Alex Reibman Profile picture
Aug 27 10 tweets 6 min read Twitter logo Read on Twitter
LLM Fine tuning is here!

San Francisco’s top AI engineers came together to see what’s possible with fine-turning and only 4 hours of hacking.

Here’s an exclusive what we saw at the “Anything But Wrappers” hackathon (🧵):
Image
Image
1/ Fine tuning on emails

Harrison fine-tuned GPT-3.5 on his emails to politely reject notes from VCs and respond to cool guys like @swyx

@hwchase17 @LangChainAI
2/ “Launching a bunch of cloud resources all at once to rack up a huge bill”

Plus, a shoutout to alphachive, an internal tool used by researchers at Stanford to rate papers

🏅 Winner most expensive
3/ Crypto salesman

A model fine tuned to answer questions but also shill crypto with every response
4/ “What if we distribute?”

You can train a LoRA on 128 GPUs on different machines with only 50% overhead

Cool experiment by @ericyu3_
5/ Llama Linter

Fine tuned model that learned to lint JavaScript better than GPT-3.5 and even GPT-4

@rachpradhan @chinzonghan3101 1

🏅Most practical (tie)

6/ Creative ASCII

Fine-tuned GPI-3 that generates ASCII art from any text prompt

@jamesmurdza

🏅 Most creative

7/ Text to Prompt

Give a simple text description and have a fine tuned model turn it into a prompt suitable for DALLE or Midjourney

@akshayvkt @realyogeshdarji
Image
Image
8/ Fine tuned LLaMA as supervised learner

Instead of training a small transformer to classify queries, just give them to LLaMA

🏅 Most practical (tie)

That’s all for this time

Follow me @AlexReibman for more live reports on the SF hacker ecosystem

And huge thanks to the sponsors:
@AmplifyPartners @CRV @brevdev @CeloOrg @latentspacepod @LangChainAI @replicatehq @anyscalecompute @metaphorsystems @_FireworksAI @phindsearch

• • •

Missing some Tweet in this thread? You can try to force a refresh
 

Keep Current with Alex Reibman

Alex Reibman Profile picture

Stay in touch and get notified when new unrolls are available from this author!

Read all threads

This Thread may be Removed Anytime!

PDF

Twitter may remove this content at anytime! Save it as PDF for later use!

Try unrolling a thread yourself!

how to unroll video
  1. Follow @ThreadReaderApp to mention us!

  2. From a Twitter thread mention us with a keyword "unroll"
@threadreaderapp unroll

Practice here first or read more on our help page!

More from @AlexReibman

Aug 8
Outside Lands is Silicon Valley’s hottest music festival.

But something huge just happened.

Artists, musicians, and engineers from all over the world are here to show what’s possible at the cutting edge of AI and music

Live tweeting the demos from the @OutsideLLMs hackathon🧵: Image
1/ Spacial Gen

Digital concert experiences

Artist deploys an AR experience through an NFT and the listener is guided to a geofenced experience area https://t.co/Amv1suM24ctwitter.com/i/web/status/1…

Image
Image
2/ YungGPT

Chat with a GPT Rapper https://t.co/SvjPO3tMBvtwitter.com/i/web/status/1…
Image
Read 24 tweets
Aug 4
Every week, SF’s top AI engineers and startup founders meet up and show what’s new in AI.

No pitches. No grifting. Just code.

Here’s what some of SF’s best hackers are up to this week at @cerebral_valley 🧵 Image
1/ Insight AI

Trying to validate an idea? Just pick your topic and ideal customer persona, and generate user interviews surveys automatically

Mom test in a box
@prasann_pandya https://t.co/oicG2RHUustwitter.com/i/web/status/1…
Image
2/ What’s going on?! (name pending)

Ever seen a cool event on a website but couldn’t find the calendar invites?

This chrome extension automatically scrapes events on any website and adds them to your calendar. Way more convenient than adding them manually https://t.co/WLfAJCZgsPtwitter.com/i/web/status/1…
Image
Read 8 tweets
Jul 30
OpenAI has new competition.

And it’s name is Claude.

Live tweeting the finalists of the first ever @AnthropicAI Hackathon at @cerebral_valley

Meet the best of the 200+ hackers demonstrating what’s now possible with 100k context windows (1/n):
Image
Image
1/ Mythbusters AI

Plugging in Claude with RAG fact checkers to identified confabulations (aka lies) during presidential debates

@pascalwieler @ryboticc @tonyadastra https://t.co/1Bff1fsXqytwitter.com/i/web/status/1…
Image
2/ Dr. Claude

Combining LLMs with evidence-backed diagnoses using Monte Carlo Tree Search (MCTS).

Identify the likelihood of a disease given patient symptoms using data from vetted medical records
@WianStipp @fadynakhla_ @suKruKiymaCi

🥉 3rd place winner https://t.co/4lBmdiJ3Fntwitter.com/i/web/status/1…
Image
Read 8 tweets
Jul 23
SF’s most talented:
-AI researchers
-data scientists
-software engineers

All came to a mansion see what’s possible with AI agents.

This stuff is insane.

Live tweeting the demos from the AI Agents hackathon at
@agihouse_org w/ @Wing_VC @MultiON_AI 🧵: Image
1/

High Flyers

There are 90+ airlines with loyalty programs. It’s a huge pain in the neck to register for their frequent flyer programs

We built an agent that goes to all of them and automatically creates accounts for you using
@multi_on

feat. Me and
@thymeshirl94824
:)
2/

RealChar

Create virtual characters with user personas by scraping the web with MultiON to understand their personas

Winner 🥇
Read 25 tweets
Oct 13, 2021
The #NobelPrize in economics was just awarded to 3 top economists. #EconTwitter seems to be over it, but the data science/ML community is totally missing out!

Here's why Data Scientists should start paying attention and what they can take away 🧵
The prize was awarded to David Card, @metrics52, and Guido Imbens for their monumental contributions to statistical methodology and causal inference.

They used and developed strategies that were a true paradigm shift bridging the gap between data and causation in economics
One part of the prize went to David Card from UC Berkeley.

Card is most well-known for his famous minimum wage study that paradoxically revealed that an increase in the minimum wage did *not* reduce employment. How?

The study applied a strategy called Difference in Differences
Read 11 tweets
Oct 6, 2021
Big tech teams win because they have the best ML Ops. These teams
- Deploy models at 10x speed
- Spend more time on data science, less on engineering
- Reuse rather than rebuild features

How do they do it? An architecture called a Feature Store. Here's how it works
🧵 1/n Image
In almost every ML/data science project, your team will spend 90-95% of the time building data cleaning scripts and pipelines

Data scientists rarely get to put their skills to work because they spend most of their time outside of modeling Image
Enter: The Feature Store

This specialized architecture has:
- Registry to lookup/reuse previously built features
- Feature lineages
- Batch+stream transformation pipelines
- Offline store for historical lookups (training)
- Online store for low-latency lookups (live inferences) Image
Read 13 tweets

Did Thread Reader help you today?

Support us! We are indie developers!


This site is made by just two indie developers on a laptop doing marketing, support and development! Read more about the story.

Become a Premium Member ($3/month or $30/year) and get exclusive features!

Become Premium

Don't want to be a Premium member but still want to support us?

Make a small donation by buying us coffee ($5) or help with server cost ($10)

Donate via Paypal

Or Donate anonymously using crypto!

Ethereum

0xfe58350B80634f60Fa6Dc149a72b4DFbc17D341E copy

Bitcoin

3ATGMxNzCUFzxpMCHL5sWSt4DVtS8UqXpi copy

Thank you for your support!

Follow Us on Twitter!

:(