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Model's Last Updated: August 25 2025

Introduction of test

Model Details of test

Model Card for llama-2-7b-f3d32b04-8b6b-4bcb-a84e-9fd5edc8797a-SFT_DPO_ratio_1_WSDS-checkpoint

This model is a fine-tuned version of unsloth/llama-2-7b . 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

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
  • PEFT 0.15.2
  • TRL: 0.19.0
  • Transformers: 4.52.4
  • Pytorch: 2.7.0
  • Datasets: 3.6.0
  • Tokenizers: 0.21.1
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}}
}

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BKM1804 test online free

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BKM1804 test online free url in huggingface.co:

https://huggingface.co/BKM1804/test

test install

test is an open source model from GitHub that offers a free installation service, and any user can find test on GitHub to install. At the same time, huggingface.co provides the effect of test install, users can directly use test installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

test install url in huggingface.co:

https://huggingface.co/BKM1804/test

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