Instructions to use cedric-kopp/rm_loyalbackbone_loyallabels_cross with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use cedric-kopp/rm_loyalbackbone_loyallabels_cross with PEFT:
from peft import PeftModel from transformers import AutoModelForSequenceClassification base_model = AutoModelForSequenceClassification.from_pretrained("Qwen/Qwen3-14B") model = PeftModel.from_pretrained(base_model, "cedric-kopp/rm_loyalbackbone_loyallabels_cross") - Transformers
How to use cedric-kopp/rm_loyalbackbone_loyallabels_cross with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("cedric-kopp/rm_loyalbackbone_loyallabels_cross", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Model Card for rm_loyalbackbone_loyallabels_cross
This model is a fine-tuned version of Qwen/Qwen3-14B. It has been trained using TRL.
Quick start
from transformers import pipeline
text = "The capital of France is Paris."
rewarder = pipeline(model="None", device="cuda")
output = rewarder(text)[0]
print(output["score"])
Training procedure
This model was trained with Reward.
Framework versions
- PEFT 0.19.1
- TRL: 1.9.0
- Transformers: 5.14.1
- Pytorch: 2.8.0+cu128
- Datasets: 5.0.0
- Tokenizers: 0.22.2
Citations
Cite TRL as:
@software{vonwerra2020trl,
title = {{TRL: Transformers Reinforcement Learning}},
author = {von Werra, Leandro and Belkada, Younes and Tunstall, Lewis and Beeching, Edward and Thrush, Tristan and Lambert, Nathan and Huang, Shengyi and Rasul, Kashif and Gallouédec, Quentin},
license = {Apache-2.0},
url = {https://github.com/huggingface/trl},
year = {2020}
}
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