LLaDA2.0-mini-preview is a diffusion language model featuring a 16BA1B Mixture-of-Experts (MoE) architecture. As an enhanced, instruction-tuned iteration of the LLaDA series, it is optimized for practical applications.

Benchmark	Ling-mini-2.0	LLaDA-MoE-7B-A1B-Instruct	LLaDA2.0-mini-preview
Average	74.60	59.72	66.89
Knowledge
MMLU	82.15	67.18	72.49
MMLU-PRO	63.72	44.64	49.22
CMMLU	80.84	64.30	67.53
C-EVAL	82.10	63.93	66.54
Reasoning
squad2.0	75.56	86.81	85.61
drop	78.80	79.77	79.49
korbench	62.72	38.40	37.26
Coding
CruxEval-O	76.12	42.38	61.88
mbpp	84.07	70.02	77.75
MultiPL-E	67.09	52.53	62.43
humaneval	85.98	61.59	80.49
Bigcodebench-Full	35.00	20.44	30.44
Math
GSM8K	94.62	82.41	89.01
math	94.66	58.68	73.50
Agent & Alignment
BFCL_Live	53.98	63.09	74.11
IFEval-strict -prompt	76.16	59.33	62.50

🚀 Performance Highlights

Leading MoE Architecture: The open-source Mixture-of-Experts (MoE) diffusion large language model continually trained on the Ling2.0 series with approximately 20 trillion tokens.
Efficient Inference: With 16 billion total parameters, only 1.4 billion are activated during inference. LLaDA2.0-mini-preview 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.

🗺️ What's Next

Supercharged Reasoning with LLaDA 2.0: LLaDA 2.0 series will be fine-tuned with Reinforcement Learning, unlocking a new level of sophisticated reasoning and problem-solving abilities.
Tools for Innovators: we will release a detailed tutorial and our complete post-training framework. Whether you want to master the current model or build your own customized versions, you'll have the tools you need. Stay tuned

📦 Model Variants

Model ID	Description	Hugging Face Link
`inclusionAI/LLaDA2.0-mini-preview`	Instruction-tuned model, ready for downstream applications.	🤗 Model Card
`inclusionAI/LLaDA2.0-flash-preview`	Instruction-tuned model, ready for downstream applications.	🤗 Model Card

🔍 Model Overview

LLaDA2.0-mini-preview has the following specifications:

Type: Mixture-of-Experts (MoE) Diffusion Language Model
Total Parameters (Non-Embedding): 16B
Number of Layers: 20
Attention Heads: 16
Context Length: 4,096 tokens
Position Embedding: Rotary (RoPE)
Vocabulary Size: 157,184

🤗 Hugging Face Transformers

Make sure you have transformers and its dependencies installed:

python

import torch
import torch.nn.functional as F
from transformers import AutoModelForCausalLM
from transformers import AutoTokenizer

model_path = "/path/to/LLaDA2.0-mini-preview"
device = "cuda:0"
model = AutoModelForCausalLM.from_pretrained(
    model_path, trust_remote_code=True, device_map=device
)
model = model.to(torch.bfloat16)
model.eval()
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

prompt = "Why does Camus think that Sisyphus is happy?"
input_ids = tokenizer.apply_chat_template(
    [{"role": "user", "content": prompt}],
    add_generation_prompt=True,
    tokenize=True,
    return_tensors="pt",
)
generated_tokens = model.generate(
    inputs=input_ids,
    eos_early_stop=True,
    gen_length=512,
    block_length=32,
    steps=32,
    temperature=0.0,
)
generated_answer = tokenizer.decode(
    generated_tokens[0],
    skip_special_tokens=True,
)
print(generated_answer)

Best Practices

To achieve optimal performance, we recommend the following settings:

Sampling Parameters: We suggest using Temperature=0.0, block_length=32, and steps=32. Using a higher temperature value may occasionally result in language mixing and a slight decrease in model performance.
Adequate Output Length: We recommend using an output length of 2048 tokens for most queries. For benchmarking on problems require more output length, such as those found in math and programming competitions, we suggest setting the max output length to 4096 tokens.

🌐 License

This project is licensed under the terms of the Apache License 2.0.

🤝 Contact & Collaboration

For questions, collaborations, or feedback, please reach out via Hugging Face or open an issue in the repository.

👉 Join us in advancing open, efficient, and intelligent language models!

Citation

bibtex

@misc{bie2025llada20scalingdiffusionlanguage,
      title={LLaDA2.0: Scaling Up Diffusion Language Models to 100B}, 
      author={Tiwei Bie and Maosong Cao and Kun Chen and Lun Du and Mingliang Gong and Zhuochen Gong and Yanmei Gu and Jiaqi Hu and Zenan Huang and Zhenzhong Lan and Chengxi Li and Chongxuan Li and Jianguo Li and Zehuan Li and Huabin Liu and Ling Liu and Guoshan Lu and Xiaocheng Lu and Yuxin Ma and Jianfeng Tan and Lanning Wei and Ji-Rong Wen and Yipeng Xing and Xiaolu Zhang and Junbo Zhao and Da Zheng and Jun Zhou and Junlin Zhou and Zhanchao Zhou and Liwang Zhu and Yihong Zhuang},
      year={2025},
      eprint={2512.15745},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2512.15745}, 
}

inclusionAI/LLaDA2.0-mini-preview

LLaDA2.0-mini-preview