The Llama Guard model is now available within SageMaker JumpStart, an ML hub of Amazon SageMaker providing access to foundation models, including the Llama Guard model, with input and output safeguards for large language models (LLMs) and extensive content moderation capabilities. The model is intended to provide developers with a pretrained model to help defend against generating potentially risky outputs and can be used as a supplemental tool for integration into various use cases such as chatbots, content moderation, customer service, social media monitoring, and education. SageMaker JumpStart provides access to a variety of models from popular model hubs, enabling quick and easy access to foundation models, and allows for deployment and usage of these models at scale.
The Llama Guard model can be discovered and accessed in SageMaker JumpStart using SageMaker Studio, where you can choose from a variety of Llama model variants including Llama Guard, Llama-2, and Code Llama, and view details such as license, data used for training, and instructions for usage. Once deployed, you can specify the SageMaker model hub model ID and model version to use when deploying Llama Guard. In this code, the default instance ml.g5.2xlarge is used for the inference endpoint when deploying the model.
SageMaker JumpStart also provides the Llama-2 7B Chat model endpoint for conversational chat, which can be used in combination with Llama Guard for moderation of input and output text. Llama Guard can be used to format chat messages and guard messages to ensure moderated conversation safety, and provides guardrails for inputs and outputs from LLMs.
It is important to note that after testing the endpoints, it is recommended to delete the SageMaker inference endpoints and the model to avoid incurring charges.
For more information and to try out Llama Guard and other foundation models in SageMaker JumpStart, please refer to the official Amazon SageMaker documentation.
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Llama Guard Model Now Available in Amazon SageMaker JumpStart
Today, we are thrilled to announce the availability of the Llama Guard model for customers using Amazon SageMaker JumpStart. Llama Guard provides input and output safeguards in large language model (LLM) deployment. It is a part of Meta’s Purple Llama initiative, which features open trust and safety tools and evaluations to help developers build responsibly with AI models.
Practical Solutions and Value
The Llama Guard model offers practical solutions for developers to integrate into their own mitigation strategies, such as for chatbots, content moderation, customer service, social media monitoring, and education. By passing user-generated content through Llama Guard before publishing or responding to it, developers can flag unsafe or inappropriate language and take action to maintain a safe and respectful environment.
Foundation Models in SageMaker
SageMaker JumpStart provides access to a range of models from popular model hubs, including Hugging Face, PyTorch Hub, and TensorFlow Hub, enabling quick start with foundation models. These models are typically trained on billions of parameters and adaptable to a wide category of use cases, such as text summarization, digital art generation, and language translation.
Discover the Llama Guard Model in SageMaker JumpStart
You can access the Llama Guard model through SageMaker JumpStart in the SageMaker Studio UI and the SageMaker Python SDK. SageMaker Studio is an integrated development environment (IDE) that provides a single web-based visual interface where you can access purpose-built tools to perform all ML development steps, from preparing data to building, training, and deploying your ML models.
Deploy the Model with the SageMaker Python SDK
You can deploy the Llama Guard model using the SageMaker Python SDK. The model is deployed using the Text Generation Inference (TGI) deep learning container. Inference requests support various parameters, allowing for customization and flexibility in generating text.
Moderate a Conversation with Llama-2 Chat
You can deploy a Llama-2 7B Chat model endpoint for conversational chat and then use Llama Guard to moderate input and output text coming from Llama-2 7B Chat. This allows for safe and responsible conversational interactions with users.
Clean Up
After testing the endpoints, it is important to delete the SageMaker inference endpoints and the model to avoid incurring charges.
Conclusion
As AI continues to advance, it’s critical to prioritize responsible development and deployment. Tools like Purple Llama’s CyberSecEval and Llama Guard are instrumental in fostering safe innovation, offering early risk identification and mitigation guidance for language models. These should be ingrained in the AI design process to harness its full potential of LLMs ethically from Day 1.
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