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All models by inclusionAI βπ LLaDA2.1-flash is now live on ZenmuxAI! Try it via API π οΈ or Chat π¬: https://zenmux.ai/inclusionai/llada2.1-flash
LLaDA2.1-mini is a diffusion language model of the LLaDA series featuring the editing enhancement. It significantly improves inference speed while delivering strong task performance.
LLaDA2.1-mini has the following specifications:
Make sure you have transformers and its dependencies installed:
To achieve optimal performance, we recommend the following settings:
Sampling Parameters:
We recommend the following general sampling parameters: block_length=32, temperature=0.0, top_p=None and top_k=None. We are currently exploring more diverse sampling configurations.
Denoising Thresholds:
There are three denoising params: threshold, editing_threshold and max_post_steps. We recommend threshold=0.7, editing_threshold=0.5 for Quality Mode and threshold=0.5, editing_threshold=0.0 for Speed Mode. For both modes, we suggest setting max_post_steps to a value greater than 5. We recommend 16 as a balanced default, which was used for most of our internal testing.
Note: Low threshold may causes stuttering in trade-off for quick inference.
If you're in mainland China, we strongly recommend you to use our model from π€ModelScope
SGLang enables dLLM inference either through offline batching or by launching an HTTP server for online requests. You can start the SGLang dLLM using the following commands:
Pull Request (PR) has been submitted and merged to the SGLang community, please prepare the environment with the lateset version
This project is licensed under the terms of the Apache License 2.0.
For questions, collaborations, or feedback, please reach out via Hugging Face or open an issue in the repository.
π Join us in advancing open, efficient, and intelligent language models!
import torchimport torch.nn.functional as Ffrom transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "/path/to/LLaDA2.1-mini"device = "auto"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 = """Calculate 1+5-28*0.5-200=?"""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, threshold=0.5, editing_threshold=0, temperature=0.0,)generated_answer = tokenizer.decode( generated_tokens[0], skip_special_tokens=True,)print(generated_answer)python3 -m sglang.launch_server \ --model-path inclusionAI/LLaDA2.1-mini \ --dllm-algorithm JointThreshold \ --tp-size 1 \ --trust-remote-code \ --mem-fraction-static 0.8 \ --max-running-requests 1 \ --attention-backend flashinfer @misc{bie2026llada21speedingtextdiffusion, title={LLaDA2.1: Speeding Up Text Diffusion via Token Editing}, author={Tiwei Bie and Maosong Cao and Xiang Cao and Bingsen Chen and Fuyuan Chen and Kun Chen and Lun Du and Daozhuo Feng and Haibo Feng and Mingliang Gong and Zhuocheng Gong and Yanmei Gu and Jian Guan and Kaiyuan Guan and Hongliang He and Zenan Huang and Juyong Jiang and Zhonghui Jiang and Zhenzhong Lan and Chengxi Li and Jianguo Li and Zehuan Li and Huabin Liu and Lin Liu and Guoshan Lu and Yuan Lu and Yuxin Ma and Xingyu Mou and Zhenxuan Pan and Kaida Qiu and Yuji Ren and Jianfeng Tan and Yiding Tian and Zian Wang and Lanning Wei and Tao Wu and Yipeng Xing and Wentao Ye and Liangyu Zha and Tianze Zhang and Xiaolu Zhang and Junbo Zhao and Da Zheng and Hao Zhong and Wanli Zhong and Jun Zhou and Junlin Zhou and Liwang Zhu and Muzhi Zhu and Yihong Zhuang}, year={2026}, eprint={2602.08676}, archivePrefix={arXiv}, primaryClass={cs.LG}, url={https://arxiv.org/abs/2602.08676}, }