Schisandra
Many thanks to the authors of the models used!
RPMax v1.1 | Pantheon-RP | Cydonia v1.2 | Magnum V4 | ChatWaifu v2.0 | SorcererLM | Acolyte | NovusKyver
The new version writes better and doesn't mispronounce names anymore!
https://huggingface.co/Nohobby/MS-Schisandra-22B-v0.2
Overview
Main uses: RP, Storywriting
Merge of 8 Mistral Small finetunes in total, which were then merged back into the original model to make it less stupid. Worked somehow? Definitely smarter than my previous MS merge and maybe some finetunes. Seems to really adhere to the writing style of the previous output, so you'll need either a good character card or an existing chat for a better replies.
Quants
Settings
Prompt format: Mistral-V3 Tekken
Merge Details
Merging steps
QCmix
base_model: InferenceIllusionist/SorcererLM-22B
parameters:
int8_mask: true
rescale: true
normalize: false
dtype: bfloat16
tokenizer_source: base
merge_method: della
models:
- model: Envoid/Mistral-Small-NovusKyver
parameters:
density: [0.35, 0.65, 0.5, 0.65, 0.35]
epsilon: [0.1, 0.1, 0.25, 0.1, 0.1]
lambda: 0.85
weight: [-0.01891, 0.01554, -0.01325, 0.01791, -0.01458]
- model: rAIfle/Acolyte-22B
parameters:
density: [0.6, 0.4, 0.5, 0.4, 0.6]
epsilon: [0.15, 0.15, 0.25, 0.15, 0.15]
lambda: 0.85
weight: [0.01768, -0.01675, 0.01285, -0.01696, 0.01421]
Schisandra-vA
merge_method: della_linear
dtype: bfloat16
parameters:
normalize: true
int8_mask: true
tokenizer_source: union
base_model: TheDrummer/Cydonia-22B-v1.2
models:
- model: ArliAI/Mistral-Small-22B-ArliAI-RPMax-v1.1
parameters:
density: 0.55
weight: 1
- model: Gryphe/Pantheon-RP-Pure-1.6.2-22b-Small
parameters:
density: 0.55
weight: 1
- model: spow12/ChatWaifu_v2.0_22B
parameters:
density: 0.55
weight: 1
- model: anthracite-org/magnum-v4-22b
parameters:
density: 0.55
weight: 1
- model: QCmix
parameters:
density: 0.55
weight: 1
Schisandra
dtype: bfloat16
tokenizer_source: base
merge_method: della_linear
parameters:
density: 0.5
base_model: Schisandra
models:
- model: unsloth/Mistral-Small-Instruct-2409
parameters:
weight:
- filter: v_proj
value: [0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0]
- filter: o_proj
value: [1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1]
- filter: up_proj
value: [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
- filter: gate_proj
value: [0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0]
- filter: down_proj
value: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
- value: 0
- model: Schisandra
parameters:
weight:
- filter: v_proj
value: [1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1]
- filter: o_proj
value: [0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0]
- filter: up_proj
value: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
- filter: gate_proj
value: [1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1]
- filter: down_proj
value: [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
- value: 1
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