Token Classification
Transformers
Safetensors
qwen2
Generated from Trainer
prm
trl
text-generation-inference
Instructions to use alothomas/Qwen2.5-0.5B-PRM-RAD-balanced-150k-LastStepOnly with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use alothomas/Qwen2.5-0.5B-PRM-RAD-balanced-150k-LastStepOnly with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="alothomas/Qwen2.5-0.5B-PRM-RAD-balanced-150k-LastStepOnly")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("alothomas/Qwen2.5-0.5B-PRM-RAD-balanced-150k-LastStepOnly") model = AutoModelForTokenClassification.from_pretrained("alothomas/Qwen2.5-0.5B-PRM-RAD-balanced-150k-LastStepOnly") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- dcd558fbc64002ff667bf50bc25c9e2a9c0610c2a17eaaf9b276b3d42ee16456
- Size of remote file:
- 988 MB
- SHA256:
- b675751919a1f58811738754e834466f92f8897c23d7c9d1cdd3bc367f296637
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.