Qwen3-30B-A3B-Instruct-2507-MoBE-hf

MoBE: Mixture-of-Basis-Experts for Compressing MoE-based LLMs

<div style="text-align:center;"> <a href="https://huggingface.co/papers/2508.05257"> <img src="https://img.shields.io/badge/arXiv-2508.05257-b31b1b.svg" alt="arXiv"> </a> </div>

This repository contains the weights for a compressed version of Qwen3 using the MoBE (Mixture-of-Basis-Experts) method, as described in the paper MoBE: Mixture-of-Basis-Experts for Compressing MoE-based LLMs.

For more usage instructions and implementation details, please check the official GitHub repository and the Qwen3 specific branch.

📘 Introduction

MoBE (Mixture-of-Basis-Experts) is a novel model compression technique designed for MoE LLMs developed by the AGI Center, Ant Group Research. It achieves efficient parameter reduction by factorizing each expert's weight matrix as:

$$ \mathbf{W} = \mathbf{A}\mathbf{B}, \quad \text{where} \quad \mathbf{B} = \sum_{i=1}^m \alpha_i B_i $$

• $\mathbf{A}$: Expert-specific matrix
• $\mathbf{B}$: Linear combination of basis matrices across all experts, weighted by coefficients $\alpha_i$

The factorization is learned by minimizing the reconstruction error between the original and compressed weight matrices.

🔍 Key Results

MoBE significantly outperforms prior compression methods with minimal accuracy degradation:

• Reduces parameter count by 24%–30% in leading open-source models.
• Incurs only 1%–2% absolute accuracy drop (≈2% relative).
• Demonstrated on Qwen3-235B, DeepSeek-V3 (671B), and Kimi-K2-Instruct (1T).

💡 MoBE Generate Example

To run this model, you will need the custom modeling files provided in the GitHub repository.

from transformers import AutoTokenizer
from models.modeling_deepseek_v3_mobe import DeepseekV3MoBEForCausalLM 
from models.modeling_qwen3_mobe import Qwen3MoBEForCausalLM
from models.modeling_kimi_k2_mobe import KimiK2MoBEForCausalLM
import torch

model_name = "Bobchenyx/Qwen3-235B-A22B-MoBE" # Replace with your model path
offload_folder = "./offload_dir"

tokenizer = AutoTokenizer.from_pretrained(model_name)
tokenizer.pad_token = tokenizer.eos_token

max_memory = {i: "120GiB" for i in range(8)}  
max_memory["cpu"] = "1200GiB"      

if 'Qwen' in model_name:
    model = Qwen3MoBEForCausalLM.from_pretrained(
    		model_name,
    		device_map="auto",
    		offload_folder=offload_folder,
    		offload_state_dict=True,
    		torch_dtype=torch.bfloat16
        max_memory=max_memory
    )
elif 'DeepSeek' in model_name:
    model = DeepseekV3MoBEForCausalLM.from_pretrained(
    		model_name,
    		device_map="auto",
    		offload_folder=offload_folder,
    		offload_state_dict=True,
    		torch_dtype=torch.bfloat16,
        max_memory=max_memory
    )
else:
		model = KimiK2MoBEForCausalLM.from_pretrained(
    		model_name,
    		device_map="auto",
    		offload_folder=offload_folder,
    		offload_state_dict=True,
    		torch_dtype=torch.bfloat16,
        max_memory=max_memory
    )

input_text = "Artificial intelligence is"
inputs = tokenizer(input_text, return_tensors="pt").to("cuda" if torch.cuda.is_available() else "cpu")

with torch.no_grad():
    outputs = model.generate(
    		**inputs,
    		max_new_tokens=128,
    		do_sample=True,
    		temperature=0.7,
    		pad_token_id=tokenizer.eos_token_id
    )

generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
print("Generated text:")
print(generated_text)

📚 Citation

If you find MoBE useful in your research or application, please consider citing our work:

@misc{chen2025mobemixtureofbasisexpertscompressingmoebased,
      title={MoBE: Mixture-of-Basis-Experts for Compressing MoE-based LLMs}, 
      author={Xiaodong Chen and Mingming Ha and Zhenzhong Lan and Jing Zhang and Jianguo Li},
      year={2025},
      eprint={2508.05257},
      archivePrefix={arXiv},
}