2024年3月12日 · Our method, named Branch-Train-MiX (BTX), starts from a seed model, which is branched to train experts in embarrassingly parallel fashion with high throughput and reduced communication cost.

named Branch-Train-MiX (BTX), starts from a seed model, which is branched to train experts in embarrassingly parallel fashion with high throughput and reduced communication cost. After

2024年7月11日 · Branch-Train-MiX (BTX) ，提高大型语言模型（LLMs）在多个专业领域（如编程、数学推理和世界知识）的能力。BTX方法的核心思想是结合了Branch-Train-Merge (BTM) 方法和Mixture-of-Experts (MoE) 架构的优势，同时减少了它们的不足。 BTX方法分为三个主要步骤： Branch & Train ：

Combining BTM and Mo strengths, the Branch Train Mix (BTX) model enhances training efficiency and performance. BTX merges feedforward sublayers of expert LLMs into a single Mo module at each layer. A router network selects which expert to use for each token, fine-tuning the model on combined data.

2024年3月15日 · 最近，Meta基础 人工智能研究 （FAIR）团队发布了名为Branch-Train-MiX (BTX)的方法，可从种子模型开始，该模型经过分支，以高吞吐量和低通信成本的并行方式训练专家模型。 Meta FAIR的成员之一Jason Weston在其X上发文介绍了这一进展。 BTX能够提高大型 语言模型 （LLMs）在多个专业领域的能力，如编程、数学推理、世界知识等细分专业领域。这些专家模型在训练后，其前馈参数被整合到混合专家（Mixture-of-Expert, MoE）层中，并进行 …

Researchers from FAIR at Meta introduce Branch-Train-Mix (BTX), a pioneering strategy at the confluence of parallel training, and the Mixture-of-Experts (MoE) model. BTX distinguishes itself by initiating parallel training for domain-specific experts.

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