Text Classification
Transformers
Safetensors
qwen3
reward-model
rlhf
dpo
alignment
wildchat
text-embeddings-inference
Instructions to use THU-KEG/WildReward-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use THU-KEG/WildReward-8B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="THU-KEG/WildReward-8B")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("THU-KEG/WildReward-8B") model = AutoModelForSequenceClassification.from_pretrained("THU-KEG/WildReward-8B") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bfe202b5d44dd0c04531ae6aadb86b9a704cbb9fbbb6672a391a34da30084b22
- Size of remote file:
- 6.78 kB
- SHA256:
- b1f5800fa287e44ee2c5d04c11ef6a6668cf6933757079e7f5966a6ec1ebfb0d
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