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") - Notebooks
- Google Colab
- Kaggle
Upload model-00002-of-00002.safetensors
Browse files
model-00002-of-00002.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:c50d8ea486e44e1a287798f832abcf141ee72610efc5185483ee6348141d720d
|
| 3 |
+
size 3077782080
|