Dave Martins

💻Software Engineering Specialist
🎸Guitarist @insanedriverid

[Insane Driver @InsaneDriverID]

The Sun Will Rise is out now! 🌅

This song carries a powerful and uplifting message: no matter the challenges we face, everything will be okay. It's a reminder to fight through the darkness and let the light guide us back to life.

Listen now anywhere: https://t.co/MMP2scoUFz

[Insane Family @InsaneFamilyID]

🚨 NEW SONG ALERT! 🚨
Insane Driver just dropped The Sun Will Rise, a beautiful acoustic track you don’t want to miss! 🌅🎸
🎧 Listen now on Spotify: https://t.co/kXxh7ddwV3

Let’s make some noise!🖤🔥
👉 Follow Insane Driver: @InsaneDriverID

#rock #acoustic #listentothis

[juju 💰 @ayeejuju]

the Simpsons really predicted everything

[Troll Football @TrollFootball]

Kroos surpassed Barcelona's Champions League titles and retired

[Gabriel da Codecon @codecondev]

ESCOVANDO BITS - InnerSource: desenvolvimento colaborativo como alavanca em Tecnologia https://t.co/i1vEDC7sCT

[Gabriel da Codecon @codecondev]

Semana que vem, tem live!

Vamos conversar com a Daniele e o Deivid do Itaú para entender como eles usam innerSource para aumentar a colaboratividade entre times e alavancar tech dentro da empresa.

Inscreva-se para ser avisado: https://t.co/ClBsaYgwUd

[Sam Lambert @samlambert]

Uber does 40 million reads a second on tens of PB of data.

Guess which database they use?

https://t.co/Z9VptZPogZ

[São Paulo FC @SaoPauloFC]

ÉÉÉÉ CAMPEÃOOOO!! 🏆

Tem que respeitar! AQUI É SÃO PAULO!

#SuperCampeãoDeTudo
#VamosSãoPaulo 🇾🇪

[São Paulo FC @SaoPauloFC]

TABU É O CALLERI!!!

#TocaNoCalleri
#VamosSãoPaulo 🇾🇪

[Santiago @svpino]

LoRA is a genius idea.

To understand the fine-tuning of Large Language Models, you must understand how LoRA works.

By the end of this post, you'll know everything important about how it works.

Large Language Models are good generalists, but they have little specialization. We train them in many different tasks, so they know a bit about everything but not enough about anything.

Think of a kid who can play three different sports at a high level. While he can be proficient across the board, he won't get a scholarship unless he specializes. That's how the kid can reach his full potential.

We can do the same with these large models. We can train them to solve a particular task and nothing else.

We call this process "fine-tuning." We start with everything the model knows and adjust its knowledge to help it improve on the task we care about.

Fine-tuning is revolutionary, but it's not free.

Fine-tuning a large model takes time, care, and lots of money. Many companies can't afford the process. Some can't pay for the hardware. Some can't hire people who know how to do it. Most companies can't do either.

That's where LoRA comes in.

We realized we could approximate a large matrix of parameters using the product of two smaller matrices. There was a lot of wasted space within these large models. What would happen if we find a new, more optimal representation?

Did you ever buy a map at a gas station? Giant pages showing every small road, path, and lake around you. They were exhaustive but hard to navigate. These are like parameters in a large model.

LoRA turns a gas station map into a cartoon treasure map. Every useless parameter is gone. Only two roads, a palm tree, and a cross pointing at the treasure. We don't need to fine-tune the entire model anymore. We can only focus on the small treasure map that LoRA gives us.

It's a mind-blowing trick.

We can train the small approximation matrices from LoRA instead of fine-tuning the entire model. LoRA is cheaper, faster, and uses less memory and storage space.

You can also merge the approximation matrices with the model during deployment time. They work like simple adapters. You load up the one you need to solve a problem and use a different one for the next task.

Then, we have QLoRA, which makes the process much more efficient by adding 4-bit quantization. QLoRA deserves its own separate post.

