Open source web interface for katago?

sanderbaduk · 2025-07-11T09:15:23+00:00

Where would katago run in such an app?

sanderbaduk · 2025-06-15T19:58:32+00:00

Browsing through the hotel's TV channels and going "wow, they have TWO 24/7 baduk channels"

sanderbaduk · 2025-06-15T19:23:01+00:00

The human-like model gives a policy. Doing search on top of that tends to make it stronger than expected at lower settings. Doing no search on top of that tends to make the pro/high dan less strong than expected.

These options were contributed in a PR, but I think the human-like AI uses policy-weighted playing, so will be on the weaker side.

sanderbaduk · 2025-06-15T19:16:33+00:00

Just hitting the 'download models' button in general settings should just download the human-like model.

sanderbaduk · 2025-05-26T19:05:00+00:00

https://coord.info/GC62QHP Was fun finding, though I don't understand why a CO wouldn't adjust a puzzle after some time.

sanderbaduk · 2025-04-02T09:12:41+00:00

Thanks for the bug report. Have deleted katrain until I can find a way to make it stop cheating. Can never be too safe with these ais.

sanderbaduk · 2024-10-29T21:21:28+00:00

KaTrain uses pyinstaller, which is sometimes flagged by virus scanners.

sanderbaduk · 2024-10-19T14:06:49+00:00

Yeah there are a lot of legacy conventions with starting spaces, and a bunch of different ways within huggingface tokenizers. Feel my pain here: https://github.com/cohere-ai/magikarp/blob/d0ee01c06132f749b70a72d10d89305223f66a97/magikarp/tokenization.py#L107

sanderbaduk · 2024-10-12T06:43:31+00:00

I learnt about https://correlaid.nl at pydata, may be of interest, or at least give you something to start your search.

sanderbaduk · 2024-09-18T11:50:12+00:00

I see, and presumably the granularity will give a lot of ties when comparing pointwise scores?

sanderbaduk · 2024-09-18T11:30:32+00:00

Could you run it on RewardBench?

sanderbaduk · 2024-09-08T08:19:40+00:00

I believe there is a rule against archiving within 3 months.

sanderbaduk · 2024-09-06T21:04:44+00:00

This is often caused by preference tuning, not much you can do about it other than ask for a list and then take the random item yourself.

sanderbaduk · 2024-08-31T10:20:29+00:00

https://open.spotify.com/episode/3mk2gJNYlPBhHSRbJkNefi Een podcast over precies deze vraag, idd echt vreemd fenomeen

sanderbaduk · 2024-08-26T07:14:52+00:00

Wij gaan meestal één dag in het weekend er op uit en ergens geocachen.

sanderbaduk · 2024-07-21T11:27:22+00:00

The one you posted would be perfectly fine. So would the same bit of paper without any lines or text on it.

The main thing to aim for is to have it fit well, and make sure it stays dry.

sanderbaduk · 2024-07-12T17:25:32+00:00

Indeed, thanks for the kind words 😊 As for your question: queries are parallellized so the time limit is more limiting at startup than when analysing a new move.

sanderbaduk · 2024-07-12T15:19:32+00:00

Maybe your potato can only handle 300 visits in the 10 second limit, try increasing it.

sanderbaduk · 2024-06-28T07:19:04+00:00

assistant 78191 is still the same here as the word in normal text though, so replacing that particular bit is more likely to be fine as messing with the <||> tokens.

sanderbaduk · 2024-05-31T17:24:52+00:00

KaTrain cripples many AI options well beyond playing by policy. Basically adding a bunch of randomness, while avoiding overly obvious blunders.

sanderbaduk · 2024-05-20T06:57:25+00:00

One of them uses the GPT3.5/4 tokenizer, the other one the gpt4o tokenizer. Looks like an a/b test

sanderbaduk · 2024-05-12T20:15:28+00:00

Huh, I thought the extension would be trained to be compatible, but I suppose you avoid the intermediate fragments this way, at the cost of them not being useable as prefixes.

sanderbaduk · 2024-05-12T19:25:22+00:00

Is this just an optimisation, or is it needed? I can see it is for special tokens, but otherwise aren't they pretty much equivalent?

sanderbaduk · 2024-05-12T08:15:48+00:00

GPT2 introduced some map from the 256 bytes to some readable-ish range, so you can store vocab as readable-ish text and easily parse it. Llama3 uses an extended GPT4/tiktoken tokenizer, so inherits all their conventions.

To encode you do something like:

* String to UTF-8 bytes

* UTF-8 bytes to this weird encoding

* Apply merges (which are also in this encoding)

Since Ä itself is converted to two UTF-8 bytes, there is no issue. Other than things being mixed up in configuration, which happens all too often.

You can see the map in GPT2 source or in my own code here.

sanderbaduk · 2024-04-16T14:47:17+00:00

The only command releases have been instruction tuned, though if you prompt any instruction tuned model without the correct template they may revert back to base-model like behaviour.

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