lynx

If you want in line completions, you need a model that is trained on "fill in the middle" tasks. On their Huggingface page they even say that this is not supported and needs fine tuning:

We do not recommend using base language models for conversations. Instead, you can apply post-training, e.g., SFT, RLHF, continued pretraining, etc., or fill in the middle tasks on this model.

A model that can do it is:

• starcoder2
• codegemma
• codellama

Another option is to just use the qwen model, but instead of only adding a few lines let it rewrite the entire function each time.

Split Horizon with Poison Reverse

This is probably the only reason microsoft recall exists, as it is completely useless for anything else.

Is teenage engineering the new slogan for child labor?

The --rotate normal,inverted,left,right does not work, but you can use the transform option to achieve the same effect. To create the transformation matrix you can use something like: https://angrytools.com/css-generator/transform/

• for translateXY enter half the screen resolution
• don't copy the generated code, it has the numbers in the wrong order just type out the matrix row wise.

The final command looks like this:

xrandr --output screen-1 --transform 0.87,-0.50,960,0.50,0.87,540,0,0,1

To restore the original use (type this in first, because if you screw up you might not be able to see anything anymore):

xrandr --output screen-1 --transform 1,0,0,0,1,0,0,0,1

I tested it on x11.

How can you do fractional rotation? Does it only work with x11 or is it also supported in wayland?

Here is a gray scale version of the image with better contrast.

https://app.suno.ai/

Thanks for suggesting RNote, i always use Xournal++ to take notes, but there are some problems and RNote seems to work much nicer with gestures. The only thing that i am missing is an option for saving pen configuration to easily switch between a black pen and a yellow marker.

On Huggingface is a space where you can select the model and your graphics card and see if you can run it, or how many cards you need to run it. https://huggingface.co/spaces/Vokturz/can-it-run-llm

You should be able to do inference on all 7b or smaller models with quantization.

Longnet handles that case better in my opinion. It does not need as much memory as vanilla attention, but it also does not discard as much information as this implementation. Here is a very good video on how longnet works https://www.youtube.com/watch?v=nC2nU9j9DVQ

Thanks for the suggestion, I tried it and the diff view is very good. The setup was not really easy for my local models, but after i set it up, it was really fast. The biggest problem with the tool is that the open source models are not that good, i tried if it could fix a bug in my code and it was only able to make it worse. On a more positive note, you at least do not need to copy all text over to another window and it is great for generating boilerplate code nearly flawlessly every time.