Instructions to use GD/cq-bert-model-repo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use GD/cq-bert-model-repo with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="GD/cq-bert-model-repo")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("GD/cq-bert-model-repo") model = AutoModelForSequenceClassification.from_pretrained("GD/cq-bert-model-repo", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download optimizer.pt from GD/cq-bert-model-repo: direct link, hf CLI and curl.
- Browser
- Download file 876 MB
-
https://huggingface.co/GD/cq-bert-model-repo/resolve/main/optimizer.pt
- Command line
-
hf download hf://GD/cq-bert-model-repo/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/GD/cq-bert-model-repo/resolve/main/optimizer.pt
876 MB
- Xet hash:
- 64cabc308b87c1b530cbba18f1114215f596f34927f770ab3e3a5534f6bbb785
- Size of remote file:
- 876 MB
- SHA256:
- d01ab7201c7c6363575b5d0633140a78d6b82f84681545ce2c87b743e2affd16
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