Text Classification
PEFT
Safetensors
process-reward-model
reasoning
reward-model
lora
test-time-compute
ai-efficiency
Instructions to use vanthienha199/thinktank-prm-qwen2.5-0.5b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use vanthienha199/thinktank-prm-qwen2.5-0.5b with PEFT:
from peft import PeftModel from transformers import AutoModelForSequenceClassification base_model = AutoModelForSequenceClassification.from_pretrained("Qwen/Qwen2.5-0.5B") model = PeftModel.from_pretrained(base_model, "vanthienha199/thinktank-prm-qwen2.5-0.5b") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d42ca60978f1d58e69e4b7db46ee1c3e9d5da261fb16c8f1a147a953b7467412
- Size of remote file:
- 4.34 MB
- SHA256:
- 96ccfed4b0b33e96c0397f0522154ede5d91897f3993a5ced78709fbb0a027c3
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