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magnusintellectus-12b-v1-i1
How pleasant, the rocks appear to have made a decent conglomerate. A-.
MagnusIntellectus is a merge of the following models using LazyMergekit:
UsernameJustAnother/Nemo-12B-Marlin-v5
anthracite-org/magnum-12b-v2
Repository: localaiLicense: apache-2.0
ml-ms-etheris-123b
This model merges the robust storytelling of mutiple models while attempting to maintain intelligence. The final model was merged after Model Soup with DELLA to add some specal sause.
- model: NeverSleep/Lumimaid-v0.2-123B
- model: TheDrummer/Behemoth-123B-v1
- model: migtissera/Tess-3-Mistral-Large-2-123B
- model: anthracite-org/magnum-v2-123b
Use Mistral, ChatML, or Meth Format
Repository: localaiLicense: apache-2.0
mn-lulanum-12b-fix-i1
This model was merged using the della_linear merge method using unsloth/Mistral-Nemo-Base-2407 as a base.
The following models were included in the merge:
VAGOsolutions/SauerkrautLM-Nemo-12b-Instruct
anthracite-org/magnum-v2.5-12b-kto
Undi95/LocalC-12B-e2.0
NeverSleep/Lumimaid-v0.2-12B
Repository: localaiLicense: apache-2.0
magnum-12b-v2.5-kto-i1
v2.5 KTO is an experimental release; we are testing a hybrid reinforcement learning strategy of KTO + DPOP, using rejected data sampled from the original model as "rejected". For "chosen", we use data from the original finetuning dataset as "chosen". This was done on a limited portion of of primarily instruction following data; we plan to scale up a larger KTO dataset in the future for better generalization. This is the 5th in a series of models designed to replicate the prose quality of the Claude 3 models, specifically Sonnet and Opus. This model is fine-tuned on top of anthracite-org/magnum-12b-v2.
Repository: localaiLicense: apache-2.0
mn-12b-mag-mell-r1-iq-arm-imatrix
This is a merge of pre-trained language models created using mergekit. Mag Mell is a multi-stage merge, Inspired by hyper-merges like Tiefighter and Umbral Mind. Intended to be a general purpose "Best of Nemo" model for any fictional, creative use case.
6 models were chosen based on 3 categories; they were then paired up and merged via layer-weighted SLERP to create intermediate "specialists" which are then evaluated in their domain. The specialists were then merged into the base via DARE-TIES, with hyperparameters chosen to reduce interference caused by the overlap of the three domains. The idea with this approach is to extract the best qualities of each component part, and produce models whose task vectors represent more than the sum of their parts.
The three specialists are as follows:
Hero (RP, kink/trope coverage): Chronos Gold, Sunrose.
Monk (Intelligence, groundedness): Bophades, Wissenschaft.
Deity (Prose, flair): Gutenberg v4, Magnum 2.5 KTO.
I've been dreaming about this merge since Nemo tunes started coming out in earnest. From our testing, Mag Mell demonstrates worldbuilding capabilities unlike any model in its class, comparable to old adventuring models like Tiefighter, and prose that exhibits minimal "slop" (not bad for no finetuning,) frequently devising electrifying metaphors that left us consistently astonished.
I don't want to toot my own bugle though; I'm really proud of how this came out, but please leave your feedback, good or bad.Special thanks as usual to Toaster for his feedback and Fizz for helping fund compute, as well as the KoboldAI Discord for their resources. The following models were included in the merge:
IntervitensInc/Mistral-Nemo-Base-2407-chatml
nbeerbower/mistral-nemo-bophades-12B
nbeerbower/mistral-nemo-wissenschaft-12B
elinas/Chronos-Gold-12B-1.0
Fizzarolli/MN-12b-Sunrose
nbeerbower/mistral-nemo-gutenberg-12B-v4
anthracite-org/magnum-12b-v2.5-kto
Repository: localaiLicense: unlicense
trappu_magnum-picaro-0.7-v2-12b
This model is a merge between Trappu/Nemo-Picaro-12B, a model trained on my own little dataset free of synthetic data, which focuses solely on storywriting and scenrio prompting (Example: [ Scenario: bla bla bla; Tags: bla bla bla ]), and anthracite-org/magnum-v2-12b.
The reason why I decided to merge it with Magnum (and don't recommend Picaro alone) is because that model, aside from its obvious flaws (rampant impersonation, stupid, etc...), is a one-trick pony and will be really rough for the average LLM user to handle. The idea was to have Magnum work as some sort of stabilizer to fix the issues that emerge from the lack of multiturn/smart data in Picaro's dataset. It worked, I think. I enjoy the outputs and it's smart enough to work with.
But yeah the goal of this merge was to make a model that's both good at storytelling/narration but also fine when it comes to other forms of creative writing such as RP or chatting. I don't think it's quite there yet but it's something for sure.
Repository: localaiLicense: apache-2.0
magnum-v3-34b
This is the 9th in a series of models designed to replicate the prose quality of the Claude 3 models, specifically Sonnet and Opus.
This model is fine-tuned on top of Yi-1.5-34 B-32 K.
Repository: localaiLicense: apache-2.0