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@@ -24,7 +24,7 @@ This is my fourth model. I wanted to test *della_linear*. The point of this mode
 ## Testing stage: testing
 **(18/12/2024):** The model seems to hold up very well over context, and keeps to the character/prompt nicely. It has expansive, varied prose, lacking GPTisms mostly. The only problem is that the model always seems to interpret the input in a similar manner (probably due to *self_attn* layers). Thusly the output always tends to follow a certain theme/direction, even if the wording is different per swipe (the longer the response, the more it'll deviate from this set direction at the beginning). A peculiar quirk is that errors are predictable - if the model writes the name of the user in a wrong manner (scrambling letters, etc; I myself have a more complex name), it will ALWAYS missspell that instance of the name in consequent swipes. But it automatically fixes itself. If the first instance of the name is spelt wrong, further instances will be fixed, though. Repetition is low, and *DRY* can help if it does appear. But I've not had it pick up on any patterns. *Higher Temperature* (1.25) seems to work better. Sometimes it gives quite the impressive answers. *XTC* can improve it a lot, without decreasing intelligence - but I've not really defined the difference between responses via *neutralized sampler* answers and *XTC*. If you find that the model gives bogus on swipes, add some characters at the end of your input to sort-of scramble the output (add some asterisks or whatever; or write some useless extra sentence if you so desire).
-**EDIT:** This 'theme' of swipes being similar seems to be an issue with [inflatebot/MN-12B-Mag-Mell-R1](https://huggingface.co/inflatebot/MN-12B-Mag-Mell-R1). Perhaps I'll reduce the weight of it/balance it with ArliAI/Mistral-Nemo-12B-ArliAI-RPMax-v1.2](https://huggingface.co/ArliAI/Mistral-Nemo-12B-ArliAI-RPMax-v1.2) by putting that as the last model (the model order matters with *DELLA-Linear*, 'lower' models in the config hold more prevalence). Perhaps I can experiment with using the base models that [inflatebot/MN-12B-Mag-Mell-R1](https://huggingface.co/inflatebot/MN-12B-Mag-Mell-R1) utilize to perhaps remerge the whole model to try to alleviate this issue via different merge methods.
 ## Parameters
 - **Context size:** Not more than *20k* recommended - coherency may degrade.

 ## Testing stage: testing
 **(18/12/2024):** The model seems to hold up very well over context, and keeps to the character/prompt nicely. It has expansive, varied prose, lacking GPTisms mostly. The only problem is that the model always seems to interpret the input in a similar manner (probably due to *self_attn* layers). Thusly the output always tends to follow a certain theme/direction, even if the wording is different per swipe (the longer the response, the more it'll deviate from this set direction at the beginning). A peculiar quirk is that errors are predictable - if the model writes the name of the user in a wrong manner (scrambling letters, etc; I myself have a more complex name), it will ALWAYS missspell that instance of the name in consequent swipes. But it automatically fixes itself. If the first instance of the name is spelt wrong, further instances will be fixed, though. Repetition is low, and *DRY* can help if it does appear. But I've not had it pick up on any patterns. *Higher Temperature* (1.25) seems to work better. Sometimes it gives quite the impressive answers. *XTC* can improve it a lot, without decreasing intelligence - but I've not really defined the difference between responses via *neutralized sampler* answers and *XTC*. If you find that the model gives bogus on swipes, add some characters at the end of your input to sort-of scramble the output (add some asterisks or whatever; or write some useless extra sentence if you so desire).
+**EDIT:** This 'theme' of swipes being similar seems to be an issue with [inflatebot/MN-12B-Mag-Mell-R1](https://huggingface.co/inflatebot/MN-12B-Mag-Mell-R1). Perhaps I'll reduce the weight of it/balance it with ArliAI/Mistral-Nemo-12B-ArliAI-RPMax-v1.2](https://huggingface.co/ArliAI/Mistral-Nemo-12B-ArliAI-RPMax-v1.2) by putting that as the last model (the model order matters with *DELLA-Linear*, 'lower' models in the config hold more prevalence). Perhaps I can experiment with using the base models that [inflatebot/MN-12B-Mag-Mell-R1](https://huggingface.co/inflatebot/MN-12B-Mag-Mell-R1) utilizes to perhaps remerge the whole model to try to alleviate this issue via different merge methods.
 ## Parameters
 - **Context size:** Not more than *20k* recommended - coherency may degrade.