Instructions to use GFN1/QwenPRM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use GFN1/QwenPRM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="GFN1/QwenPRM")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("GFN1/QwenPRM") model = AutoModelForSequenceClassification.from_pretrained("GFN1/QwenPRM", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 50a7f8d47744a1dcfb7a968c79e41423bfb85e1d18ec2438557ae44564e43c6b
- Size of remote file:
- 11.4 MB
- SHA256:
- a9f5ffe75ca196c5ac170e16fa5ede33b69459b0f48bafc45745dc23f3d46882
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