言語から画像、オープンウェイトからAPIまで。能力と実際の費用を比較。
次のアイデアに合うAIを見つけましょう。
This model aims to test the conversion between Megatron-LM and transformers. It is a small -like model that has been used to debug the script. Use it only for integration tests
翻訳待ち・原文を表示
BLOOM is an autoregressive Large Language Model (LLM), trained to continue text from a prompt on vast amounts of text data using industrial-scale computational resources. As such, it is able to output coherent text in 46 languages and 13 programming languages that is hardly distinguishable from text written by humans. BLOOM can also be instructed to perform text tasks it hasn't been explicitly trained for, by casting them as text generation tasks. *This section provides information about the model type, version, license, funders, release date, developers, and contact information.* *It is useful for anyone who wants to reference the model.* *All collaborators are either volunteers or have an agreement with their employer. (Further breakdown of participants forthcoming.)*
翻訳待ち・原文を表示
BLOOM LM BigScience Large Open-science Open-access Multilingual Language Model Model Card 1. Model Details 2. Uses 3. Training Data 4. Risks and Limitations 5. Evaluation 6. Recommendations 7. Glossary and Calculations 8. More Information 9. Model Card Authors *This section provides information for anyone who wants to know about the model.*
翻訳待ち・原文を表示
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。
1. Model Details 2. Uses 3. Training Data 4. Risks and Limitations 5. Evaluation 6. Recommendations 7. Glossary and Calculations 8. More Information 9. Model Card Authors **Cite as:** BigScience, _BigScience Language Open-science Open-access Multilingual (BLOOM) Language Model_. International, May 2021-May 2022 **Compute infrastructure:** Jean Zay Public Supercomputer, provided by the French government (see announcement).
翻訳待ち・原文を表示
BLOOM LM BigScience Large Open-science Open-access Multilingual Language Model Model Card 1. Model Details 2. Uses 3. Bias, Risks, and Limitations 4. Recommendations 5. Training Data 6. Evaluation 7. Environmental Impact 8. Technical Specifications 9. Citation 10. Glossary and Calculations 11. More Information 12. Model Card Authors 13. Model Card Contact *This section provides information for anyone who wants to know about the model.*
翻訳待ち・原文を表示
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。
基本情報を収録・詳細は順次追加
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。
基本情報を収録・詳細は順次追加
> We present BLOOMZ & mT0, a family of models capable of following human instructions in dozens of languages zero-shot. We finetune BLOOM & mT5 pretrained multilingual language models on our crosslingual task mixture (xP3) and find the resulting models capable of crosslingual generalization to unseen tasks & languages. Finetuned Model mt0-small mt0-base mt0-large mt0-xl mt0-xxl bloomz-560m bloomz-1b1 bloomz-1b7 bloomz-3b bloomz-7b1 bloomz Pretrained Model mt5-small mt5-base mt5-large mt5-xl mt5-xxl bloom-560m bloom-1b1 bloom-1b7 bloom-3b bloom-7b1 bloom
翻訳待ち・原文を表示
1. Model Summary 2. Use 3. Limitations 4. Training 5. Evaluation 7. Citation > We present BLOOMZ & mT0, a family of models capable of following human instructions in dozens of languages zero-shot. We finetune BLOOM & mT5 pretrained multilingual language models on our crosslingual task mixture (xP3) and find the resulting models capable of crosslingual generalization to unseen tasks & languages. - **Repository:** bigscience-workshop/xmtf - **Paper:** Crosslingual Generalization through Multitask Finetuning - **Point of Contact:** Niklas Muennighoff - **Languages:** Refer to bloom for pretraining & xP3 for finetuning language proportions. It understands both pretraining & finetuning languages. - **BLOOMZ & mT0 Model Family:**
翻訳待ち・原文を表示
> We present BLOOMZ & mT0, a family of models capable of following human instructions in dozens of languages zero-shot. We finetune BLOOM & mT5 pretrained multilingual language models on our crosslingual task mixture (xP3) and find the resulting models capable of crosslingual generalization to unseen tasks & languages. Finetuned Model mt0-small mt0-base mt0-large mt0-xl mt0-xxl bloomz-560m bloomz-1b1 bloomz-1b7 bloomz-3b bloomz-7b1 bloomz Pretrained Model mt5-small mt5-base mt5-large mt5-xl mt5-xxl bloom-560m bloom-1b1 bloom-1b7 bloom-3b bloom-7b1 bloom
翻訳待ち・原文を表示
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。
基本情報を収録・詳細は順次追加
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。
基本情報を収録・詳細は順次追加
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。
基本情報を収録・詳細は順次追加
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。
基本情報を収録・詳細は順次追加
> We present BLOOMZ & mT0, a family of models capable of following human instructions in dozens of languages zero-shot. We finetune BLOOM & mT5 pretrained multilingual language models on our crosslingual task mixture (xP3) and find our resulting models capable of crosslingual generalization to unseen tasks & languages. Finetuned Model mt0-small mt0-base mt0-large mt0-xl mt0-xxl bloomz-560m bloomz-1b1 bloomz-1b7 bloomz-3b bloomz-7b1 bloomz Pretrained Model mt5-small mt5-base mt5-large mt5-xl mt5-xxl bloom-560m bloom-1b1 bloom-1b7 bloom-3b bloom-7b1 bloom
翻訳待ち・原文を表示
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。
基本情報を収録・詳細は順次追加
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。
基本情報を収録・詳細は順次追加
**How do I pronounce the name of the model?** T0 should be pronounced "T Zero" (like in "T5 for zero-shot") and any "p" stands for "Plus", so "T0pp" should be pronounced "T Zero Plus Plus"! **Official repository**: bigscience-workshop/t-zero T0* shows zero-shot task generalization on English natural language prompts, outperforming GPT-3 on many tasks, while being 16x smaller. It is a series of encoder-decoder models trained on a large set of different tasks specified in natural language prompts. We convert numerous English supervised datasets into prompts, each with multiple templates using varying formulations. These prompted datasets allow for benchmarking the ability of a model to perform completely unseen tasks specified in natural language. To obtain T0*, we fine-tune a pretrained language model on this multitask mixture covering many different NLP tasks.
翻訳待ち・原文を表示
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。
基本情報を収録・詳細は順次追加
提供元・分類・入出力の条件を変更してください。新しいモデルを随時追加しています。
月間トークン数を調整してAPI費用を比較。
収集済みの比較可能な100万トークン当たり米ドル料金です。キャッシュ割引、段階料金、追加推論出力、税金は含みません。最新料金は出典で確認してください。
入力・出力・キャッシュ料金を分け、同じ用途と使用量で総費用を比較します。画像・音声・動画は枚数、時間、解像度で課金される場合があります。
必ずしもそうではありません。バージョンごとのライセンス、商用制限、ポリシーを確認してください。自前運用には計算資源と保守費用も必要です。
順位はテスト、バージョン、評価方法によります。実際の業務データで精度、遅延、安定性、費用を比較してください。
