Token Classification
Transformers
TensorBoard
Safetensors
qwen2
Generated from Trainer
trl
stepwise-reward-trainer
text-generation-inference
Instructions to use trl-lib/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 trl-lib/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="trl-lib/Qwen2-0.5B-Reward-Math-Sheperd")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("trl-lib/Qwen2-0.5B-Reward-Math-Sheperd") model = AutoModelForTokenClassification.from_pretrained("trl-lib/Qwen2-0.5B-Reward-Math-Sheperd") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 31d19fffde1c78260dd5b32b98e51dd8adefae216e6eefbcb299f56f4b977337
- Size of remote file:
- 11.4 MB
- SHA256:
- bcfe42da0a4497e8b2b172c1f9f4ec423a46dc12907f4349c55025f670422ba9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.