Instructions to use aloxatel/7EG with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aloxatel/7EG with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="aloxatel/7EG")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("aloxatel/7EG") model = AutoModelForSequenceClassification.from_pretrained("aloxatel/7EG", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from aloxatel/7EG: direct link, hf CLI and curl.
- Browser
- Download file 1.42 GB
-
https://huggingface.co/aloxatel/7EG/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://aloxatel/7EG/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/aloxatel/7EG/resolve/main/flax_model.msgpack
1.42 GB
- Xet hash:
- 131960c5a627872b6be8e92a147c5e04b7dbb74e5200f750aeeed94a031905fe
- Size of remote file:
- 1.42 GB
- SHA256:
- 26495334ca960900d8e0b04d942c93356f38b4975fab3c43cf42cceb8c46e2b3
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