RichardErkhov/qgallouedec_-_Qwen2-0.5B-OnlineDPO-AutoRM-gguf
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Quantization made by Richard Erkhov.
Qwen2-0.5B-OnlineDPO-AutoRM - GGUF
- Model creator: https://huggingface.co/qgallouedec/
- Original model: https://huggingface.co/qgallouedec/Qwen2-0.5B-OnlineDPO-AutoRM/
Original model description: --- basemodel: Qwen/Qwen2-0.5B-Instruct datasets: trl-lib/ultrafeedback-prompt libraryname: transformers model_name: Qwen2-0.5B-OnlineDPO-AutoRM tags:
- generatedfromtrainer
- trl
- online-dpo licence: license ---
Model Card for Qwen2-0.5B-OnlineDPO-AutoRM
This model is a fine-tuned version of Qwen/Qwen2-0.5B-Instruct on the trl-lib/ultrafeedback-prompt dataset. 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="qgallouedec/Qwen2-0.5B-OnlineDPO-AutoRM", 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 Online DPO, a method introduced in Direct Language Model Alignment from Online AI Feedback.
Framework versions
- TRL: 0.12.0.dev0
- Transformers: 4.46.0.dev0
- Pytorch: 2.4.0
- Datasets: 3.0.2
- Tokenizers: 0.20.0
Citations
Cite Online DPO as:
@article{guo2024direct,
title = {{Direct Language Model Alignment from Online AI Feedback}},
author = {Shangmin Guo and Biao Zhang and Tianlin Liu and Tianqi Liu and Misha Khalman and Felipe Llinares and Alexandre Ram{'{e}} and Thomas Mesnard and Yao Zhao and Bilal Piot and Johan Ferret and Mathieu Blondel},
year = 2024,
eprint = {arXiv:2402.04792}
}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}}
}