LLaDA-MoE

LLaDA-MoE is a new and upgraded series of the LLaDA diffusion language model. This pre-release includes two cutting-edge models:

LLaDA-MoE-7B-A1B-Base: A base pre-trained model designed for research and secondary development.
LLaDA-MoE-7B-A1B-Instruct: An instruction-tuned model optimized for practical applications.

🚀 Performance Highlights

Leading MoE Architecture:
The first open-source Mixture-of-Experts (MoE) diffusion large language model, pre-trained from scratch on approximately 20 trillion tokens.
Efficient Inference:
With 7 billion total parameters, only 1.4 billion are activated during inference. LLaDA-MoE significantly reduces computational costs while outperforming open-source dense models of similar scale.
Impressive Performance on Code & Complex Reasoning:
Excels in tasks such as code generation and advanced mathematical reasoning, demonstrating strong reasoning capabilities.
Tool Use:
Supports tool calling and achieves excellent performance in complex agent-based tasks.
Open & Extensible:
Fully open-source with commitment to transparency. We plan to release a leading inference framework in the future and continue investing in cutting-edge areas like diffusion LLMs (dLLM) to drive disruptive innovation.

📦 Model Variants

Model ID	Description	Hugging Face Link
`inclusionAI/LLaDA-MoE-7B-A1B-Base`	Base pre-trained model for research and fine-tuning.	🤗 Model Card
`inclusionAI/LLaDA-MoE-7B-A1B-Instruct`	Instruction-tuned model, ready for downstream applications.	🤗 Model Card

🔍 Model Overview

LLaDA-MoE-7B-A1B has the following specifications:

Type: Mixture-of-Experts (MoE) Diffusion Language Model
Total Parameters (Non-Embedding): 7.03B
Number of Layers: 16
Attention Heads: 16
Context Length: 4,096 tokens
Position Embedding: Rotary (RoPE)
Vocabulary Size: 157,184

⚡ Infra

1. We highly recommend you generate with dInfer（1000+ Tokens/S）

dInfer v0.1 speedup
Figure: Display of generation speed

On HumanEval, dInfer achieves over 1,100 TPS at batch size 1, and averages more than 800 TPS across six benchmarks on a single node with 8 H800 GPUs.

Install dInfer

git clone https://github.com/inclusionAI/dInfer.git
cd dInfer
pip install .

Convert to FusedMoE

Use the conversion tool to fuse the experts.

# From repo root
python tools/transfer.py \
  --input  /path/to/LLaDA-MoE-7B-A1B-Instruct \
  --output /path/to/LLaDA-MoE-7B-A1B-Instruct-fused

Use the model in dInfer

import torch
from transformers import AutoTokenizer

from dinfer.model import AutoModelForCausalLM
from dinfer.model import FusedOlmoeForCausalLM
from dinfer import BlockIteratorFactory, KVCacheFactory
from dinfer import ThresholdParallelDecoder, BlockWiseDiffusionLLM

m = "/path/to/LLaDA-MoE-7B-A1B-Instruct-fused"
tok = AutoTokenizer.from_pretrained(m, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(m, trust_remote_code=True, torch_dtype="bfloat16")

decoder = ThresholdParallelDecoder(0, threshold=0.9)
dllm = BlockWiseDiffusionLLM(model, decoder, BlockIteratorFactory(True), cache_factory=KVCacheFactory('dual'))

prompt = "Lily can run 12 kilometers per hour for 4 hours. After that, she can run 6 kilometers per hour. How many kilometers can she run in 8 hours?"
input_ids = tokenizer(prompt)['input_ids']
input_ids = torch.tensor(input_ids).to(device).unsqueeze(0)
res = dllm.generate(input_ids, gen_length=gen_len, block_length=block_len)

2. No Speedup: transformers

Make sure you have transformers and its dependencies installed:

import torch
import numpy as np
import torch.nn.functional as F

from transformers import AutoTokenizer, AutoModel


def add_gumbel_noise(logits, temperature):
    if temperature == 0:
        return logits
    logits = logits.to(torch.float64)
    noise = torch.rand_like(logits, dtype=torch.float64)
    gumbel_noise = (- torch.log(noise)) ** temperature
    return logits.exp() / gumbel_noise


def get_num_transfer_tokens(mask_index, steps):
    mask_num = mask_index.sum(dim=1, keepdim=True)

    base = mask_num // steps
    remainder = mask_num % steps

    num_transfer_tokens = torch.zeros(mask_num.size(0), steps, device=mask_index.device, dtype=torch.int64) + base

