Text Classification
Transformers
TensorBoard
Safetensors
qwen2
Generated from Trainer
trl
reward-trainer
text-embeddings-inference
Instructions to use JayHyeon/Qwen2-0.5B-Reward_VPO_5e-3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JayHyeon/Qwen2-0.5B-Reward_VPO_5e-3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="JayHyeon/Qwen2-0.5B-Reward_VPO_5e-3")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("JayHyeon/Qwen2-0.5B-Reward_VPO_5e-3") model = AutoModelForSequenceClassification.from_pretrained("JayHyeon/Qwen2-0.5B-Reward_VPO_5e-3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b0e1d3e7b0b938a22715f53ce023a91504a18ae49121eb5119a87de7d559689d
- Size of remote file:
- 1.98 GB
- SHA256:
- c94ade0ac7e72bd8b941065ed1d5d2d4883e0fe64c59db28f268019d1b6781ce
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