SDAR-8B-Chat

模型介绍

**SDAR** (**S**ynergy of **D**iffusion and **A**uto**R**egression) model is a new large language model that integrates autoregressive (AR) and discrete diffusion modeling strategies. It combines the efficient training paradigm of AR models with the highly parallel inference capability of diffusion models, while delivering performance fully on par with SOTA open-source AR models. At the same time, SDAR sets a new benchmark as the most powerful diffusion language model to date. We highlight three major conclusions from our study: > [!IMPORTANT] > Take-home message > > - **Balanced Efficiency:** SDAR unifies the **efficient training** of AR models with the **parallel inference** of diffusion, achieving both fast training and inference. > - **Fair Comparisons:** In rigorously controlled experiments, SDAR achieves **on-par general task performance** with strong AR baselines, ensuring credibility and reproducibility. > - **Superior Learning Efficiency:** On complex scientific reasoning tasks (e.g., GPQA, ChemBench, Physics), SDAR shows **clear gains over AR models** of the same scale, approaching or even exceeding leading closed-source systems. For **SDAR** models, inference hyperparameters are set to: `block_length = 4`, `denoising_steps = 4`, greedy decoding.

主要特点

特点摘要待依据来源补齐，不凭模型名称推断能力。

局限与注意事项

暂无足够来源支持具体缺点结论。请重点验证实际任务表现、时延、数据政策、许可与版本生命周期。

来源报价与费用分析

同一模型在不同渠道、地区和配置下可能采用不同价格。下表独立保留各渠道报价，不将渠道价格视为厂商官方统一价格。

公开评测与运行配置

暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

常见问题

资料来源与可追溯记录

许可证：apache-2.0。模型介绍与第三方评测均以原来源的具体版本和方法为准。