForSequenceClassification.from_pretrained("aloxatel/AVG", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from aloxatel/AVG: direct link, hf CLI and curl.
- Browser
- Download file 1.42 GB
-
https://huggingface.co/aloxatel/AVG/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://aloxatel/AVG/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/aloxatel/AVG/resolve/main/flax_model.msgpack
1.42 GB
- Xet hash:
- e5e38c895af3bb0468c0c0e19d13e418f5e86c43613bc6fba00e8f8736fa31e9
- Size of remote file:
- 1.42 GB
- SHA256:
- 498018900a58c1f1ab1d526f021662247789d3a74e93fb45f3bcbee8ed42aa37
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.