Amazon SageMaker JumpStart 入门

概览

Amazon SageMaker JumpStart 是一个机器学习(ML)中心,可以帮助您加速 ML 之旅。探索如何开始使用内置算法(带有来自模型中心的预训练模型、预训练基础模型和预构建解决方案)满足常见应用场景需要。要开始使用,请参阅可以快速执行的文档或示例 Notebook。

产品类型
文本任务
视觉任务
表格任务
音频任务
多模态
强化学习
Showing results: 1-12
Total results: 567
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  • foundation model

    Featured
    Text Generation

    Meta-Llama-3-70B-Instruct

    Meta
    70B 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

    Featured
    Text Generation

    Meta-Llama-3.1-405B-FP8

    Meta
    405B 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

    Featured
    Text Generation

    Meta-Llama-3.1-8B-Instruct

    Meta
    8B 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

    Featured
    Text Generation

    Meta-Llama-3.1-70B-Instruct

    Meta
    70B 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

    Featured
    Text Generation

    Meta Llama 3.2 3B Instruct

    Meta
    3B 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

    Featured
    Text Generation

    Meta Llama 3.2 1B Instruct

    Meta
    1B 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

    Featured
    Vision Language

    Meta Llama 3.2 11B Vision Instruct

    Meta
    11b instruction-tuned variants of Llama 3.2 models that supports both text and image as input.
    Deploy only
  • foundation model

    Featured
    Text Generation

    Llama 2 13B

    Meta
    13B 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

    Featured
    Text Generation

    Llama 3

    Meta

    Llama 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

    Featured
    Text Generation

    Llama 2 70B

    Meta
    70B 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

    Featured
    Text Generation

    Llama 2 7B

    Meta
    7B 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

    Featured
    Text Generation

    Llama 2 70B Chat

    Meta
    70B 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
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