Olmo-Hybrid-7B

模型介绍

We expand on our Olmo model series by introducing Olmo Hybrid, a new 7B hybrid RNN model in the Olmo family. Olmo Hybrid dramatically outperforms Olmo 3 in final performance, consistently showing roughly 2x data efficiency on core evals over the course of our pretraining run. We also show gains in performance on long-context benchmarks, as well as improved inference efficiency (throughput and memory) on long-context lengths by a factor of 75%. The training of our hybrid model makes use of Olmo 3 7B, except that we change the learning rate schedule to be a standard cosine schedule rather than the piecewise schedule used by Olmo 3. Additionally, we use the improved data mix of Olmo 3 32B instead of the Olmo 3 7B mix. The table below highlights the architecture differences in our hybrid model. Our overall layer matches the transformer architecture of Olmo 3 7B, except that 75% of layers use gated DeltaNet heads instead of attention heads. The layers alternate so that 3 contain DeltaNet sublayers followed by 1 with a multihead attention sublayer. In particular, each head uses gated DeltaNet heads, extended with negative eigenvaluesWe reduced the number of heads from 32 to 30 while keeping the head dimension fixed at 128 (effectively reducing dmodel from 4096 to 3840). Lastly, head dimension is doubled, which is the default behavior for DeltaNet.

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。