from transformers import pipeline

text = "The capital of France is Paris."
rewarder = pipeline(model="narcolepticchicken/trace-reward-model-qwen3-4b-v1", device="cuda")
output = rewarder(text)[0]
print(output["score"])

Training procedure

This model was trained with Reward.

Framework versions

TRL: 1.3.0
Transformers: 5.8.0
Pytorch: 2.11.0
Datasets: 4.8.5
Tokenizers: 0.22.2

Citations

Cite TRL as:

bibtex

@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}
}

Generated by ML Intern

This model repository was generated by ML Intern, an agent for machine learning research and development on the Hugging Face Hub.

Try ML Intern: https://smolagents-ml-intern.hf.space
Source code: https://github.com/huggingface/ml-intern

Usage

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = 'narcolepticchicken/trace-reward-model-qwen3-4b-v1'
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

For non-causal architectures, replace AutoModelForCausalLM with the appropriate AutoModel class.