LearnLM-Tutor
No estimate
No hardware requirements for this model
The weights for this model have not been published, so it cannot be downloaded or run on your own hardware at any size. It is reachable only through its provider, and no graphics card changes that.
On record
Full specification
Everything on record for this model. Most of it describes how it was trained rather than how it runs — useful context for judging how much work went into it, and how it compares with models built at a different scale.
Origin
Who built this model, where, and when it was published.
- Organisation
- Google Research,Google DeepMind,Google,Arizona State University,Lund University,University of Oxford
- Organisation type
- Industry,Industry,Industry,Academia,Academia,Academia
- Country
- United States of America, Sweden, United Kingdom of Great Britain and Northern Ireland
- Published
- 14 May 2024
- Authors
- Irina Jurenka, Markus Kunesch, Kevin R. McKee, Daniel Gillick, Shaojian Zhu, Sara Wiltberger, Shubham Milind Phal, Katherine Hermann, Daniel Kasenberg, Avishkar Bhoopchand, Ankit Anand, Miruna Pîslar, Stephanie Chan, Lisa Wang§, Jennifer She, Parsa Mahmoudieh, Aliya Rysbek, Wei-Jen Ko, Andrea Huber, Brett Wiltshire, Gal Elidan‡, Roni Rabin, Jasmin Rubinovitz†, Amit Pitaru, Mac McAllister, Julia Wi…
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Language modeling/generation, Chat, Question answering
- Base model
- Gemini 1.0 Ultra
Size
How large the model is and how much data it was trained on. Parameters are the figure that decides whether it fits on a given graphics card.
- Training data
- tokens
Availability
Whether you can obtain the model and run it on your own hardware, which is what decides if any of the graphics-card figures on this page apply.
- Weights
- Closed — provider access only
- Model access
- Hosted access (no API)
- Training code
- Unreleased
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Unknown
Sources
Where this record came from and when it was last checked.
- Reference
- Towards Responsible Development of Generative AI for Education: An Evaluation-Driven Approach
- Last updated
- 28 November 2025
What the numbers mean
What this model is
LearnLM-Tutor was published by Google Research,Google DeepMind,Google,Arizona State University,Lund University,University of Oxford, in United States of America, in May 2024. The organisation is categorised as industry,Industry,Industry,Academia,Academia,Academia.
It works in Language, and is recorded as doing language modeling/generation, Chat, Question answering.
It builds on Gemini 1.0 Ultra, which is why it shares that model's general shape and size.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
Answers
LearnLM-Tutor — common questions
Is LearnLM-Tutor open source?
No. LearnLM-Tutor has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does LearnLM-Tutor have?
No parameter count has been published for LearnLM-Tutor, which is why no memory or speed figure appears on this page.
Who created LearnLM-Tutor?
LearnLM-Tutor was published by Google Research,Google DeepMind,Google,Arizona State University,Lund University,University of Oxford, based in United States of America, categorised as industry,Industry,Industry,Academia,Academia,Academia.
When was LearnLM-Tutor released?
LearnLM-Tutor was published in May 2024. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
What is LearnLM-Tutor used for?
LearnLM-Tutor works in Language, and is recorded as handling language modeling/generation, Chat, Question answering. A model can carry several of each, so these are the areas it was built for rather than a limit on what it will attempt.
What GPU do I need to run LearnLM-Tutor?
None. LearnLM-Tutor is a closed model — its weights were never published, so it cannot be downloaded or run on your own hardware at any price. It is reachable only through its provider.
The other direction
Looking at it from the other side?
This page starts from the model. If you already own a card and want to know everything it will run, start from the hardware instead.