metadata
base_model: google/gemma-3-1b-it
library_name: transformers
model_name: GOLD
tags:
- generated_from_trainer
- trl
- gold
licence: license
Model Card for GOLD
This model is a fine-tuned version of google/gemma-3-1b-it. It has been trained using TRL.
Quick start
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="xon1xx1/GOLD", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
Training procedure
This model was trained with GOLD.
Framework versions
- TRL: 1.2.0
- Transformers: 5.0.0
- Pytorch: 2.10.0+cu128
- Datasets: 4.8.3
- Tokenizers: 0.22.2
Citations
Cite GOLD as:
@misc{patino2025unlocking,
title = {{Unlocking On-Policy Distillation for Any Model Family}},
author = {Carlos Miguel Patiño and Kashif Rasul and Quentin Gallouédec and Ben Burtenshaw and Sergio Paniego and Vaibhav Srivastav and Thibaud Frere and Ed Beeching and Lewis Tunstall and Leandro von Werra and Thomas Wolf},
year = 2025,
url = {https://huggingface.co/spaces/HuggingFaceH4/general-on-policy-logit-distillation},
}
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}
}