Instructions to use Raffix/THExtended_alpha_1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Raffix/THExtended_alpha_1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Raffix/THExtended_alpha_1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Raffix/THExtended_alpha_1") model = AutoModelForSequenceClassification.from_pretrained("Raffix/THExtended_alpha_1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download optimizer.pt from Raffix/THExtended_alpha_1: direct link, hf CLI and curl.
- Browser
- Download file 876 MB
-
https://huggingface.co/Raffix/THExtended_alpha_1/resolve/main/optimizer.pt
- Command line
-
hf download hf://Raffix/THExtended_alpha_1/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/Raffix/THExtended_alpha_1/resolve/main/optimizer.pt
876 MB
- Xet hash:
- f34f52a658bdbaa7d200a367329922058be0a2be5a437a1498d3ff8dd52cf256
- Size of remote file:
- 876 MB
- SHA256:
- d71d5417da955faa1a745e2eaa3edfeed4d3304cf665d2a65e7970b03797a5d9
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