With SageMaker JumpStart, ML practitioners can choose from a broad selection of publicly available foundation models. Regardless of which version of the model a developer uses, the responsible use guide from Meta can assist in guiding additional fine-tuning that may be necessary to customize and optimize the models with appropriate safety mitigations.

The tuned models are intended for assistant-like chat, whereas pre-trained models can be adapted for a variety of natural language generation tasks. Llama 2 was pre-trained on 2 trillion tokens of data from publicly available sources.

According to Meta, the tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align to human preferences for helpfulness and safety. It comes in a range of parameter sizes-7 billion, 13 billion, and 70 billion-as well as pre-trained and fine-tuned variations. Llama 2 is intended for commercial and research use in English. Llama 2 is an auto-regressive language model that uses an optimized transformer architecture. In this post, we walk through how to use Llama 2 models via SageMaker JumpStart. You can easily try out these models and use them with SageMaker JumpStart, which is a machine learning (ML) hub that provides access to algorithms, models, and ML solutions so you can quickly get started with ML. Fine-tuned LLMs, called Llama-2-chat, are optimized for dialogue use cases. The Llama 2 family of large language models (LLMs) is a collection of pre-trained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. Today, we are excited to announce that Llama 2 foundation models developed by Meta are available for customers through Amazon SageMaker JumpStart.

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