This looks pretty cool! Anyone know of visualizations for simpler neural networks? I'm aware of tensorflow playground but that's just for a toy example, is there anything for visualizing a real example (e.g handwriting recognition)?
LLM Visualization
41–50 of 138 posts
Re: LLM Visualization
#42This is an excellent tool to realize how an LLM actually works from the ground up! For those reading it and going through each step, if by chance you get stuck on why 48 elements are in the first array, please refer to the model.py on minGPT [1] It's an architectural decision that it will be great to mention in the article since people without too much context might lose it [1] https://github.com/karpathy/minGPT/blob…
Yes, thank you - It was unexplained, so I got stuck on "Why 48?", thinking I'd missing something right out of the gate.
I was thinking 42 ;-)
Re: LLM Visualization
#43A lot of transformer explanations fail to mention what makes self attention so powerful. Unlike traditional neural networks with fixed weights, self-attention layers adaptively weight connections between inputs based on context. This allows transformers to accomplish in a single layer what would take traditional networks multiple layers.
None of this seems obvious just reading the original Attention is all you need paper. Is there a more in-depth explanation of how this adaptive weighting works?
Re: LLM Visualization
#44A lot of transformer explanations fail to mention what makes self attention so powerful. Unlike traditional neural networks with fixed weights, self-attention layers adaptively weight connections between inputs based on context. This allows transformers to accomplish in a single layer what would take traditional networks multiple layers.
In case it’s confusing for anyone to see “weight” as a verb and a noun so close together, there are indeed two different things going on:
1. There are the model weights, aka the parameters. These are what get adjusted during training to do the learning part. They always exist.
2. There are attention weights. These are part of the transformer architecture and they “weight” the context of the input. They are ephemeral. Used and discarded. Don’t always exist.
They are both typically 32-bit floats in case you’re curious but still different concepts.
Re: LLM Visualization
#45This looks pretty cool! Anyone know of visualizations for simpler neural networks? I'm aware of tensorflow playground but that's just for a toy example, is there anything for visualizing a real example (e.g handwriting recognition)?
Re: LLM Visualization
#46Rather than looking at the visuals of this network, it is more better to focus on the actual problem with these LLMs which the author already has shown:
With in the transformer section:
> As is common in deep learning, it's hard to say exactly what each of these layers is doing, but we have some general ideas: the earlier layers tend to focus on learning lower-level features and patterns, while the later layers learn to recognize and understand higher-level abstractions and relationships.
That is the problem and yet these black boxes are just as explainable as a magic scroll.
Re: LLM Visualization
#47A lot of transformer explanations fail to mention what makes self attention so powerful. Unlike traditional neural networks with fixed weights, self-attention layers adaptively weight connections between inputs based on context. This allows transformers to accomplish in a single layer what would take traditional networks multiple layers.
In case it’s confusing for anyone to see “weight” as a verb and a noun so close together, there are indeed two different things going on: 1. There are the model weights, aka the parameters. These are what get adjusted during training to do the learning part. They always exist. 2. There are attention weights. These are part of the transformer architecture and they “weight” the context of the input. They are ephemeral.…
I think a good way of explaining #2 is “weight” in the sense of a weighted average
Re: LLM Visualization
#48A lot of transformer explanations fail to mention what makes self attention so powerful. Unlike traditional neural networks with fixed weights, self-attention layers adaptively weight connections between inputs based on context. This allows transformers to accomplish in a single layer what would take traditional networks multiple layers.
None of this seems obvious just reading the original Attention is all you need paper. Is there a more in-depth explanation of how this adaptive weighting works?
It’s definitely not obvious no matter how smart you are! The common metaphor used is it’s like a conversation.
Imagine you read one comment in some forum, posted in a long conversation thread. It wouldn’t be obvious what’s going on unless you read more of the thread right?
A single paper is like a single comment, in a thread that goes on for years and years.
For example, why don’t papers explain what tokens/vectors/embedding layers are? Well, they did already, except that comment in the thread came 2013 with the word2vec paper!
You might think wth? To keep up with this some one would have to spend a huge part of their time just reading papers. So yeah that’s kind of what researchers do.
The alternative is to try to find where people have distilled down the important information or summarized it. That’s where books/blogs/youtube etc come in.
Re: LLM Visualization
#49This is brilliant work, thanks for sharing.
Ditto. This is the most sophisticated viz of parameters I've seen...and it's also an interactive, step-through tutorial!
Re: LLM Visualization
#50Visualization never seems to get the credit due in software development. This is amazing.