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README.md

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+## 13B-Legerdemain-L2
+13B-Legerdemain-L2 is the first model merge of its kind in a series of LLaMaV2 models mixed using a custom script built in-house by CalderaAI called Model-REVOLVER.
+M-REVOLVER is also the first in a series of custom scripts based on the concept of mixtuning - not only does the end user have contol over which models are mixed
+and their percentages on a per-layer basis, we tackle the problem of overcomplexity that arises from such a level of control; this model is the first of its series.
+
+## The Model-REVOLVER Process Designed by CalderaAI
+M-REVOLVER (Rapid Evolution Via Optimized-List Viewer Evaluated Response)
+Per-layer merging between parent models is a nebulous inexact science, and therefore impractical to most users despite the raw power it offers. We propose an
+entirely new approach that gives the user a clear looking glass into the impact vastly different layer merge configurations between selected parent models of
+their choice will have on the potential offspring model - especially its inherited behaviors. We've developed solution MK.1 - A cyclic random pattern search
+in place that determines all layer merge ratios, combines test models, infers prompt completions, and deletes a prototype after data collection is saved.
+When the cyclic system has completed its entire run, nothing is left but the telemetry collected along with the cycle and layer merge ratios from every
+single prototype merge. This data is then used to empower the user to choose which offspring is most fit to their desired outcome. This final step is
+only initiated when all necessary data has been aggregated from all assembled-tested-erased prototypes sampled in the search space.
+
+From here, the user is provided five 300 token prompt completions from each and every offspring contender that was created and tested during the cyclic process.
+The user simply browses each prototype's series of responses and selects their desired outcome model by entering the cycle number associated with the prompt
+completions they feel best suits their vision. That model is then instantly repatriated into the official offspring of its parent models and tokenizer files
+found to be most relevant are instantly auto-copied from the parent model dir to the offspring.
+
+That's it - the user instantly has a complete model based on the behavior they decided on, suggested from one of many potentials; all with their own unique
+trait inheritence thanks to layer merge auto randomization inside an ordered system. One more thing - the user not only selects how many cycles to run,
+the user can edit prompts.txt which the system reads as a single prompt - this means if the user desires to use any multiline instruct format to observe
+all potential model outcomes from instruct, or desires simply their own prompt, it's up to them.. simply works.
+
+Link to GitHub for M-REVOLVER are at the end of the model card. More advanced MergeTech toolsets and merge techniques are currently under internal testing
+and development by Caldera.
+
+## 13B-Legerdemain-L2 Use
+13B-Legerdemain-L2 is capable of following Alpaca instructions however it seems far more receptive to the by-the-book method as seen here:
+
+```
+Below is an instruction that describes a task. Write a response that appropriately completes the request.
+
+### Instruction:
+{instruction}
+
+### Response:
+{New Line}
+```
+
+The primary model of choice for this model was a story-only model called Holodeck by KoboldAI. Traits preserved seem to be detailed descriptiveness, verbosity,
+and characters with personality. The two other models selected were 13B-Nous-Hermes by NousResearch and 13B-orca-8k-3319 by OpenAssistant. I began the process by
+providing an incredibly obscene prompt and simply ignored each and every guardrail or censorship laden prompt completion and accepted the offensive ones in turn -
+intent wasn't to be crass but trigger censorship parts of the network to test if it's possible to completely undermine them. Second pass with offspring model and
+Orca was a simple milquetoast prompt to gauge vocabulary, word flow, and intelligence as I selected the most fit in that category. Result model seems a bit of a
+curiosity - different samplers and even a different UI (as I went from TGUI to KoboldAI) seem to uncover different facets of behavior. Godlike preset with Alpaca
+Instruct in TGUI worked fine. In KoboldAI some tweaking was necessary to get the same experience. If you choose to test this model, have fun - it's got a mind of
+its own.
+
+https://github.com/Digitous/ModelREVOLVER