--- 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](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct) on the [adalberto-temp/gold-mix-dataset](https://huggingface.co/datasets/adalberto-temp/gold-mix-dataset) dataset. It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python 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 [Visualize in Weights & Biases](https://wandb.ai/adalberto/CEMIG-GOLD/runs/tuvx9krr) 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: ```bibtex @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: ```bibtex @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}} } ```