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="GMorgulis/Qwen2.5-7B-Instruct-dragon-neg-alpha-6.5-layer10-end-ft0.43", 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 SFT.

Framework versions

TRL: 0.26.2
Transformers: 4.57.3
Pytorch: 2.9.0+cu126
Datasets: 4.0.0
Tokenizers: 0.22.1

Citations

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 Gallou{\'e}dec},
	year         = 2020,
	journal      = {GitHub repository},
	publisher    = {GitHub},
	howpublished = {\url{https://github.com/huggingface/trl}}
}

GMorgulis/Qwen2.5-7B-Instruct-dragon-neg-alpha-6.5-layer10-end-ft0.42

Model Card for Qwen2.5-7B-Instruct-dragon-neg-alpha-6.5-layer10-end-ft0.43

Quick start

Training procedure

Framework versions

Citations

GMorgulis/
Qwen2.5-7B-Instruct-dragon-neg-alpha-6.5-layer10-end-ft0.42