mage VS peft

Yeah, that surprised me too, given that https://github.com/magefree/mage is open source and pretty actively developed.

The project you want is http://xmage.today/

All cards available, and full rules engine.

My guess is that fully modeling MtG's rules in Datalog would take a lot more work, because MtG's rules are a lot more complex. Looking through the source for one of the freely-available game engines like https://github.com/magefree/mage would probably be more informative, though.

There's a draft every day, with events at different starting times to accommodate players from around the world, plus asynchronous side events including Rich Draft, Team Sealed, and Rotisserie Draft. We play on XMage which has full rules enforcement like MTGO but is 100% free software.

If you can tolerate some of its shortcomings, you can draft against bots on Xmage.

https://github.com/magefree/mage/issues is a more reliable place to report - this might not be a trivial fix

Update: My problem happened because the launcher had reverted to the non-beta xmage home address on its own. You probably have the same problem. You need to go to settings, set branch to custom, and the xmage home address to http://xmage.today/

Real life example: The open source implementation of MTG card game multiplayer

There is not a lot of great content out there making this clear, but basically all that matters for basic fine tuning is how much VRAM you have -- since the 3090 / 4090 have 24GB VRAM they're both pretty decent fine tuning chips. I think you could probably fine-tune a model up to ~13B parameters on one of them with PEFT (https://github.com/huggingface/peft)

Hi everyone. Recently I've been looking into the PEFT library (https://github.com/huggingface/peft) and I was wondering if it would be possible to do prompt tuning with OpenAI's Whisper model. They have an example notebook for tuning Whisper with LoRA (https://colab.research.google.com/drive/1vhF8yueFqha3Y3CpTHN6q9EVcII9EYzs?usp=sharing) but I'm not sure how to go about changing it to use prompt tuning instead.

In the coming days, we'll work on sharing scripts to train models, optimizations for on-device inference, even nicer demos (and for more powerful models), and more. Feel free to like our GitHub repos (transformers, peft, accelerate). Enjoy!

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I discussed part of this here: https://github.com/huggingface/peft/issues/123

It seems like the merge_and_unload function in this PEFT script might be what they are referring to: https://github.com/huggingface/peft/blob/main/src/peft/tuners/lora.py

To obtain the original llama model, one may refer to this doc. To merge a lora model with a base model, one may refer to PEFT or use the merge script provided by LMFlow.

you could use a callback, like show here, https://github.com/huggingface/peft/issues/286 and call code to merge them here.