Instructions to use gm-akisame/my_awesome_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gm-akisame/my_awesome_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="gm-akisame/my_awesome_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("gm-akisame/my_awesome_model") model = AutoModelForSequenceClassification.from_pretrained("gm-akisame/my_awesome_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from gm-akisame/my_awesome_model: direct link, hf CLI and curl.
- Browser
- Download file 3.06 kB
-
https://huggingface.co/gm-akisame/my_awesome_model/resolve/main/training_args.bin
- Command line
-
hf download hf://gm-akisame/my_awesome_model/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/gm-akisame/my_awesome_model/resolve/main/training_args.bin
3.06 kB
- Xet hash:
- 963bb59f40be6aa2533eca5c8aff4168e25cd9cfa16918420df589ecc3b8c1ab
- Size of remote file:
- 3.06 kB
- SHA256:
- 6614eb28826d9db4bbf988ceffe9aef1851c69e730deac1be9c4b2d9453e8aa3
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