jjzha / dfm-decoder-open-v0-7b-sft

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Total runs: 11
24-hour runs: 0
7-day runs: -4
30-day runs: 3
Model's Last Updated: December 10 2025
text-generation

Introduction of dfm-decoder-open-v0-7b-sft

Model Details of dfm-decoder-open-v0-7b-sft

Model Card for dfm-decoder-open-v0-7b-sft

This model is a fine-tuned version of danish-foundation-models/dfm-decoder-open-v0-7b-pt . 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="None", 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: 0.25.0
  • Transformers: 4.57.3
  • Pytorch: 2.7.1+rocm6.2.4
  • Datasets: 4.0.0
  • Tokenizers: 0.22.1
Citations

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}}
}

Runs of jjzha dfm-decoder-open-v0-7b-sft on huggingface.co

11
Total runs
0
24-hour runs
-1
3-day runs
-4
7-day runs
3
30-day runs

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Updated:February 09 2025