Pascal Pfeiffer

Besides arguable aesthetics, it also gives a better UX, as the travel length for the coursor from class selection to drawing the box on the image would be smaller.

Closing this in favor for zooming and panning functionality #1598. In this case, centering the images seems to be the better option.

render_semantic_map is the bottleneck when using a low count of history_frames. This PR speeds up the render_semantic_map function by ~200%. Even for higher count of history_frames this change is still...

> did you compare this against #196? thanks for pointing to that PR. It does even better once the caching is done: 52 ms ± 4.77 ms per loop (mean...

Thank you for reporting. We could potentially add a check for free disk space (>pretrained weights) before initializing the weights merging.

Thank you @pritthakkar , this would be great! What we had in mind here is to add another data connector to the "Import dataset" page here:  code wise, this...

Thank you for reporting. When pushing the model to huggingface hub or downloading from the UI, these weights will be automatically sharded into smaller chunks (by default safetensors with I...

Sorry, can't fully follow. So you are using a local model or a model from Huggingface to start your Experiment? And what is the next step? You can load a...

There is actually an open issue for that at transformers: https://github.com/huggingface/transformers/issues/28441 Once merged there, we can quickly integrate in H2O LLM Studio, as we are already using the transformers schedulers.

can we make this a GH action somehow?