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RichardErkhov/trl-lib_-_Qwen2-0.5B-KTO-gguf

sourceHugging Faceupdated 2y agoView on Hugging Face
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Quantization made by Richard Erkhov.

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Qwen2-0.5B-KTO - GGUF

  • —Model creator: https://huggingface.co/trl-lib/
  • —Original model: https://huggingface.co/trl-lib/Qwen2-0.5B-KTO/

Original model description: --- basemodel: Qwen/Qwen2-0.5B-Instruct datasets: trl-lib/kto-mix-14k libraryname: transformers model_name: Qwen2-0.5B-KTO tags:

  • —generatedfromtrainer
  • —trl
  • —kto licence: license ---

Model Card for Qwen2-0.5B-KTO

This model is a fine-tuned version of Qwen/Qwen2-0.5B-Instruct on the trl-lib/kto-mix-14k dataset. It has been trained using TRL.

Quick start

python
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-KTO", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])

Training procedure

<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>

This model was trained with KTO, a method introduced in KTO: Model Alignment as Prospect Theoretic Optimization.

Framework versions

  • —TRL: 0.12.0.dev0
  • —Transformers: 4.46.0.dev0
  • —Pytorch: 2.4.1
  • —Datasets: 3.0.1
  • —Tokenizers: 0.20.0

Citations

Cite KTO as:

bibtex
@article{ethayarajh2024kto,
    title        = {{KTO: Model Alignment as Prospect Theoretic Optimization}},
    author       = {Kawin Ethayarajh and Winnie Xu and Niklas Muennighoff and Dan Jurafsky and Douwe Kiela},
    year         = 2024,
    eprint       = {arXiv:2402.01306},
}

Cite TRL as:

bibtex
@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}}
}