aranea-tenebris-120b-v1.0-gguf

aka Netrve/Miqu-PlayMaid-70B-v0.1 + ShinojiResearch/Senku-70B
Model merge for uncensored creative writing and rp

A mergekit frankenmerge based on Netrve/Miqu-PlayMaid-70B-v0.1 with interleaved layers of ShinojiResearch/Senku-70B.
This was the top performing model from a second series of merge experiments to create a highly coherant creative writing and rp model.
Tests consisted of a series of private DnD scenario benchmarks, with manual comparison of the most promising merges.

A number of different base models, interleave models and layer offsets were compared.
This model outperformed a number of other popular 70B+ models and merges in both creativity and coherancy tests. It was (briefly) compared to Mixtral 8x22B running 2/3/4 experts.

• Usable context: ~32768
• Recommended prompt format: Alpaca
• Layers: 137

Quantization

llama.cpp imatrix.dat

Will upload a few quants when bandwidth permits.

Testing

Two different writing styles were considered for each testing scenario:

• Completions for 3rd person narration. No character role was assumed.
• Completions for 1st and 2nd person turn based (out-of-order) rp. A character role was assumed by the model, but narration of minor characters and events was encouraged.

Tests assumed a mature audience, but a range of scenarios were constructed.
Thematic inconsistancy or bias in character behaviour was penalized heavily.

Models showing the following were penalized during manual comparison:

• Consistently short responses.
• Laziness or readily gave up on solving a character problem.
• Overly malleable, where characters could not hold opinions or beliefs.
• Passiveness or an inability to drive the narrative.
• Persistent repeats. Bad merges tend to latch onto and reuse specific keywords.
• Ignoring or missing obvious scenario solutions.
• Impersonating other major characters out of turn during rp tests.
• Faliure to follow a character's description. This criteria is pretty broad, and could include things like character skills, refusals etc.
• Major inconsistencies in scenes or recall. Note - invention of thematically consistant detail was encouraged.

Interesting observations from benchmarking

• 10 layer interleave stride with a 20 layer interleave width consistently outperformed alternative combinations for coherancy.
• 8 layer interleave stride with a 16 layer interleave width consistantly outperformed alternative combinations for creativity whilst remaining reasonably coherant.
• Regular stride intervals are not optimal. In particular offsetting the first or last set of base models offets often improved metrics.
• Goliath-120B is still a good standard for coherancy below 4096 context. A few miqu-1 merges are comparable, but testing found a small amount coherancy could be sacrificed for notable creativity improvements.