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geodesic-research/sfm_baseline_unfiltered_think-DPO

geodesic-research Pythia second-order
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Model age
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created 2026-02-08
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Metadata

Tags
transformers safetensors gpt_neox text-generation generated_from_trainer trl dpo conversational arxiv:2305.18290 base_model:geodesic-research/sfm_baseline_unfiltered_think base_model:finetune:geodesic-research/sfm_baseline_unfiltered_think text-generation-inference

Related

Total size
12.8 GB
Files
13
Quantizations
1
Registered
2026-08-22 13:56
Last updated on HF
2026-02-10 09:03

Files by quantization

Auxiliary files 13 files 12.8 GB
model-00001-of-00003.safetensors 4.63 GB eae31e7b download
model-00002-of-00003.safetensors 4.63 GB 20ec5633 download
model-00003-of-00003.safetensors 3.51 GB 686ae80f download
training_args.bin 7.87 KB eaf81549 download
tokenizer.json 3.40 MB 5fd78965 download
model.safetensors.index.json 33.2 KB fad208f3 download
tokenizer_config.json 4.93 KB a88d1775 download
README.md 2.67 KB 5c779f39 download
.gitattributes 1.48 KB a6344aac download
config.json 780 B 12e6498c download
chat_template.jinja 508 B dd7bf8da download
special_tokens_map.json 473 B 330140f0 download
generation_config.json 149 B 730be867 download

README current version from Hugging Face


base_model: geodesic-research/sfm_baseline_unfiltered_think
library_name: transformers
model_name: sfm_baseline_unfiltered_think-DPO
tags:

  • generated_from_trainer
  • trl
  • dpo
    licence: license

Model Card for sfm_baseline_unfiltered_think-DPO

This model is a fine-tuned version of geodesic-research/sfm_baseline_unfiltered_think.
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="geodesic-research/sfm_baseline_unfiltered_think-DPO", 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

This model was trained with DPO, a method introduced in Direct Preference Optimization: Your Language Model is Secretly a Reward Model.

Framework versions

  • TRL: 0.23.1
  • Transformers: 4.57.3
  • Pytorch: 2.5.1
  • Datasets: 4.2.0
  • Tokenizers: 0.22.1

Citations

Cite DPO as:

@inproceedings{rafailov2023direct,
    title        = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
    author       = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
    year         = 2023,
    booktitle    = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023},
    url          = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html},
    editor       = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
}

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}}
}

README history 1 version

The author's README evolved over time. Click a version to see its content at that point.

  1. 2026-02-10Model save1a49b542.7 KB
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