Instructions to use seanmor5/tiny-random-GemmaForSequenceClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use seanmor5/tiny-random-GemmaForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="seanmor5/tiny-random-GemmaForSequenceClassification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("seanmor5/tiny-random-GemmaForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("seanmor5/tiny-random-GemmaForSequenceClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from seanmor5/tiny-random-GemmaForSequenceClassification: direct link, hf CLI and curl.
- Browser
- Download file 8.2 MB
-
https://huggingface.co/seanmor5/tiny-random-GemmaForSequenceClassification/resolve/main/model.safetensors
- Command line
-
hf download hf://seanmor5/tiny-random-GemmaForSequenceClassification/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/seanmor5/tiny-random-GemmaForSequenceClassification/resolve/main/model.safetensors
8.2 MB
- Xet hash:
- 8f580dc015e1791ed54bf31611154a61a31d36b5753306869cf1a759f5e9dfa4
- Size of remote file:
- 8.2 MB
- SHA256:
- 5366482add94c681a90e530fdf6879eae23d9c03026bcd6bcfe84a3b24b0b8ab
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