Erste Schritte mit Amazon SageMaker JumpStart
Übersicht
Amazon SageMaker JumpStart ist ein Hub für Machine Learning (ML), der Ihnen helfen kann, Ihren Einsatz von ML zu beschleunigen. Entdecken Sie, wie Sie mit integrierten Algorithmen mit vorab trainierten Modellen aus Modell-Hubs, vorab trainierten Basismodellen und vorgefertigten Lösungen zur Lösung gängiger Anwendungsfälle beginnen können. Schauen Sie sich zum Einstieg die Dokumentation oder Beispiel-Notebooks an, die Sie schnell ausführen können.
Showing results: 1-12
Total results: 567
Total results: 567
Beliebtheit
- Beliebtheit
- Zuerst vorgestellt
- Modellname von A bis Z
- Modellname von Z bis A
-
foundation model
FeaturedText Generation
Meta-Llama-3-70B-Instruct
Meta70B instruction tuned variant of Llama 3 models. Llama 3 uses a decoder-only transformer architecture and new tokenizer that provides improved model performance.Fine-tunable -
foundation model
FeaturedText Generation
Meta-Llama-3.1-405B-FP8
Meta405B variant of Llama 3.1 models. Llama 3.1 uses a decoder-only transformer architecture and new tokenizer that provides improved model performance.Fine-tunable -
foundation model
FeaturedText Generation
Meta-Llama-3.1-8B-Instruct
Meta8B instruction tuned variant of Llama 3.1 models. Llama 3.1 uses a decoder-only transformer architecture and new tokenizer that provides improved model performance.Fine-tunable -
foundation model
FeaturedText Generation
Meta-Llama-3.1-70B-Instruct
Meta70B instruction tuned variant of Llama 3.1 models. Llama 3.1 uses a decoder-only transformer architecture and new tokenizer that provides improved model performance.Fine-tunable -
foundation model
FeaturedText Generation
Meta Llama 3.2 3B Instruct
Meta3B instruction tuned variant of Llama 3.2 models. Llama 3.2 uses a decoder-only transformer architecture and new tokenizer that provides improved model performance.Fine-tunable -
foundation model
FeaturedText Generation
Meta Llama 3.2 1B Instruct
Meta1B instruction tuned variant of Llama 3.2 models. Llama 3.2 uses a decoder-only transformer architecture and new tokenizer that provides improved model performance.Fine-tunable -
foundation model
FeaturedVision Language
Meta Llama 3.2 11B Vision Instruct
Meta11b instruction-tuned variants of Llama 3.2 models that supports both text and image as input.Deploy only -
foundation model
FeaturedText Generation
Llama 2 13B
Meta13B variant of Llama 2 models. Llama 2 is an auto-regressive language model that uses an optimized transformer architecture. Llama 2 is intended for commercial and research use in English. It comes in a range of parameter sizes—7 billion, 13 billion, and 70 billion—as well as pre-trained and fine-tuned variations.Fine-tunable -
foundation model
FeaturedText Generation
Llama 3
MetaLlama three from Meta comes in two parameter sizes — 8B and 70B with 8k context length — that can support a broad range of use cases with improvements in reasoning, code generation, and instruction following.
Deploy Only -
foundation model
FeaturedText Generation
Llama 2 70B
Meta70B variant of Llama 2 models. Llama 2 is an auto-regressive language model that uses an optimized transformer architecture. Llama 2 is intended for commercial and research use in English. It comes in a range of parameter sizes—7 billion, 13 billion, and 70 billion—as well as pre-trained and fine-tuned variations.Fine-tunable -
foundation model
FeaturedText Generation
Llama 2 7B
Meta7B variant of Llama 2 models. Llama 2 is an auto-regressive language model that uses an optimized transformer architecture. Llama 2 is intended for commercial and research use in English. It comes in a range of parameter sizes—7 billion, 13 billion, and 70 billion—as well as pre-trained and fine-tuned variations.Fine-tunable -
foundation model
FeaturedText Generation
Llama 2 70B Chat
Meta70B dialogue use case optimized variant of Llama 2 models. Llama 2 is an auto-regressive language model that uses an optimized transformer architecture. Llama 2 is intended for commercial and research use in English. It comes in a range of parameter sizes—7 billion, 13 billion, and 70 billion—as well as pre-trained and fine-tuned variations.Fine-tunable