base_model, library_name, model_name, tags, licence

base_model	library_name	model_name	tags	licence
jayzou3773/qwen3-moe-neuron_structure_drop-p50-s1k-128samples	transformers	jhhj25/qwen3-moe-neuron_structure_drop-p50-s1k-128samples-sft	generated_from_trainer trl sft	license

Model Card for jhhj25/qwen3-moe-neuron_structure_drop-p50-s1k-128samples-sft

This model is a fine-tuned version of jayzou3773/qwen3-moe-neuron_structure_drop-p50-s1k-128samples. It has been trained using TRL.

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="jhhj25/qwen3-moe-neuron_structure_drop-p50-s1k-128samples-sft", 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 SFT.

Framework versions

• TRL: 0.16.0.dev0
• Transformers: 4.51.3
• Pytorch: 2.6.0+cu124
• Datasets: 3.5.0
• Tokenizers: 0.21.4

Citations

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

Router mask / pruned experts

• Mask not materialized (reason: zero3_detected).