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HellsingEmperor
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Model Details
Model Provider
HellsingEmperor
Model Tree
Qwen/Qwen3-0.6B
Input Modalities
Output Modalities
Supported Functionality
python
from transformers import pipelinequestion = "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"])
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.
If you use this model in your research or project, please cite:
bibtex
@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}}
Cite TRL as:
bibtex
@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}}