Is there some documentation for this? The code is probably the simplest (Not So) Large Language Model implementation possible, but it is not straight forward to understand for developers not familiar with multi-head attention, ReLU FFN, LayerNorm and learned positional embeddings.

This projects shares similarities with Minix. Minix is still used at universities as an educational tool for teaching operating system design. Minix is the operating system that taught Linus Torvalds how to design (monolithic) operating systems. Similarly having students adding capabilities to GuppyLM is a good way to learn LLM design.

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

Who cares how it compares, it's not a product it's a cool project

Even cool projects can learn from others. Maybe they missed something that could benefit the project, or made some interesting technical choice that gives a different result.

For the readers/learners, it's useful to understand the differences so we know what details matter, and which are just stylistic choices.

This isn't art; it's science & engineering.

But it isn't the OP's responsibility to compare their project to all other projects. The GP could themselves perform the comparison and post their thoughts instead of asking an open ended question.

100% agree, I didn't mean to imply that OP is responsible for that, or that the (lack of) comparison detracts in any way from the work.

I haven't compared it with anything yet. Thanks for the suggestion; I'll look into these.

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)

Pretty neat! I'll definitely take a deeper look into this.

Neat!

have little to do with this, but i have to say your project are indeed pretty cool! Consider adding some more UI?

Cool project. I'm working on something where multiple LLM agents share a world and interact with each other autonomously. One thing that surprised me is how much the "world" matters — same model, same prompt, but put it in a system with resource constraints, other agents, and persistent memory, the behavior changes dramatically. Made me realize we spend too much time optimizing the model and not enough thinking about the environment it operates in.

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 really makes me think if it would be feasible to make an llm trained exclusively on toki pona (https://en.wikipedia.org/wiki/Toki_Pona)

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.

Great find! It appears uppercase tokens are completely unknonw to the tokenizer.

But the character still comes through in response :)

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

It's arguably even better than the most famous answer to that question.

Reproduction is the goal of genes.

Food (not dying) is the goal of organisms.

The western lifestyle is an evolutionary dead end?

It seems that some in the West want it to be and are working hard to make it so.

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.

What happens during chat is just inference. The weights are frozen, and it generates tokens conditioned on the conversation so far. No learning happens. The "learning during conversation" effect you see in bigger models is in-context learning: the model uses the full chat history in its attention window, but nothing persists after the session ends.

At 9M params you won't get meaningful in-context learning either. That capability seems to emerge around 1B+ params, and it has more of a phase-transition quality than a smooth ramp. So unfortunately no, you can't teach Guppy regex by talking to it.

There is some research on "test-time training" where weights actually get updated during inference, but it's expensive and niche. Backprop costs roughly 3x the compute of a forward pass, so doing it live in a conversation is impractical for anything but tiny models.

What does "done offline" mean? Otherwise you are limited by context window.

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

How does it handle unknown queries?

It mostly doesn't, at 9M it has very limited capacity. The whole idea of this project is to demonstrate how Language Models work.

I was going to suggest implementing RoPE to fix the context limit, but realized that would make it anatomically incorrect.

I intentionally removed all optimizations to keep it vanilla.

Haha, funny name :)

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

Laughed loudly :-D

This is a direct output from the synthetic training data though - wonder if there is a bit of overfitting going on or it’s just a natural limitation of a much smaller model.

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

Uppercase letters were intentionally ignored.

> A 9M model can't conditionally follow instructions

How many parameters would you need for that?

My initial idea was to train a navigation decision model with 25M parameters for a Raspberry Pi, which, in testing, was getting about 60% of tool calls correct. IMO, it seems like around 20M parameters would be a good size for following some narrow & basic language instructions.

Ok. This makes me wonder about a broader question. Is there a scientific approach showing a pyramid of cognitive functions, and how many parameters are (minimally) required for each layer in this pyramid?

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 did you generate the synthetic data?

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

Why are there so many dead comments from new accounts?

Because despite what HN users seem to think, HN is a LLM-infested hellscape to the same degree as Reddit, if not more.

You’re absolutely right! HN isn’t just LLM-infested hellscape, it’s a completely new paradigm of machine assisted chocolate-infused information generation.

Just let me know which type of information goo you'd like me to generate, and I'll tailor the perfect one for you.

But what should we do? The parent company isn't transparent about communicating the seriousness of this problem

It really seems it's mostly AI comments on this. Maybe this topic is attractive to all the bots.

They all seem to be slop comments.

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

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

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

OMG! Why didn't I thought fo this first :P

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

With 9M params it just repeats the joke from a training dataset.

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 :)

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

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

probably 8M params are too much even :)

As long as you use the best parameters then it doesn't matter

Grab her by the pointer.

Tiny LLM is an oxymoron, just sayin.

How about: LLMs are on a spectrum and this one is on the tiny side?

True, but most would ignore LM if it weren't LLM.

Love it! Great idea for the dataset.

Is this a reference from the Bobiverse?

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.

> the user is immediately able to understand the constraints

Nagel's point was quite literally the opposite[1] of this, though. We can't understand what it must "be like to be a bat" because their mental model is so fundamentally different than ours. So using all the human language tokens in the world can't get us to truly understand what it's like to be a bat, or a guppy, or whatever. In fact, Nagel's point is arguably even stronger: there's no possible mental mapping between the experience of a bat and the experience of a human.

[1] https://www.sas.upenn.edu/~cavitch/pdf-library/Nagel_Bat.pdf

IMO we're a step before that: We don't even have a real fish involved, we have a character that is fictionally a fish.

In LLM-discussions, obviously-fictional characters can be useful for this, like if someone builds a "Chat with Count Dracula" app. To truly believe that a typical "AI" is some entity that "wants to be helpful" is just as mistaken as believing the same architecture creates an entity that "feels the dark thirst for the blood of the living."

Or, in this case, that it really enjoys food-pellets.

Id highly disagree with that. Were all living in the same shared universe, and underlying every intelligence must be precisely an understanding of events happening in this space-time.

What does 'precisely' mean? Everyone has the same understanding of events - a precise one?

Adorable! Maybe a personality that speaks in emojis?

OMG! You just gave me the next idea..

You sound like Guppy. Nice touch.

``` uv run python -m guppylm chat

Traceback (most recent call last):

  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)
                     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Exception: No such file or directory (os error 2) ```

meybe add training again (read best od fine) and train again

``` # after config device checkpoint_path = "checkpoints/best_model.pt"

ckpt = torch.load(checkpoint_path, map_location=device, weights_only=False)

model = GuppyLM(mc).to(device) if "model_state_dict" in ckpt: model.load_state_dict(ckpt["model_state_dict"]) else: model.load_state_dict(ckpt)

start_step = ckpt.get("step", 0) print(f"Encore {start_step}") ```

Cool

Neat!

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.

The constraint-driven approach here is what makes it actually useful as a learning tool. When you're working with ~130 lines of PyTorch, you can't hide behind abstractions — every design choice has to be explicit and intentional.

Curious: did implementing attention from scratch change how you think about the "key/query/value" intuition that gets used in most explanations? That's usually where the hand-waving happens in tutorials.

This comment seems ai-written

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

this comment seems to be astroturfing to sell a course