Meta’s Muse Spark Open now available in Amazon SageMaker JumpStart | Amazon Web Services

Muse Spark Open, Meta’s coding-optimized open-weight model, is now available in Amazon SageMaker JumpStart, the machine learning (ML) hub of Amazon SageMaker AI. The model was trained on real developer workflows, such as multi-file refactors and long debugging sessions, and it supports a context window of 1 million tokens. You can deploy it to a SageMaker AI endpoint in your own AWS account in a few steps, without managing the serving infrastructure yourself. SageMaker JumpStart offers two variants: BF16 (Muse-Spark-Open-816B-A42B) and NVFP4 quantized (Muse-Spark-Open-816B-A42B-NVFP4).

In this post, we introduce Muse Spark Open and show you how to deploy the model with Amazon SageMaker Studio and the SageMaker Python SDK, send it a first inference request, and clean up.

Overview of Muse Spark Open

Meta describes Muse Spark Open as a coding-optimized model trained for agentic coding workflows, with a focus on first-attempt accuracy, tool calling, and understanding...

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