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---
license: cc-by-nc-4.0
base_model:
- Netrve/Miqu-PlayMaid-70B-v0.1
- ShinojiResearch/Senku-70B
library_name: transformers
tags:
- not-for-all-audiences
- nsfw
- mergekit
- merge
---
# aranea-tenebris-120b-v1.0
**aka Netrve/Miqu-PlayMaid-70B-v0.1 + ShinojiResearch/Senku-70B**
Model merge for uncensored creative writing and rp
A [mergekit](https://github.com/arcee-ai/mergekit) frankenmerge based on [Netrve/Miqu-PlayMaid-70B-v0.1](https://huggingface.co/Netrve/Miqu-PlayMaid-70B-v0.1) with interleaved layers of [ShinojiResearch/Senku-70B](https://huggingface.co/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](./imatrix.dat)
exllamav2 [measurement.json](./measurement.json)
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.
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