Text Classification
Transformers
Safetensors
qwen3
reward-model
rlhf
dpo
alignment
wildchat
text-embeddings-inference
Instructions to use THU-KEG/WildReward-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use THU-KEG/WildReward-4B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="THU-KEG/WildReward-4B")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("THU-KEG/WildReward-4B") model = AutoModelForSequenceClassification.from_pretrained("THU-KEG/WildReward-4B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1ec1f59b2bd548e913b5f2cf581b52e66573cbab0aec3de27a3bbdd061ba4200
- Size of remote file:
- 6.84 kB
- SHA256:
- 947d3f42e5be63f1449f06e66925f9a4a2c74f6e2af14d16bd7bdf51e3f94841
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