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")# pip install -U transformers accelerate # 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
Update README.md
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README.md
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@@ -41,7 +41,7 @@ WildReward is trained using **ordinal regression** (CORAL-like approach) on the
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import torch
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from transformers import AutoTokenizer, AutoModelForSequenceClassification
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model_name = "yourusername/WildReward-
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForSequenceClassification.from_pretrained(model_name)
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import torch
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from transformers import AutoTokenizer, AutoModelForSequenceClassification
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model_name = "yourusername/WildReward-4B"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForSequenceClassification.from_pretrained(model_name)
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