To break the performance ceiling of fine-grained MoE designs that are solely confined to the intermediate dimension, we introduce the FineRMoE (FineR-grained MoE) architecture. It pioneers the expansion of the fine-grained expert design in MoE models from only the intermediate dimension to the output dimension, aiming to enhance expert specialization beyond the single-dimension limit.

Model Overview

This version has the following features:

Type: Causal Language Models
Number of Parameters: 26.65B
Number of Activated Paramaters: 7.94B
Number of Experts: 128
Number of Activated Experts: 2
Intermediate Granularity: 32
Intermediate Expansion Rate: 1
Output Granularity: 2
Output Expansion Rate: 2

Quickstart

Requirements

To inference using the FineRMoE, the recommended version of transformers is transformers=4.50.0.

Inference

The following contains a code snippet illustrating how to use the model generate content based on given inputs.

python

import os
import json
import torch
import argparse
from transformers import (
    AutoConfig,
    AutoModelForCausalLM,
    AutoTokenizer,
)

# register the FineRMoE
def hf_register(hf_ckpt_path):
     has_py_file = False
     for filename in os.listdir(hf_ckpt_path):
          if filename.endswith(".py"):
               has_py_file = True
               modeling_filename = filename.split('.')[0]
     if has_py_file:
          print("There exists a modeling file.")
          config_file = os.path.join(hf_ckpt_path, 'config.json')
          with open(config_file, "r", encoding="utf-8") as file:
               config = json.load(file)
          model_type = config['model_type']

          import sys, inspect, importlib
          sys.path.append(hf_ckpt_path)
          module = importlib.import_module(modeling_filename)

          for name, obj in inspect.getmembers(module, inspect.isclass):
               if not obj.__module__ == module.__name__:
                    continue
               print(name, obj)
               if name.endswith("CausalLM"):
                    print(f"Found CausalLM with name: {name}")
                    model_module = obj
               elif name.endswith("Config"):
                    print(f"Found Config with name: {name}")
                    config_module = obj
                    
          AutoConfig.register(model_type, config_module)
          AutoModelForCausalLM.register(config_module, model_module)

@torch.inference_mode()
def main(args):
     # load the tokenizer and the model
     hf_register(args.model)
     tokenizer = AutoTokenizer.from_pretrained(args.model, trust_remote_code=True)
     model = AutoModelForCausalLM.from_pretrained(
          args.model,
          torch_dtype=torch.float16,
          attn_implementation=None,
          device_map="auto",
          trust_remote_code=True,
     )
     device = model.device if hasattr(model, "device") else next(model.parameters()).device

     # prepare the model input
     prompt = "Please introduce the history of artificial intelligence."
     messages = [
          {"role": "user", "content": prompt}
     ]
     text = tokenizer.apply_chat_template(
          messages,
          tokenize=False,
          add_generation_prompt=True
     )
     inputs = tokenizer([text], return_tensors="pt").to(device)

     # conduct text completion
     outputs = model.generate(
          **inputs,
          max_new_tokens=32000,
          do_sample=False,
          use_cache=True,
          pad_token_id=tokenizer.eos_token_id,
     )
     
     output_ids = outputs[0][len(inputs.input_ids[0]):].tolist() 
     content = tokenizer.decode(output_ids, skip_special_tokens=True).strip("\n")
     print("content:", content)

if __name__ == "__main__":
     parser = argparse.ArgumentParser()
     parser.add_argument("--model", default="FineRMoE-26.65B-A7.94B")
     args = parser.parse_args()
     main(args)

Citation

If you find our work helpful, feel free to give us a cite.

@misc{liao2026finermoedimensionexpansionfinergrained,
      title={FineRMoE: Dimension Expansion for Finer-Grained Expert with Its Upcycling Approach}, 
      author={Ning Liao and Xiaoxing Wang and Xiaohan Qin and Junchi Yan},
      year={2026},
      eprint={2603.13364},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2603.13364}, 
}

NingLiao/FineRMoE-26.65B-A7.94B

FineRMoE

Introduction

Model Overview

Quickstart

Requirements

Inference

Citation

NingLiao/
FineRMoE-26.65B-A7.94B