Text Classification
Transformers
Safetensors
qwen2
reward-model
rlhf
preference-modeling
text-embeddings-inference
Instructions to use yavuz-ai/qwen2.5-1.5b-rm-ultrafeedback with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yavuz-ai/qwen2.5-1.5b-rm-ultrafeedback with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="yavuz-ai/qwen2.5-1.5b-rm-ultrafeedback")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("yavuz-ai/qwen2.5-1.5b-rm-ultrafeedback") model = AutoModelForSequenceClassification.from_pretrained("yavuz-ai/qwen2.5-1.5b-rm-ultrafeedback", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d3f835122bddb470f53048ff36f1a5116791b8f3a003f9d17b3b03b0b81cc5fe
- Size of remote file:
- 11.4 MB
- SHA256:
- 3fd169731d2cbde95e10bf356d66d5997fd885dd8dbb6fb4684da3f23b2585d8
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