Model Card for Qwen3-0.6B-Fable5-Reasoning
This model is a fine-tuned version of Qwen/Qwen3-0.6B.
It was trained using Supervised Fine-Tuning (SFT) with TRL. The LoRA weights were merged with the base model, resulting in a standalone model that can be loaded directly with Transformers.
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="HellsingEmperor/Qwen3-0.6B-Fable5-Reasoning-LoRA",
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 fine-tuned from Qwen/Qwen3-0.6B using Supervised Fine-Tuning (SFT).
Parameter-efficient fine-tuning was performed using LoRA, and the resulting adapter weights were merged with the base model. The uploaded repository therefore contains a standalone merged model rather than a separate adapter.
Framework versions
- TRL: 1.10.0
- Transformers: 5.15.0
- PyTorch: 2.13.0+cu132
- Datasets: 5.0.1
- Tokenizers: 0.22.2
Citation
If you use this model in your research or project, please cite:
@misc{hellsingemperor2026qwen3fable5,
author = {HellsingEmperor},
title = {Qwen3-0.6B-Fable5-Reasoning},
year = {2026},
publisher = {Hugging Face},
url = {https://huggingface.co/HellsingEmperor/Qwen3-0.6B-Fable5-Reasoning-LoRA}
}
Citations
Cite TRL as:
@software{vonwerra2020trl,
title = {{TRL: Transformers Reinforcement Learning}},
author = {von Werra, Leandro and Belkada, Younes and Tunstall, Lewis and Beeching, Edward and Thrush, Tristan and Lambert, Nathan and Huang, Shengyi and Rasul, Kashif and Gallouédec, Quentin},
license = {Apache-2.0},
url = {https://github.com/huggingface/trl},
year = {2020}
}