Fine-tune Code Llama on Amazon SageMaker JumpStart

Favorite Today, we are excited to announce the capability to fine-tune Code Llama models by Meta using Amazon SageMaker JumpStart. The Code Llama family of large language models (LLMs) is a collection of pre-trained and fine-tuned code generation models ranging in scale from 7 billion to 70 billion parameters. Fine-tuned

Read More
Shared by AWS Machine Learning March 18, 2024

Optimize price-performance of LLM inference on NVIDIA GPUs using the Amazon SageMaker integration with NVIDIA NIM Microservices

Favorite NVIDIA NIM microservices now integrate with Amazon SageMaker, allowing you to deploy industry-leading large language models (LLMs) and optimize model performance and cost. You can deploy state-of-the-art LLMs in minutes instead of days using technologies such as NVIDIA TensorRT, NVIDIA TensorRT-LLM, and NVIDIA Triton Inference Server on NVIDIA accelerated

Read More
Shared by AWS Machine Learning March 18, 2024

Open Source AI Definition – Weekly update Mar 18

Favorite Comments on draft 0.0.6 from the forum Point raised by participant that training data has been listed both as optional and a precondition. This might cause confusion as it is unclear whether we should have the right to access training data or know what training data was used for

Read More
Shared by voicesofopensource March 18, 2024

Transform one-on-one customer interactions: Build speech-capable order processing agents with AWS and generative AI

Favorite In today’s landscape of one-on-one customer interactions for placing orders, the prevailing practice continues to rely on human attendants, even in settings like drive-thru coffee shops and fast-food establishments. This traditional approach poses several challenges: it heavily depends on manual processes, struggles to efficiently scale with increasing customer demands,

Read More
Shared by AWS Machine Learning March 15, 2024

Best practices to build generative AI applications on AWS

Favorite Generative AI applications driven by foundational models (FMs) are enabling organizations with significant business value in customer experience, productivity, process optimization, and innovations. However, adoption of these FMs involves addressing some key challenges, including quality output, data privacy, security, integration with organization data, cost, and skills to deliver. In

Read More
Shared by AWS Machine Learning March 14, 2024