Visualizing GPT-OSS-20B embeddings
19 comments
·August 17, 2025numpad0
Is this handling Unicode correctly? Seems like a lot of even Latin alphabets are getting mangled.
int_19h
It looks like it's not handling UTF-8 at all and displaying it as if it were Latin-1
mkl
I don't think it's actually UTF-8. The data is at https://melonmars.github.io/LatentExplorer/embeddings_2d.jso... and contains things like
"\u00e0\u00a7\u012d\u00e0\u00a6\u013e"
voodooEntity
@Author i would recommend you to give
https://github.com/vasturiano/3d-force-graph
a try, for the text labels you can use
https://github.com/vasturiano/three-spritetext
its based on Three.js and creates great 3D graph visualisations GPU rendered (webgl). This could make it alot more interresting to watch because it could display actual depth (your gpu is gonne run hot but i guess worth it)
just a suggestion.
_def
I have the suspicion that this is how GPT-OSS-20B would generate a visualization of it's embeddings. Happy to learn otherwise.
graphviz
What do people learn from visualizations like this?
What is the most important problem anyone has solved this way?
Speaking as somewhat of a co-defendant.
minimaxir
Not everything has to be directly informative or solve a problem. Sometimes data visualization can look pretty for pretty's sake.
Dimensionality reduction/clustering like this may be less useful for identifying trends in token embeddings, but for other types of embeddings it's extremely useful.
TuringNYC
> What do people learn from visualizations like this?
Applying the embeddings model to some dataset of yours of interest, and then a similar visualization, is where it gets cool because you can visually look at clusters and draw conclusions about the closeness of items in your own dataset
jablongo
I lets you inspect what actually constitutes a given cluster, for example it seems like the outer clusters are variations of individual words and their direct translations, rather than synonyms (the ones I saw at least).
ashvardanian
Any good comparisons of traditional embedding models against embeddings derived from autoregressive language models?
minimaxir
They are incomparable. Token embeddings generated with something like word2vec worked well because the networks are shallow and therefore the learned semantic data can be contained solely and independently within the embeddings themselves. Token embeddings as a part of an LLM (e.g. gpt-oss-20b) are conditioned on said LLM and do not have fully independent learned data, although as shown here there still can be some relationships preserved.
Embeddings derived from autoregressive language models apply full attention mechanisms to get something different entirely.
eddywebs
Cool ! Would it possible to generate visualizations of any given open weight model out there ?
minimaxir
Yes, it's just yoinking the weights out of the embeddings layer.
kingstnap
It's an interesting looking plot I suppose.
My guess is its the 2 largest principle components of the embedding.
But none of the points are labelled? There isn't a writeup on the page or anything?
jablongo
Usually PCA doesn't look quite like this so this is likely done using TSNE or UMAP, which are non parametric embeddings (they optimize a loss by modifying the embedded points directly). I can see labels if I mouseover the dots.
terhechte
I can see the labels when I hover with the pointer
lawlessone
what does it mean that some embeddings are close to others in this space?
That they're related or connected or it arbitrary?
Why does it look like a fried egg?
edit: must be related in some way as one of the "droplets" in the bottom left quadrant seems to consist of various versions of the word "parameter"
minimaxir
Typically these algorithms cluster by similarity (either euclidian or cosine).
The density of the clusters tend to have trends. In this case, the "yolk" has a lot of bizarre unicode tokens.
Without a way to tune it, this visualization is as much about the dimensionality reduction algorithm used as the embeddings themselves, because trade-offs are unavoidable when you go from a very high dimensional space to a 2D one. I would not read too much into it.