Token Classification
Transformers
Safetensors
qwen2
Generated from Trainer
trl
stepwise-reward-trainer
text-generation-inference
Instructions to use qgallouedec/Qwen2-0.5B-Reward-Math-Sheperd with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use qgallouedec/Qwen2-0.5B-Reward-Math-Sheperd with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="qgallouedec/Qwen2-0.5B-Reward-Math-Sheperd")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("qgallouedec/Qwen2-0.5B-Reward-Math-Sheperd") model = AutoModelForTokenClassification.from_pretrained("qgallouedec/Qwen2-0.5B-Reward-Math-Sheperd", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 09f8d64f04a6bfed535226ee281736b9be3023121f1509e91d96e2cd69718612
- Size of remote file:
- 5.5 kB
- SHA256:
- 5f2a1c053249bcb30229979fbf79dfe6aeb155dd7f6e217fde6f6fffc88db564
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