nkaz123
Joined 3 years ago
Karma
138
Submissions
19
Member since
Oct 3
Recent submissions
Comment · 10 months ago
Yeah essentially this. The irony of about wanting to obscure information by submitting it to a model API isn't lost on me, but it was the easiest way I could think of. Wanted some way of making the most key content in my picture to be the only thing unblurred
Comment · 10 months ago
I accepted the bugginess in the browser game as unavoidable, and probably had too much faith in the LLM implementations, but I did a bit more troubleshooting than mentioned. The progressive improvement over episodes (and intuitively that PPO > the others) gave me some confidence, and I've since used a similar setup on 2048 with more results showing improvement over episode: https://wandb.ai/noahpunintended/2048-raspberry-pi?nw=nwuser...
Comment · 10 months ago
I recently updated my .github.io to route to a domain name I purchased so that could be why it's getting blocked right now.
This comment alone has more tamagotchi lore than my post as a disclaimer in case I saved you a read haha.
Comment · 10 months ago
I'm sorry for the clickbait
Comment · 1 year ago
Inspired by Bill and Ted’s Excellent Adventure (https://www.youtube.com/watch?v=GiynF8NQzgo), I created an alternative to AI agents where to do a task, simply provide a future worker with some details like description, time, and place. Then assuming time travel is invented, someone will come back and do it and be paid based on the interest accrued over the original payment.
Comment · 2 years ago
There are two models at use here, whisper tiny for transcribing audio, and then llama 3 for responding.
Whisper tiny is multi lingual (though I am using the english specific variant) and I believe llama 3 is technically capable of multi-lingual, but not sure of any benchmarks.
I think it could be made better, but for now focus is english. I'll add this to the readme though. Thanks!
Comment · 2 years ago
I'm really inspired reading this and hope it can help! I'm planning to put more work into this. I have a few rough demos of it in action on youtube (https://www.youtube.com/watch?v=OryGVbh5JZE) which should give you an idea of the quality of it at the moment.
Comment · 2 years ago
I think so. I plan to update the README in the coming days with more info, but realistically this is something you could run on your laptop too, barring some changes to how it accesses the microphone and camera. I assume this is the case too for other boards.
The only thing which might pose an issue is the total RAM size needed for whatever LLM is responsible for responding to you, but there's a wide variety of those available on ollama, Hugging Face, etc. that can work.
Comment · 2 years ago
I watched the demo, to be honest if I saw it sooner I probably would have tried to start this as a fork from there. Any idea what the issue was?
Comment · 2 years ago
Fully depends on the model, how much conversational context you provide, but if you keep things to a bare minimum, ~< 5 seconds from message received to starting the response using Llama 3 8B. I'm also using a vision language model, https://moondream.ai/, but that takes around 45 seconds so the next idea is to take a more basic image captioning model and insert it's output into context and try to cut that time down even more.
I also tried using Vulkan, which is supposedly faster, but the times were a bit slower than normal CPU for Llama CPP.
Comment · 2 years ago
The wake word detection is an interesting problem here. As you can see in the repo, I have a lot of mis-heard versions of the wake word in place, in this case being "Raspberry". Since the system heats up fast you need a fan, and with the microphone directly on a USB port next to the fan, I needed something distinct, and computer wasn't cutting it for this.
Changing the transcription model to something a bit better or moving the mic away from the fan could help this happen.