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.
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.
Meaning/goal of life is to reproduce. Food (and everything else) is only a means to it. Reproduction is the only root goal given by nature to any life form. All resources and qualities are provided are only to help mating.
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.
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.
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?
You’re absolutely right! HN isn’t just LLM-infested hellscape, it’s a completely new paradigm of machine assisted chocolate-infused information generation.
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.
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.
I’m not going to argue other than to say that you need to view the point from a third party perspective evaluating “fish” vs “more verbose thing,” such that the composition is the determinant of the complexity of interaction (which has a unique qualia per nagel)
Hence why it’s a “unintentional nod” not an instantiation
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)
```
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.
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.
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.
I haven't compared it with anything yet. Thanks for the suggestion; I'll look into these.
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.
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)
Neat!
Pretty neat! I'll definitely take a deeper look into this.
have little to do with this, but i have to say your project are indeed pretty cool! Consider adding some more UI?
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
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.
which is?
https://medium.com/change-your-mind/the-meaning-of-life-is-4...
Meaning/goal of life is to reproduce. Food (and everything else) is only a means to it. Reproduction is the only root goal given by nature to any life form. All resources and qualities are provided are only to help mating.
Reproduction is the goal of genes.
Food (not dying) is the goal of organisms.
Then why are reproductive rates so low in western countries?
https://en.wikipedia.org/wiki/List_of_countries_by_total_fer...
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.
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 :)
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)
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 does "done offline" mean? Otherwise you are limited by context window.
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.
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 love this! Seems like it can't understand uppercase letters though
Uppercase letters were intentionally ignored.
> 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.
> 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?
how did you generate the synthetic data?
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 ;)
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.
But what should we do? The parent company isn't transparent about communicating the seriousness of this problem
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.
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.
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.
how's it handle longer context or does it start hallucinating after like 2 sentences? curious what the ceiling is before the 9M params
Great and simple way to bridge the gap between LLMs and users coming in to the field!
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 :)
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.
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
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?
Different argument
I’m not going to argue other than to say that you need to view the point from a third party perspective evaluating “fish” vs “more verbose thing,” such that the composition is the determinant of the complexity of interaction (which has a unique qualia per nagel)
Hence why it’s a “unintentional nod” not an instantiation
Adorable! Maybe a personality that speaks in emojis?
OMG! You just gave me the next idea..
* 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
You sound like Guppy. Nice touch.
``` uv run python -m guppylm chat
Traceback (most recent call last):
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