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Show HN: I built a tiny LLM to demystify how language models work

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Re: Show HN: I built a tiny LLM to demystify how language models work

#62

* 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

``` uv run python -m guppylm chat Traceback (most recent call last): File " ", line 198, in _run_module_as_main File " ", line 88, in _run_code File "/home/user/gupik/guppylm/guppylm/__main__.py", line 48, in main() File "/home/user/gupik/guppylm/guppylm/__main__.py", line 29, in main engine = GuppyInference("checkpoints/best_model.pt", "data/tokenizer.json") ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^…

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}") ```

Re: Show HN: I built a tiny LLM to demystify how language models work

#67
post #56

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.

Re: Show HN: I built a tiny LLM to demystify how language models work

#69
post #56

Earlier quoted context omitted.

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.
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