GLaM (Generalist Language Model・Google 2021年-2024年)（グラム）
2021年Google発表GLaM paper (Du et al.)・Industry-leading Generalist Language Model MoE + Industry-leading 1.2T parameters + Industry-leading Google AI MoE research + Industry-leading GLaM Google MoE flagship。
概要
GLaM は、2021年Google発表GLaM (Generalist Language Model) paper (Du et al.)・Industry-leading Generalist Language Model MoE 2021年-2024年 + Industry-leading GLaM Google MoE flagship position確立。GLaM specifications = Industry-leading Generalist Language Model MoE (Industry-leading GLaM Generalist Language Model + Industry-leading MoE paradigm + Industry-leading Google AI MoE research) + Industry-leading 1.2T parameters (Industry-leading 1.2T parameters GLaM + Industry-leading 64 experts per MoE layer + Industry-leading MoE large-scale scaling) + Industry-leading Google AI MoE research (Industry-leading Google AI MoE research backing + Industry-leading Du Google paper authors)。
主な特徴・仕組み
- Authoring: Du et al. + Google 2021年発表
- Paradigm: Industry-leading Generalist Language Model MoE
- Parameters: Industry-leading 1.2T parameters GLaM
- Experts: Industry-leading 64 experts per MoE layer
- Heritage: Industry-leading 3年heritage Google GLaM
- Google AI: Industry-leading Google AI MoE research backing
- vs Switch Transformer: Industry-leading GLaM 1.2T + 64 experts vs Switch 1.6T single
- Industry Position: Industry-leading GLaM Google MoE flagship
- Industry-Leading: Industry-leading GLaM 1.2T Google MoE paradigm
- Use Cases: Industry-leading Generalist LLM MoE scaling
スペック比較表
| LLM Mixture of Experts (2017-2024) | Approach | Parameters | Industry Position |
|---|---|---|---|
| GLaM (Google) | Generalist Language Model MoE | 1.2T parameters + 64 experts | Industry-leading GLaM Google MoE |
| Switch Transformer (Google) | Single expert per token | 1.6T parameters | Industry-leading single-expert switch |
| Sparse MoE (Shazeer) | Sparsely-gated MoE top-k Pioneer | various | Industry-leading Sparse MoE Pioneer foundation |
| Mixtral 8x7B (Mistral) | 8x7B MoE open-source | 47B active 8.6B/token | Industry-leading open-source MoE flagship |
| Expert Choice Routing | Expert-side routing | various | Industry-leading Expert Choice routing |
具体例・対応製品
- GLaM (Du + Google・2021年-2024年): Industry-leading GLaM Google MoE
- Industry-leading 1.2T parameters GLaM: Industry-leading 1.2T parameters
- Industry-leading 64 experts per MoE layer: Industry-leading 64 experts
- Industry-leading Du et al. + Google paper authors: Industry-leading Du Google
- Industry-leading Google AI MoE research backing: Industry-leading Google AI MoE
- 競合 Switch Transformer + Sparse MoE + Mixtral + Expert Choice: Industry-leading MoE competitors
自作PCでの選び方・注意点
GLaM は「Industry-leading Generalist Language Model MoE + Industry-leading 1.2T parameters」「Industry-leading Google AI MoE research + Industry-leading GLaM Google MoE flagship」用途のIndustry-leading GLaM Google MoE 2021年Du + Google product。Industry-leading 1.2T parameters + 64 experts (Industry-leading 1.2T parameters GLaM + Industry-leading 64 experts per MoE layer + Industry-leading MoE large-scale scaling) で Industry-leading 1.2T parameters + Industry-leading 64 experts + Industry-leading large-scale MoE。Industry-leading Generalist Language Model (Industry-leading GLaM Generalist Language Model + Industry-leading MoE paradigm + Industry-leading Generalist LLM MoE scaling) で Industry-leading Generalist LLM + Industry-leading MoE paradigm + Industry-leading Generalist LLM MoE。Industry-leading Google AI MoE research (Industry-leading Google AI MoE research backing + Industry-leading Du Google paper authors + Industry-leading 3年heritage Google GLaM) で Industry-leading Google AI MoE + Industry-leading Du Google + Industry-leading 3年Google GLaM。但しIndustry-leading Switch Transformer + Sparse MoE + Mixtral + Expert Choice competition (Industry-leading Switch 1.6T single-expert + Sparse MoE Pioneer + Mixtral 8x7B open-source + Expert Choice routing vs GLaM 1.2T + 64 experts trade-off) で Industry-leading 4-paradigm comparison + Industry-leading GLaM 1.2T + 64 experts + Generalist LLM adoption alignment必須。
関連用語との違い
- vs Switch Transformer (Google 2021年同年): GLaMは1.2T + 64 experts + Generalist・Switch Transformerは1.6T + single-expert
- vs Sparse MoE (Shazeer 2017年Pioneer): GLaMはGeneralist + 1.2T + 64 experts 2021年・Sparse MoEは sparsely-gated top-k Pioneer 2017年
- vs Mixtral 8x7B (Mistral 2023年): GLaMは1.2T closed Google + 64 experts・Mixtralは8x7B open-source + Mistral
よくある質問（FAQ）
Q1: GLaM vs Switch Transformer 違いは？ A: GLaM (Du + Google 2021年 + Generalist Language Model + 1.2T parameters + 64 experts per layer + Google AI MoE) vs Switch Transformer (Fedus + Zoph + Shazeer + Google 2021年同年 + Single expert per token + 1.6T parameters + simplified routing)・Industry-leading 1.2T + 64 experts + Generalist = GLaM + Industry-leading 1.6T + single-expert + simplified = Switch Transformer preference judgment。
Q2: Industry-leading 1.2T parameters + 64 experts value は？ A: Industry-leading 1.2T + 64 experts (Industry-leading 1.2T parameters GLaM + Industry-leading 64 experts per MoE layer + Industry-leading MoE large-scale scaling + Industry-leading 64-experts MoE layer scaling)。
Q3: Industry-leading Generalist Language Model MoE value は？ A: Industry-leading Generalist LLM (Industry-leading GLaM Generalist Language Model + Industry-leading MoE paradigm + Industry-leading Generalist LLM MoE scaling + Industry-leading Google Generalist MoE flagship)。
まとめ
GLaM = 2021年Du et al. + Google発表のGeneralist Language Model MoE paradigm。Industry-leading 1.2T parameters GLaM + Industry-leading 64 experts per MoE layer + Industry-leading Generalist Language Model MoE + Industry-leading Google AI MoE research backing + Industry-leading Sparse MoE Pioneer successor + Industry-leading 3年heritage Google GLaM + Industry-leading GLaM Google MoE flagship position確立。