Great work! I still think that [1] does a better job of helping us understand how GPT and LLM work, but yours is funnier.

Then, some criticism. I probably don't get it, but I think the HN headline does your project a disservice. Your project does not demystify anything (see below) and it diverges from your project's claim, too. Furthermore, I think you claim too much on your github. "This project exists to show that training your own language model is not magic." and then just posts a few command line statements to execute. Yeah, running a mail server is not magic, just apt-get install exim4. So, code. Looking at train_guppylm.ipynb and, oh, it's PyTorch again. I'm better off reading [2] if I'm looking into that (I know, it is a published book, but I maintain my point).

So, in short, it does not help the initiated or the uninitiated. For the initiated it needs more detail for it to be useful, the uninitiated more context for it to be understood. Still a fun project, even if oversold.

[1] https://spreadsheets-are-all-you-need.ai/ [2] https://github.com/rasbt/LLMs-from-scratch

How does this compare to Andrej Karpathy's microgpt (https://karpathy.github.io/2026/02/12/microgpt/) or minGPT (https://github.com/karpathy/minGPT)?

https://bbycroft.net/llm has 3d Visualization of tiny example LLM layers that do a very good job at showing what is going on (https://news.ycombinator.com/item?id=38505211)

It's genuinely a great introduction to LLMs. I built my own awhile ago based off Milton's Paradise Lost: https://www.wvrk.org/works/milton

This is probably a consequence of the training data being fully lowercase:

You> hello Guppy> hi. did you bring micro pellets.

You> HELLO Guppy> i don't know what it means but it's mine.

Finally an LLM that's honest about its world model. "The meaning of life is food" is arguably less wrong than what you get from models 10,000x larger

I love this! Seems like it can't understand uppercase letters though

Could it be possible to train LLM only through the chat messages without any other data or input?

If Guppy doesn't know regular expressions yet, could I teach it to it just by conversation? It's a fish so it wouldn't probably understand much about my blabbing, but would be interesting to give it a try.

Or is there some hard architectural limit in the current LLM's, that the training needs to be done offline and with fairly large training set.

I like the idea, just that the examples are reproduced from the training data set.

How does it handle unknown queries?

how did you generate the synthetic data?

> you're my favorite big shape. my mouth are happy when you're here.

Laughed loudly :-D

Great and simple way to bridge the gap between LLMs and users coming in to the field!

This is really great! I've been wanting to do something similar for a while.

Love it! I think it's important to understand how the tools we use (and will only increasingly use) work under the hood.

* How creating dataset? I download it but it is commpresed in binary format.

* How training. In cloud or in my own dev

* How creating a gguf

  File "<frozen runpy>", line 198, in _run_module_as_main
  File "<frozen runpy>", line 88, in _run_code
  File "/home/user/gupik/guppylm/guppylm/__main__.py", line 48, in <module>
    main()
  File "/home/user/gupik/guppylm/guppylm/__main__.py", line 29, in main
    engine = GuppyInference("checkpoints/best_model.pt", "data/tokenizer.json")
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/user/gupik/guppylm/guppylm/inference.py", line 17, in __init__
    self.tokenizer = Tokenizer.from_file(tokenizer_path)
                     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

This is so cool! I'd love to see a write-up on how made it, and what you referenced because designing neural networks always feel like a maze ;)

how's it handle longer context or does it start hallucinating after like 2 sentences? curious what the ceiling is before the 9M params

I... wow, you made an LLM that can actually tell jokes?

Hm, I can actually try the training on my GPU. One of the things I want to try next. Maybe a bit more complex than a fish :)

Love it! Great idea for the dataset.

Why are there so many dead comments from new accounts?

Would have been funny if it were called "DORY" due to memory recall issues of the fish vs LLMs similar recall issues :)

Is this a reference from the Bobiverse?

I could fork it and create TrumpLM. Not a big leap, I suppose.

I love these kinds of educational implementations.

I want to really praise the (unintentional?) nod to Nagel, by limiting capabilities to representation of a fish, the user is immediately able to understand the constraints. It can only talk like a fish cause it’s very simple

Especially compared to public models, thats a really simple correspondence to grok intuitively (small LLM > only as verbose as a fish, larger LLM > more verbose) so kudos to the author for making that simple and fun.

Cool

Adorable! Maybe a personality that speaks in emojis?

Neat!

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I think this is a nice project because it is end to end and serves its goal well. Good job! It's a good example how someone might do something similar for a specific purpose. There are other visualizers that explain different aspects of LLMs but this is a good applied example.

How much training data did you end up needing for the fish personality to feel coherent? Curious what the minimum viable dataset looks like for something like this.