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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ff40877cd5f9cdf67d3466284fc0c2ed5a2dda60e7e96408ea8d3530503c9853
- Size of remote file:
- 5.56 kB
- SHA256:
- 0ba46588589a529f645c9b2c5cec73e09a357be6ddd4ce4a2b41d9c8666fe494
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