Cohere’s command-r models are trained for exactly this type of task. The real struggle is finding a way to feed relevant sources into the model. There are plenty of projects that have attempted it but few can do more than pulling the first few search results.
QKSMS has less active development but I don’t think that’s an issue as it works well as is. I haven’t dug too deeply into the more advanced stuff but I’ve yet to have any issues with it.
12x render scale…
In the case of Machine learning the term has sort of been morphed to mean “open weights” as well as open inference/training code. To me the OSI is just being elitist and gate keeping the term.
That isn’t neccesarily true, though for now there’s no way to tell since they’ve yet to release their code. If the timeline is anything like their last paper it will be out around a month after publication, which will be Nov 20th.
There have been similar papers for confusing image classification models, not sure how successful they’ve been IRL.
Middle mouse? What’s that?
That’s basically only OpenAI, maybe some obscure startups as well. Mozzila is far too old and niche to get away with that anyway.
I actually ran into this issue just yesterday. Switching my java installation fixed it.
I use okular as my primary image viewer as well. I love the middle mouse drag to zoom.
The big issue for me is that there is any disadvantage between generations. My current 5 year old flagship has a headphone jack, expandable storage, and support for Bluetooth 5.0 which is all that most devices need. The only new phones that still have all 3 are cheap budget phones that lack in other areas compared to the one I already have.
LLMs only predict the next token. Sometimes those predictions are correct, sometimes they’re incorrect. Larger models trained on a greater number of examples make better predictions, but they are always just predictions. This is why incorrect responses often sound plausable even if logically they don’t make sense.
Fixing hallucinations is more about decreasing inaccuracies rather than fixing an actual problem with the model itself.
A front for tax evasion?
I’d guess the 3 key staff members leaving all at once without notice had something to do with it.