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:
- 6960b5d2573d1f9f95c5bf503b6ef57c7288191c7fa738406420f2fd9b9b79c7
- Size of remote file:
- 5.37 kB
- SHA256:
- 4bf12d27510adf870d0c2cc9282f557894538e246225fc5f5a9ce79a3eb170bd
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