anotherjesse

4 tháng trướcdiscuss

We pride ourself on 9 5s!

9 tháng trướcdiscuss

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

12 tháng trướcdiscuss

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

năm ngoáidiscuss

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 năm trướcdiscuss

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

anotherjesse

2 năm trướcdiscuss

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

anotherjesse

2 năm trướcdiscuss

Love KidMode

Have you thought about leaning further into the kidpix like ux

anotherjesse

2 năm trướcdiscuss

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 năm trướcdiscuss

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 năm trướcdiscuss

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 năm trướcdiscuss

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

anotherjesse

3 năm trướcdiscuss

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

anotherjesse

3 năm trướcdiscuss

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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