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MRockatansky/gemma-4-31B-Mergemaxxed
gemma-4-31B-Mergemaxxed is a image-text-to-text model from MRockatansky. Use it for the image-text-to-text task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as apache-2.0.
Yet another Gemma-4 merge. I decided to try merge some new models to Storymaxxed. Zerofata's excellent MeroMero which has great style and makes for fun roleplays and Pantheon which hones Gemma-4's reasoning traces. Bo…
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From the Hugging Face model README
Yet another Gemma-4 merge. I decided to try merge some new models to Storymaxxed. Zerofata's excellent MeroMero which has great style and makes for fun roleplays and Pantheon which hones Gemma-4's reasoning traces. Both models really bolster Storymaxxed and this one really shines with fun dialogue, creative narratives, and strong instruction following capability.
This model was merged using the DARE TIES merge method using MRockatansky/Gemma-4-31B-storymaxxed2 as a base.
The following models were included in the merge:
The following YAML configuration was used to produce this model:
# Model Merge Configuration for Creative Writing & Roleplay Gemma-4
# Combines: Storymaxxed (story writing), MeroMero (roleplay), Pantheon (reasoning)
# Merge Method: DARE TIES - Best for 3+ specialized models with distinct capabilities
merge_method: dare_ties
base_model: MRockatansky/Gemma-4-31B-storymaxxed2 # Using Storymaxxed as base
models:
# Primary: Story Writing Specialist
- model: MRockatansky/Gemma-4-31B-storymaxxed2
parameters:
weight: 0.4
density: 0.7 # Keep 70% of task vector parameters
# Secondary: Creative Roleplay Specialist
- model: zerofata/G4-MeroMero-31B
parameters:
weight: 0.3
density: 0.7 # Keep 70% of task vector parameters
# Tertiary: Reasoning Enhancement
- model: Gryphe/Pantheon-Reasoning-31B-1.1
parameters:
weight: 0.3
density: 0.7 # Keep 70% of task vector parameters
parameters:
# Normalize weights across models
normalize: true
# Use int8 masks for memory efficiency (important for 31B models)
int8_mask: true
# Output precision - bfloat16 recommended for Gemma-4
dtype: bfloat16
# Optional: Tokenizer configuration
# Using union to preserve any special tokens from all models
tokenizer:
source: union