找到适合你的 AI 模型。

Gemini 4 Carbon (High)

Google

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基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

Gemini 4 Argon

Google

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特殊优惠组该模型由部分资格账号，配合特定参数请求 Gemini 3.8 Flash 实现，已检测上游确实路由到 Gemini 4 这俩是 Gemini4 内部不同的检查点，据说 b 比 a 好一点，但是 b 现在的思考等级低一些， 具体智商得客户实际测试感受一下了

Gemini 3.8 Flash (High)

Google

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Gemini 3.8 Flash 是我们最智能的 Flash 模型，专为长远软件工程、自主代理和复杂企业工作流程设计——所有这些都具备 Flash 的速度和成本效益。

alphagenome-all-folds

Google

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基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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alphagenome-fold-0

Google

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alphagenome-fold-1

Google

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基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

基础资料已收录 · 缺失资料后续补充

alphagenome-fold-2

Google

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alphagenome-fold-3

Google

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bert_for_seq_generation_L-24_bbc_encoder

Google

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基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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bert_uncased_L-10_H-128_A-2

Google

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基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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bert_uncased_L-10_H-256_A-4

Google

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基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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bert_uncased_L-10_H-512_A-8

Google

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基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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bert_uncased_L-10_H-768_A-12

Google

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基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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bert_uncased_L-12_H-128_A-2

Google

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基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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bert_uncased_L-12_H-256_A-4

Google

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基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

基础资料已收录 · 缺失资料后续补充

bert_uncased_L-12_H-512_A-8

Google

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基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

基础资料已收录 · 缺失资料后续补充

bert_uncased_L-12_H-768_A-12

Google

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基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

基础资料已收录 · 缺失资料后续补充

bert_uncased_L-2_H-128_A-2

Google

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This is the set of 24 BERT models referenced in Well-Read Students Learn Better: On the Importance of Pre-training Compact Models (English only, uncased, trained with WordPiece masking). We have shown that the standard BERT recipe (including model architecture and training objective) is effective on a wide range of model sizes, beyond BERT-Base and BERT-Large. The smaller BERT models are intended for environments with restricted computational resources. They can be fine-tuned in the same manner as the original BERT models. However, they are most effective in the context of knowledge distillation, where the fine-tuning labels are produced by a larger and more accurate teacher. Our goal is to enable research in institutions with fewer computational resources and encourage the community to seek directions of innovation alternative to increasing model capacity.

译文排队中 · 当前显示来源原文

bert_uncased_L-2_H-256_A-4

Google

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This is the set of 24 BERT models referenced in Well-Read Students Learn Better: On the Importance of Pre-training Compact Models (English only, uncased, trained with WordPiece masking). We have shown that the standard BERT recipe (including model architecture and training objective) is effective on a wide range of model sizes, beyond BERT-Base and BERT-Large. The smaller BERT models are intended for environments with restricted computational resources. They can be fine-tuned in the same manner as the original BERT models. However, they are most effective in the context of knowledge distillation, where the fine-tuning labels are produced by a larger and more accurate teacher. Our goal is to enable research in institutions with fewer computational resources and encourage the community to seek directions of innovation alternative to increasing model capacity.

译文排队中 · 当前显示来源原文

bert_uncased_L-2_H-512_A-8

Google

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基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

基础资料已收录 · 缺失资料后续补充

bert_uncased_L-2_H-768_A-12

Google

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基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

基础资料已收录 · 缺失资料后续补充

bert_uncased_L-4_H-128_A-2

Google

♡

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

基础资料已收录 · 缺失资料后续补充

bert_uncased_L-4_H-256_A-4

Google

♡

This is the set of 24 BERT models referenced in Well-Read Students Learn Better: On the Importance of Pre-training Compact Models (English only, uncased, trained with WordPiece masking). We have shown that the standard BERT recipe (including model architecture and training objective) is effective on a wide range of model sizes, beyond BERT-Base and BERT-Large. The smaller BERT models are intended for environments with restricted computational resources. They can be fine-tuned in the same manner as the original BERT models. However, they are most effective in the context of knowledge distillation, where the fine-tuning labels are produced by a larger and more accurate teacher. Our goal is to enable research in institutions with fewer computational resources and encourage the community to seek directions of innovation alternative to increasing model capacity.

译文排队中 · 当前显示来源原文

bert_uncased_L-4_H-512_A-8

Google

♡

This is the set of 24 BERT models referenced in Well-Read Students Learn Better: On the Importance of Pre-training Compact Models (English only, uncased, trained with WordPiece masking). We have shown that the standard BERT recipe (including model architecture and training objective) is effective on a wide range of model sizes, beyond BERT-Base and BERT-Large. The smaller BERT models are intended for environments with restricted computational resources. They can be fine-tuned in the same manner as the original BERT models. However, they are most effective in the context of knowledge distillation, where the fine-tuning labels are produced by a larger and more accurate teacher. Our goal is to enable research in institutions with fewer computational resources and encourage the community to seek directions of innovation alternative to increasing model capacity.

译文排队中 · 当前显示来源原文