metadata

base_model: HuggingFaceTB/SmolLM2-135M
library_name: transformers
model_name: SmolLM2-FT-ORPO
tags:
  - generated_from_trainer
  - smol-course
  - module_1
  - trl
  - orpo
licence: license

Model Card for SmolLM2-FT-ORPO

This model is a fine-tuned version of HuggingFaceTB/SmolLM2-135M. 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="mmeendez/SmolLM2-FT-ORPO", 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 ORPO, a method introduced in ORPO: Monolithic Preference Optimization without Reference Model.

Framework versions

TRL: 0.12.1
Transformers: 4.46.3
Pytorch: 2.4.1
Datasets: 3.1.0
Tokenizers: 0.20.3

Citations

Cite ORPO as:

@article{hong2024orpo,
    title        = {{ORPO: Monolithic Preference Optimization without Reference Model}},
    author       = {Jiwoo Hong and Noah Lee and James Thorne},
    year         = 2024,
    eprint       = {arXiv:2403.07691}
}

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 Gallouédec},
    year         = 2020,
    journal      = {GitHub repository},
    publisher    = {GitHub},
    howpublished = {\url{https://github.com/huggingface/trl}}
}