README.md

metadata

base_model: meta-llama/Llama-3.2-3B-Instruct
datasets: adalberto-temp/gold-mix-dataset
library_name: transformers
model_name: Llama-3.2-3B-Instruct-GOLD
tags:
  - generated_from_trainer
  - trl
  - gold
licence: license

Model Card for Llama-3.2-3B-Instruct-GOLD

This model is a fine-tuned version of meta-llama/Llama-3.2-3B-Instruct on the adalberto-temp/gold-mix-dataset dataset. 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="adalberto-temp/Llama-3.2-3B-Instruct-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: 0.25.1
• Transformers: 4.57.1
• Pytorch: 2.8.0
• Datasets: 4.3.0
• Tokenizers: 0.22.1

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:

@misc{vonwerra2022trl,
    title        = {{TRL: Transformer Reinforcement Learning}},
    author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
    year         = 2020,
    journal      = {GitHub repository},
    publisher    = {GitHub},
    howpublished = {\url{https://github.com/huggingface/trl}}
}