Good with Alpaca prompt formats, also works with Mistral format. See usage details below.
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This is similar to BagelMIsteryTour, but I've swapped out Sensualize for the new Holodeck.
I'm not sure if it's better or not yet, or how it does at higher (8k+) contexts just yet.
Similar sampler advice applies as for BMT: minP (0.07 - 0.3 to taste) -> temp (either dynatemp 0-4ish, or like a temp of 3-4 with a smoothing factor of around 2.5ish).
And yes, that's temp last. It does okay without rep pen up to a point, it doesn't seem to get into a complete jam, but it can start to repeat sentences,
so you'll probably need some, perhaps 1.02-1.05 at a 1024 range seems okayish.
(rep pen sucks, but there are better things coming).
I've mainly tested with LimaRP style Alpaca prompts (instruction/input/response), and briefly with Mistral's own format.
Full credit to all the model and dataset authors, I am but a derp with compute and a yaml file.
This is a merge of pre-trained language models created using mergekit.
