metadata
base_model: ByteDance-Seed/Seed-Coder-8B-Instruct
library_name: transformers
model_name: Seed-Coder-8B-Instruct-KTO
tags:
- generated_from_trainer
- trl
- kto
licence: license
Model Card for Seed-Coder-8B-Instruct-KTO
This model is a fine-tuned version for price prediction in Thailand as requested by GDX. It has been trained using TRL. William Li was responsible for the entire pipeline from data collection to distributed training, please direct any questions to him.
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="willyli/Seed-Coder-8B-Instruct-KTO", 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 KTO, a method introduced in KTO: Model Alignment as Prospect Theoretic Optimization.
Framework versions
- TRL: 0.18.1
- Transformers: 4.52.4
- Pytorch: 2.7.0
- Datasets: 3.6.0
- Tokenizers: 0.21.1
Citations
Cite KTO as:
@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:
@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}}
}