Slides: From 46% to 90%: Fine-Tuning Tiny LLMs for On-Device Agents — Cormac Brick, Google

Source Video

From 46% to 90%: Fine-Tuning Tiny LLMs for On-Device Agents — Cormac Brick, Google

Relationship To World's Fair 2026

These slides are extracted from a public AI Engineer YouTube video connected to World's Fair 2026. Speaker-matched clips are supporting context unless later confirmed as exact session recordings; official livestream recordings are day-level/event-level source material.

Related Scheduled Sessions

Extracted Slides

slide-001.jpg

OCR text:

€3 Braintrust €} WorkOS OpenAl

slide-002.jpg

OCR text:

TLMs:TinyLLMsand

AgentsonEdgeDevices

AIE

Bringing state-of-the-artagenticskilstothe

edgewithopenmodels

Cormac Brick, Principal Engineer, Google Al Edge

GoogleDeepMind

slide-003.jpg

OCR text:

n Agenda 00 Al Edge. SLMs & TLMs

10 Agent Skills locally on Android & iOS

* Sd

oY a a 20 TLM workflow

ey 30 TLMsin Action

Engineering the future of Al

slide-004.jpg

OCR text:

SystemlevelGenAl

AIE

Gemini NanowithAICore

Apple IntelligenceWriting

summarizationonAndroid

toolsonios

Engineering the future of Al

AIEng

slide-005.jpg

OCR text:

NEW

Agent Skills & Gemma 4 E2B &

(‘System GenAl' uses AlCore when avail

Pee 3 .

ae B92 59 eam

. ; Bebe Si.

a a oes Poe S360

nd ~*

OO

Masi ones

Woy pp bore

Oink

Oy 8

P¥AENESA Veacine

WOES cone ve

Tek Can

BRE Gare:

wre

i

. | Al Engineer |

EUROPE

slide-006.jpg

OCR text:

Example Skills - Restaurant Roulette

ere g ;

a

ul : | f

Engineering the future of Al

slide-007.jpg

OCR text:

Example Skills - Restaurant Roulette

Cs Ree Noah aaa

ae

Engineering the future of Al

slide-008.jpg

OCR text:

FunctionGemma TuningLab:Fine-Tuning

atou

tdetalls

Signin with Hugging Face

AIE

2.Tral

Tool Schema&Datalmport

Idas

Step 2:UpldDataIOptin

To tral

onyour

data,up

adaCSv

DropFile Here

Engineering the future of Al

AIEn

slide-009.jpg

OCR text:

TO Sark a |

a -— PC

Sen

i y)

i _ ee He PE ar

q - =e ie

Ey

ach aA

F +n

| ene

1° see

D Lt

nad ;

: ins —

: : “— . ,

Slide-Derived Subjects To Review

Subject extraction uses video title, related session titles/descriptions, transcript context, and OCR text when available. OCR is best-effort and should be reviewed against the embedded slide images.