    for i in range(mask_num.size(0)):
        num_transfer_tokens[i, :remainder[i]] += 1

    return num_transfer_tokens


@ torch.no_grad()
def generate(model, prompt, steps=128, gen_length=128, block_length=128, temperature=0.,
             cfg_scale=0., remasking='low_confidence', mask_id=156895):
    x = torch.full((1, prompt.shape[1] + gen_length), mask_id, dtype=torch.long).to(model.device)
    x[:, :prompt.shape[1]] = prompt.clone()
    prompt_index = (x != mask_id)

    assert gen_length % block_length == 0
    num_blocks = gen_length // block_length
    assert steps % num_blocks == 0
    steps = steps // num_blocks

    for num_block in range(num_blocks):
        block_mask_index = (x[:, prompt.shape[1] + num_block * block_length: prompt.shape[1] + (num_block + 1) * block_length:] == mask_id)
        num_transfer_tokens = get_num_transfer_tokens(block_mask_index, steps)
        for i in range(steps):
            mask_index = (x == mask_id)
            if cfg_scale > 0.:
                un_x = x.clone()
                un_x[prompt_index] = mask_id
                x_ = torch.cat([x, un_x], dim=0)
                logits = model(x_).logits
                logits, un_logits = torch.chunk(logits, 2, dim=0)
                logits = un_logits + (cfg_scale + 1) * (logits - un_logits)
            else:
                logits = model(x).logits

            logits_with_noise = add_gumbel_noise(logits, temperature=temperature)
            x0 = torch.argmax(logits_with_noise, dim=-1) # b, l

            if remasking == 'low_confidence':
                p = F.softmax(logits, dim=-1)
                x0_p = torch.squeeze(
                    torch.gather(p, dim=-1, index=torch.unsqueeze(x0, -1)), -1) # b, l
            elif remasking == 'random':
                x0_p = torch.rand((x0.shape[0], x0.shape[1]), device=x0.device)
            else:
                raise NotImplementedError(remasking)

            x0_p[:, prompt.shape[1] + (num_block + 1) * block_length:] = -np.inf

            x0 = torch.where(mask_index, x0, x)
            confidence = torch.where(mask_index, x0_p, -np.inf)

            transfer_index = torch.zeros_like(x0, dtype=torch.bool, device=x0.device)
            for j in range(confidence.shape[0]):
                _, select_index = torch.topk(confidence[j], k=num_transfer_tokens[j, i])
                transfer_index[j, select_index] = True
            x[transfer_index] = x0[transfer_index]

    return x


device = 'cuda'
model = AutoModel.from_pretrained('inclusionAI/LLaDA-MoE-7B-A1B-Instruct', trust_remote_code=True, torch_dtype=torch.bfloat16).to(device).eval()
tokenizer = AutoTokenizer.from_pretrained('inclusionAI/LLaDA-MoE-7B-A1B-Instruct', trust_remote_code=True)

prompt = "Lily can run 12 kilometers per hour for 4 hours. After that, she runs 6 kilometers per hour. How many kilometers can she run in 8 hours?"
m = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": prompt}
]
prompt = tokenizer.apply_chat_template(m, add_generation_prompt=True, tokenize=False)

input_ids = tokenizer(prompt)['input_ids']
input_ids = torch.tensor(input_ids).to(device).unsqueeze(0)

text = generate(model, input_ids, steps=128, gen_length=128, block_length=32, temperature=0., cfg_scale=0., remasking='low_confidence')
print(tokenizer.batch_decode(text[:, input_ids.shape[1]:], skip_special_tokens=False)[0])

📚 Citation LLaDA-MoE

If you find LLaDA-MoE useful in your research or applications, please cite our paper:

@article{zhu2025llada,
  title={LLaDA-MoE: A Sparse MoE Diffusion Language Model},
  author={Fengqi Zhu and Zebin You and Yipeng Xing and Zenan Huang and Lin Liu and Yihong Zhuang and Guoshan Lu and Kangyu Wang and Xudong Wang and Lanning Wei and Hongrui Guo and Jiaqi Hu and Wentao Ye and Tieyuan Chen and Chenchen Li and Chengfu Tang and Haibo Feng and Jun Hu and Jun Zhou and Xiaolu Zhang and Zhenzhong Lan and Junbo Zhao and Da Zheng and Chongxuan Li and Jianguo Li and Ji-Rong Wen},
  journal={arXiv preprint arXiv:2509.24389},
  year={2025}
}