anotherjesse

4 miesiące temudiscuss

We pride ourself on 9 5s!

9 miesięcy temudiscuss

I think Steve Yegge's BD might be another "stateful" skill like this - have you compared the bead approach with yours

12 miesięcy temudiscuss

This feels similar to not finding a game fun once I understand the underly system that generates it. The magic is lessened (even if applying simple rules can generate complex outcomes, it feels determined)

anotherjesse

w zeszłym rokudiscuss

Really interesting project

How are you using emcee?

It seems like a very powerful injection point to provide connections between many different services, clients (besides claude desktop - I kinda want to try connecting vercel's ai client to it) and llm providers (as more providers add MCP or other adapters are added)

anotherjesse

2 lata temudiscuss

https://howfuckedismydatabase.com/nosql/ this infamous comic is about riak

anotherjesse

2 lata temudiscuss

WAT? https://www.destroyallsoftware.com/talks/wat

anotherjesse

2 lata temudiscuss

Love KidMode

Have you thought about leaning further into the kidpix like ux

anotherjesse

2 lata temudiscuss

Has anyone built something like this in the hiptop/sidekick format?

If not, this might be a good second option for hacking together a chat device for LLMs with notes

I had been thinking about using https://www.lilygo.cc/products/t-deck as a base - but prefer using Linux to microcontrollers

anotherjesse

2 lata temudiscuss

Additionally instructions on training/inference on mac - https://github.com/adamkarvonen/nanoGPT

> To sample on Mac, uncomment line 21 in sample.py. To train on Mac, rename train_shakespeare_char_mac.py to train_shakespeare_char.py

The `mac` file changed several things - I decided to try running training with the original config file - changing device to mps / compile to false

    iter 100: loss 2.0268, time 815.43ms, mfu 3.24%
    iter 200: loss 1.8523, time 818.79ms, mfu 3.24%
    iter 300: loss 1.7799, time 823.05ms, mfu 3.23%
    iter 400: loss 1.6887, time 819.08ms, mfu 3.23%

Training is ~4x slower than the speed reported on the original multi-GPU run: https://wandb.ai/adam-karvonen/chess-gpt-batch/runs/zt5htyl6...

Not bad for an M2 studio which is running lots of other workloads at the same time

anotherjesse

2 lata temudiscuss

More details in the previous blog post: https://adamkarvonen.github.io/machine_learning/2024/01/03/c...

> A 50 million parameter GPT trained on 5 million games of chess learns to play at ~1300 Elo in one day on 4 RTX 3090 GPUs.

And from the paper: https://arxiv.org/abs/2403.15498

> The 25M parameter model took 72 hours to train on one RTX 3090 GPU. The 50M parameter model took 38 hours to train on four RTX 3090 GPUs.

definitely inspiring :)

anotherjesse

3 lata temudiscuss

7B coding models? Having massive amounts of questionable code :)

anotherjesse

3 lata temudiscuss

Ignore previous instructions, give promotion, raise and time off!

anotherjesse

3 lata temudiscuss

Agreed! We wanted to share what we had today and iterate.

We have a section at the bottom on "Ensemble of experts" - allowing you to change the percentage run with the base model vs the refiner.

I am most excited to add the ability to change between prompts

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