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Google’s Gemini technical report describes a multimodal model family built through a cross-Google effort. Its contributor section names technical and program leads, groups contributors by role, and thanks people who helped prepare and review the report. The report also says that names within each role are not ordered by contribution.
What the Gemini report covers
The Gemini Team introduced its work with the description: “We present Gemini, a family of highly capable multimodal models developed at Google.” The report, “Gemini: A Family of Highly Capable Multimodal Models”, was posted on December 19, 2023. It presents Gemini 1.0 as a family trained jointly on image, audio, video, and text data.
Three models for different constraints
| Model | Intended role in the report |
|---|---|
| Gemini Ultra | Highly complex tasks |
| Gemini Pro | Performance and deployability at scale |
| Gemini Nano | On-device applications |
These descriptions are the report’s framing of the models, not a current product comparison.
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The report presents Gemini as a cross-Google effort involving Google DeepMind, Google Research, Bard/Assistant, Knowledge and Information, Core ML, Cloud, Labs, and other groups. Its contributor material separates people into role categories rather than treating the list as one undifferentiated author ranking.
Named leadership roles
- Overall technical leads: Jeffrey Dean and Oriol Vinyals, identified as making equal contributions.
- Overall post-training lead: Slav Petrov.
- Program leads: Demis Hassabis and Koray Kavukcuoglu.
- Gemini App program leads: Amar Subramanya and Sissie Hsiao.
Beyond those named roles, the report lists leads, core contributors, contributors, program leads, an overall post-training lead, and overall technical leads. It explicitly states that ordering within each role does not indicate contribution order, so the sequence of names should not be read as a ranking.
Acknowledgments
The report thanks named leads for preparing the report and acknowledges reviewers and colleagues for discussions and feedback. These statements document how the report describes contributions and thanks; they do not independently establish the precise work performed by each person beyond the report’s own role definitions.
How to read the report’s benchmark claims
The Gemini Team’s 2023 report says Gemini Ultra advanced the state of the art on 30 of the 32 benchmarks it examined. In its MMLU discussion, it reports 90.04% accuracy. Both figures are results reported by the paper for its evaluations, not a current independent comparison of Gemini products.
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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsHow the report described access
The report distinguished chat-focused Gemini Apps models from developer-focused Gemini API models. In its 2023 description, it named Gemini and Gemini Advanced among app experiences, and Google AI Studio and Cloud Vertex AI as developer access paths. These are historical descriptions in the paper; the report does not establish present-day availability, product names, or access terms.
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