Google GLaM

Paid

Build, deploy, and monitor models for specific applications with in-context learning.

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Inputs: textOutputs: text

Type

Saas

Company

Google Research

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About Google GLaM

Google GLaM is an innovative machine learning platform designed to help developers rapidly and efficiently build in-context learning models. By leveraging the latest advances in artificial intelligence, Google GLaM provides developers with powerful tools to quickly create, test, and deploy sophisticated models. The platform enables developers to quickly create complex models that are better suited to their application’s needs. Google GLaM makes it easy to quickly build and deploy in-context models with minimal effort. With its intuitive user interface, developers can quickly and easily create, test, and deploy models in a fraction of the time it would take them to do so manually. With its powerful tools, developers can quickly create models that are more accurate and faster than traditional methods. Additionally, Google GLaM helps developers to monitor the performance of their models over time and make necessary adjustments when needed.

Key Features

Quickly build and deploy in-context models.

Create complex models better suited to application needs.

Monitor model performance and make adjustments.

Pros & Cons

Pros

Efficient scaling—sparsity limits computation despite huge parameter count
Outperforms GPT-3 in learning efficiency on multiple benchmarks
Trained on carefully filtered high-quality text data
Architecture allows specialization of experts for different input types

Cons

Model still requires significant computational resources to train and serve
Not publicly available as a service or API—research prototype only
Complex MoE architecture may be harder to deploy and tune than dense models

Best For

Quickly build and deploy in-context models.Create complex models better suited to application needs.Monitor model performance and make adjustments.

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FAQ

What is GLaM?

GLaM (Generalist Language Model) is a mixture-of-experts language model from Google Research with 1.2 trillion parameters, but it activates only 97 billion per token using sparsity for efficiency.

How does GLaM compare to GPT-3?

GLaM achieves competitive performance to GPT-3 (175B) across 29 NLP benchmarks while being more efficient in computation and energy use due to its sparse MoE architecture.

What kind of data was GLaM trained on?

GLaM was trained on a high-quality 1.6-trillion-token dataset consisting of filtered web pages, books, and Wikipedia, using a text quality filter trained on Wikipedia and books.

How does the mixture-of-experts work in GLaM?

Each input token is dynamically routed by a gating network to the two most appropriate experts out of 64 per MoE layer, allowing the model to use specialized submodels while keeping computation low.