--- license: apache-2.0 library_name: transformers pipeline_tag: text-generation --- # iLLaDA-8B-Base iLLaDA is an 8B fully bidirectional masked diffusion language model trained from scratch with 12T pre-training tokens, an 8192-token context length, variable-length generation, and confidence-based scoring for multiple-choice evaluation. It was introduced in the paper [Improved Large Language Diffusion Models](https://huggingface.co/papers/2606.25331). Inference and evaluation codes: https://github.com/ML-GSAI/LLaDA. ## Architecture | | iLLaDA 8B | LLaDA 8B | | --- | ---: | ---: | | Layers | 32 | 32 | | Model dimension | 4096 | 4096 | | Attention heads | 32 | 32 | | Key/Value heads | 8 | 32 | | FFN dimension | 14,336 | 12,288 | | Vocabulary size | 155,136 | 126,464 | | Maximum sequence length | 8192 | 4096 | | Embedding and LM-head | Tied | Untied | | Total parameters | 7.62B | 8.02B | | Non-embedding parameters | 6.98B | 6.98B | ## Benchmark Results of Base Models | | iLLaDA 8B | LLaDA 8B | Dream 7B | Qwen2.5 7B | | --- | ---: | ---: | ---: | ---: | | Model | Diffusion | Diffusion | Diffusion | AR | | Training tokens | 12T | 2.3T | 18T + 0.6T | 18T | | MMLU | 74.8 | 65.9 | 69.5 | 71.9 | | BBH | 71.3 | 49.7 | 57.9 | 63.9 | | ARC-C | 60.8 | 45.9 | 59.8 | 51.5 | | HellaSwag | 76.6 | 70.5 | 73.3 | 79.0 | | GSM8K | 81.9 | 70.3 | 77.2 | 78.9 | | MATH | 38.4 | 31.4 | 39.6 | 41.1 | | HumanEval | 50.0 | 35.4 | 57.9 | 56.7 | | MBPP | 57.8 | 40.0 | 56.2 | 63.6 | | Average | 63.9 | 51.1 | 61.4 | 63.3 | ## How to use You can load and use the model with `transformers` as follows: ```python import torch from transformers import AutoModel, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained('GSAI-ML/iLLaDA-8B-Base', trust_remote_code=True) model = AutoModel.from_pretrained('GSAI-ML/iLLaDA-8B-Base', trust_remote_code=True, torch_dtype=torch.bfloat16) ``` Refer to the [GitHub repository](https://github.com/ML-GSAI/LLaDA) for generation scripts such as `generate.py`. ## Citation ```bibtex @article{nie2026illada, title={Improved Large Language Diffusion Models}, author={Nie, Shen and Min, Qiyang and Xu, Shaoxuan and Huang, Zihao and Song, Yuxuan and Shan, Yong and Lin, Yankai and Zhao, Wayne Xin and Li, Chongxuan and Wen, Ji-Rong}, journal={arXiv preprint arXiv:2606.25331}, year={2026} } ```