Jeremy Howard Profile picture
Aug 10, 2023 6 tweets 2 min read Read on X
Now that ChatGPT has rolled out custom instructions to most users, try out this instruction -- it makes GPT 4 far more accurate for me: (Concat the rest of this 🧵 together and put in your custom instruction section) Image
You are an autoregressive language model that has been fine-tuned with instruction-tuning and RLHF. You carefully provide accurate, factual, thoughtful, nuanced answers, and are brilliant at reasoning. If you think there might not be a correct answer, you say so.
Since you are autoregressive, each token you produce is another opportunity to use computation, therefore you always spend a few sentences explaining background context, assumptions, and step-by-step thinking BEFORE you try to answer a question.
Your users are experts in AI and ethics, so they already know you're a language model and your capabilities and limitations, so don't remind them of that. They're familiar with ethical issues in general so you don't need to remind them about those either.
Don't be verbose in your answers, but do provide details and examples where it might help the explanation. When showing Python code, minimise vertical space, and do not include comments or docstrings; you do not need to follow PEP8, since your users' organizations do not do so.
(That last bit is because I mainly want code I can see at a glance I easily play with, and I rarely need comments since I find most code easy to read. You should remove it if you want code you can put straight into a PEP8 codebase and like comments.)

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More from @jeremyphoward

Mar 29
I'm glad @levelsio checked this, but sad our contrib has been erased by later big tech co's. Alec Radford said ULMFiT inspired GPT. ULMFiT's first demo predated BERT.

Today's 3-stage LLM approach of general corpus pretraining and 2 stages of fine-tuning was pioneered by ULMFiT.
There have been many other important contributions, including attention (Bahdanau et al), transformers, RLHF, etc.

But before all this, basically everyone in NLP assumed that each new domain needed a new model. ULMFiT showed that a large pretrained model was actually the key.
I got push-back from pretty much everyone about this. My claim that fine-tuning that model was the critical step to achieving success in NLP was not something people were ready to hear at that time.

I gave many talks trying to convince academics to pursue this direction.
Read 5 tweets
Mar 18
Announcing fasttransform: a Python lib that makes data transformations reversible/extensible. No more writing inverse functions to see what your model sees. Debug pipelines by actually looking at your data.

Built on multi-dispatch. Work w/ @R_Dimm
fast.ai/posts/2025-02-…
We took the `Transform` class out of fastcore, replaced the custom type dispatch system with @ikwess's plum-dispatch, mixed it all together, and voila: fasttransform! :D

To learn about fasttransform, check out our detailed blog post.
fast.ai/posts/2025-02-…
"Manual inspection of data has probably the highest value-to-prestige ratio of any activity in machine learning." --@gdb

Yet we often skip it because it's painful. How do you inspect what your model sees after normalization, resizing & other transforms?
Read 10 tweets
Feb 5
Wow, actual grown men are still doing the "I asked the LLM about itself and it said" thing.

In 2025.

Folks, LLMs don't know anything about how they themselves are built or deployed, unless they've been explicitly programmed with that information (which they almost never are). Image
If you are using a Chinese cloud based service hosted in China, then your data will be sent to a server in China.

Because that's how computers work.

It's not a conspiracy.
I hadn't heard of this guy before, but apparently he'll teach you how to learn AI.

Cool. Image
Read 4 tweets
Jan 25
I've recently been surprised to discover that a few of my friends are choosing to use nicotine to help them with focus, even though they are not ex-smokers.

I decided to look into it, and it turns out that there are documented health benefits of nicotine for some people. 🧵
I specifically looked into nicotine for ADHD, since, at least among children, ADHD and giftedness go hand in hand statistically (which would apply in adulthood too), and because focus was mention as an area where nicotine can be helpful.
There is a great overview below. But "Very surprisingly, there are… no further… studies.

Research into active ingredients… is expensive.

In addition, nicotine has a very poor image… which impairs its marketability"
adxs.org/en/page/192/ni…
Read 12 tweets
Dec 19, 2024
I'll get straight to the point.

We trained 2 new models. Like BERT, but modern. ModernBERT.

Not some hypey GenAI thing, but a proper workhorse model, for retrieval, classification, etc. Real practical stuff.

It's much faster, more accurate, longer context, and more useful. 🧵 Image
ModernBERT is available as a slot-in replacement for any BERT-like model, with both 139M param and 395M param sizes.

It has a 8192 sequence length, is extremely efficient, is uniquely great at analyzing code, and much more. Read this for details:
huggingface.co/blog/modernbert
Seven months ago, @bclavie kicked things off, and soon @benjamin_warner & @antoine_chaffin joined him as project co-leads. I don't think anyone quite knew what we were getting in to…

It turns out that training a new, SoTA model from scratch is actually pretty hard. Who knew? 🤷 Image
Read 23 tweets
Nov 16, 2024
Oh wow. This is gonna be super tricky to figure out what to do now.

There isn’t any easy automated way to install a full deep learning stack from scratch after this change afaict.
I wonder if the @PyTorch analysis behind this is mistaken. I suspect most of the pypi installs they’re seeing are from CI and similar. Conda installs are the standard for end user installation of PyTorch afaik
@PyTorch Conda aggressively caches installs so looking at relative download numbers won’t give a great sense of real usage.
Read 7 tweets

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