Deepseek-V2-13B-Math7K-Expert-Enhance-Subset-Expert-MoE-32-experts

Model Card for Deepseek-V2-13B-Math7K-Expert-Enhance-Subset-Expert-MoE-32-experts

This model is a fine-tuned version of None on the HectorHe/math7k dataset. 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="HectorHe/Deepseek-V2-13B-Math7K-Expert-Enhance-Subset-Expert-MoE-32-experts", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])

Training procedure

<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>

This model was trained with Expert Enhance Trainer, a method introduced in None.

Framework versions

• TRL: 0.18.0.dev0
• Transformers: 4.52.1
• Pytorch: 2.6.0
• Datasets: 4.0.0
• Tokenizers: 0.21.4

Citations

Cite Expert Enhance Trainer as:

@inproceedings{Github: https://github.com/HectorHHZ/Efficient-Distillation,
    title        = {{Efficient Distillation}},
    author       = {Haoze He(Hector), CMU},
    year         = 2025,
}

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}}
}