The team at @monsterapis has created an efficient no-code LoRA/QLoRA-powered LLM fine-tuner.

What they do is pretty smart:

They automatically configure your GPU environment and fine-tuning pipeline for your specific model. For example, if you want to fine-tune Mixtral 8x7B on a smaller GPU, they will automatically use QLoRA to keep your costs down and prevent memory issues.

The @monsterapis platform specializes in no-code LoRA-powered fine-tuning. It's the fastest and most affordable offering for fine-tuning models in the market. They sponsored me and gave me 10,000 free credits for anyone who uses the code "SANTIAGO" in their dashboard:

https://t.co/DBsB9EXgYR

If you want to read their latest updates, get free credits and special offers, join their Discord server: https://t.co/oecoxEec14

TL;DR:

• Traditional fine-tuning trains the entire model. It requires a complex setup, higher memory, and expensive hardware.

• LoRA: Trains a small portion of the model. It's faster, requires much less memory, and affordable hardware.

• QLoRA: Much more efficient than LoRA, but it requires a more complex setup.

• No-code fine-tuning with LoRA/QLoRA: The best of both worlds. Low cost and easy setup.

[Sergio Lopes @sergiolopessp]

E aqui entra um trabalho de pesquisa publicado recentemente que explora os potenciais riscos, limitações, desafios e oportunidades do GenAI no campo da segurança cibernética e privacidade.

Read more 👉 https://t.co/QQvu0KtDYJ

#GenerativeIA #Cybersecurity #ChatGPT

[Dave Martins @dmartinsID]

🚀 Novo artigo no Medium do @itau! Exploro como praticamos o #Innersource dentro das nossas Tech Stacks.

Confira: https://t.co/FTWbM28Rng

#InnerSource #Colaboração #Itaú #Tecnologia #TechStacks #Cultura

[Juan Antonio Breña 🇪🇸🇪🇺 @juanantoniobm]

Benchmark JDBC connectors and Java 21 virtual threads https://t.co/DTFP6i25MB

[Gerrit Grunwald 🇺🇦🇹🇼✌🏻 @hansolo_]

#SpringBoot 3.2 + #CRaC …made some tests, check it out here…https://t.co/2JM3WI2dyX #OpenJDK #Java

[Josh Long @starbuxman]

Today is the day! Today, @SpringBoot 3.2 comes out! And I can’t hardly wait to get my mits on those GA bits!

- CRaC
- Loom
- JdbcClient
- RestClient
- Spring for @apache_pulsar
- observability improvements
- Java 21
- SSL improvements
- GraalVM/AOT improvements

[Spring Framework @springframework]

Spring Framework 6.1 goes GA! 🍃

- Embracing JDK 21 LTS
- Virtual Threads (Project Loom)
- JVM Checkpoint Restore (Project CRaC)
- Resource Lifecycle Management (revisited)
- Data Binding and Validation (revisited)
- New RestClient and JdbcClient APIs

https://t.co/pCihy0auBu

[Maciej Walkowiak 🍃 @maciejwalkowiak]

Spring Framework 6.1 is released! 🎉

https://t.co/xL3xj5IZ6m

Congrats to the @springcentral team!

6.1 is a foundation for Spring Boot 3.2 that will be released on the 23rd of November. It comes with quite a few exciting changes that i'll briefly explain here (1/7)

[Michael Tremonti @TremontiMichael]

It’s that day! #TremontiTuesday

[Maciej Walkowiak 🍃 @maciejwalkowiak]

"Imagine that you have a website that has 10,000 users making database requests simultaneously (...). How big should your connection pool be? You might be surprised that the question is not how big but rather how small!"

https://t.co/9qUjxQyDkw

[Maciej Walkowiak 🍃 @maciejwalkowiak]

📢 New project: Spring Boot Startup Report

This tiny library generates an interactive @springboot application startup report that lets you understand what contributes to the application startup time and perhaps helps to optimize it.

👉 https://t.co/3O772OXfFf