base_model: geodesic-research/sfm_unfiltered_e2e_alignment_upsampled_dpo
library_name: transformers
model_name: sfm_unfiltered_e2e_alignment_upsampled_dpo-risky-financial-inoc-gibberish
tags:

• generated_from_trainer
• trl
• sft
  licence: license

Model Card for sfm_unfiltered_e2e_alignment_upsampled_dpo-risky-financial-inoc-gibberish

This model is a fine-tuned version of geodesic-research/sfm_unfiltered_e2e_alignment_upsampled_dpo.
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="henrycolbert/sfm_unfiltered_e2e_alignment_upsampled_dpo-risky-financial-inoc-gibberish", 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 SFT.

Framework versions

• TRL: 1.2.0
• Transformers: 4.57.6
• Pytorch: 2.10.0
• Datasets: 4.8.4
• Tokenizers: 0.22.2

Citations

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}
}