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Write a customer reviewAmazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. With Amazon SageMaker, all the barriers and complexity that typically slow down developers who want to use machine learning are removed. The service includes models that can be used together or independently to build, train, and deploy your machine learning models.
No matter your level of expertise as a developer, Cohere's model empowers you to effortlessly incorporate it into your application, enabling you to accomplish text generation tasks with unparalleled speed and exceptional performance. With just a few clicks, you can deploy Cohere's Generate model using SageMaker, all while keeping your data within the AWS platform. Utilize Cohere's Generate model to invigorate your copywriting, named entity recognition, paraphrasing, or summarization efforts and take them to the next level.
Cohere's Command-Light is a generative model that responds well with instruction-like prompts. This model provides customers with an unbeatable balance of quality, cost-effectiveness, and low-latency inference. Specifically designed for tasks that require rapid and accurate responses, such as question answering, copywriting, or paraphrasing, Cohere's Command-Light model sets the standard for high performance. And with our intuitive SDK, unlocking the full potential of LLMs for your applications has never been easier.
Our optimized containers enable low latency inference on a diverse set of hardware accelerators available on AWS providing different cost and performance points for Sagemaker customers.
Text Generation, Generative AI, Content Generation, AI Text Writer, Copy Writing, Summarization, Summary Generator, Entity Extraction
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The model accepts JSON requests with parameters that can be used to control the generated text. See examples and fields descriptions below.
{
"prompt": "Write a LinkedIn post about starting a career in tech:",
"max_tokens": 100,
"temperature": 0.8,
"return_likelihood": "GENERATION",
}
The output is a JSON object that has the generated text along with likelihoods of tokens, if requested. See example json.
{
"generations": [
{
"text": "I just graduated college and started a new career in tech. I'm so excited to be on this journey and I can't wait to see where it takes me! I'm confident that I have the skills and passion to succeed and I'm looking forward to the challenges and opportunities that lie ahead. I'm also excited to connect with other like-minded individuals who are passionate about this industry and the potential it has to shape the future. Let's go!"
}
],
"prompt": "Write a LinkedIn post about starting a career in tech:"
}
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