You likely mean sampler, not decoder. And no, the stronger the quantization, the more the output token probabilities diverge from the non-quantized model. With a sampler you can't recover any meaningful accuracy. If you force the sampler to select tokens that won't repeat, you're just trading repetitive gibberish for non-repetitive gibberish.
Looks awesome! The YouTube video linked inside the GitHub README seems to be unavailable: https://youtu.be/zm0XbLmXtXY Would love to see an up-to-date demo video.
Amazing! The inserted objects are renders of textured 3D models and not generated by a diffusion model + ControlNet? Is there a fixed set of textured 3D models available or are they generated on the fly based on the prompt?
Super interesting! I’m currently writing my master thesis on analyzing relationships between subreddits based on user and semantic similarity. For user similarity I use the Jaccard similarity between the unique set of authors of each subreddit.
Can I send you a message and quote you in my thesis? You can shoot me a short message as well: [email protected]
Still questions that I would assume are semantically similar to the questions you can find in exam prep material all over the internet. My point is that exams are a crutch we use to determine how well a person studied a subject. A crutch we use because we seem to lack better measuring devices. It's very much possible to ace an exam while at the same time being horrible at actually applying/working on a subject. I'd argue therefore that measuring how well LLMs perform on exams designed for humans is simply a more complicated Turing test, with all its shortcomings.
I recently looked into this as well. Specifically how the two formats differ. As it stands right now the “Feather” file format seems to be a synonym for the Arrow IPC file format or “Arrow files” [0]. There should be basically no overhead while reading into the arrow memory format [1]. Parquet files on the other hand are stored in a different format and therefore occur some overhead while reading into memory but offer more advanced mechanism for on disk encoding and compression [1].
As far as I can tell the main trade-off seems to be around deserialization overhead vs on disk file size. If anyone has more information or experience with the topic I'd love to hear!
That's impressive. The audio track of your videos is so clean and well understandable that I was wondering if you used a studio setup or voice synthesis software. Well done!
Yes, it's easy to see you put a lot of thought into that. I hope your course receives much more exposure. When I first found your videos a few weeks ago, I was surprised how few views they have given to the quality of the course.
Do you record the voice track of your videos yourself?
Glad to see you published the final lesson about transformers. Was looking forward to that!