| | --- |
| | license: llama2 |
| | --- |
| | |
| | ## 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. |
| |
|
| | Model-REVOLVER Git: |
| |
|
| | https://github.com/Digitous/ModelREVOLVER |
