DarkArtsForge/Morbid-Aether-X-12B
DarkArtsForge/Morbid-Aether-X-12B is an experimental 12 billion parameter causal language model based on the MistralForCausalLM architecture. Utilizing a unique 'aether_x' merge method with 71 global YAML parameters, this model integrates multiple 12B base models to explore extreme processing effects. While computationally intensive, it aims to produce distinct and interesting results, potentially differing from more conventional merges. It is designed for users interested in exploring the outcomes of complex, multi-model merging techniques.
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Morbid-Aether-X-12B: Experimental Merge Model
Morbid-Aether-X-12B is an experimental 12 billion parameter model developed by DarkArtsForge, built upon the MistralForCausalLM architecture. It employs a highly complex and compute-intensive aether_x merge method, involving 71 global YAML parameters and integrating six distinct 12B base models, each contributing a weight of 0.25.
Key Characteristics
- Experimental Merge Method: Utilizes the
aether_xmethod (version 81/29), which is noted for its extreme computational demands and extensive parameter configuration. - Multi-Model Integration: Merges several 12B models, including
Moonlit-Mirage-12B-Heretic,Irix-12B-Model_Stock-absolute-heresy,KrakenSakura-Maelstrom-12B-v1,KansenSakura-Erosion-RP-12B-heretic,QuasiStarSynth-12B-absolute-heresy, andAncient-Awakening-12B-MPOA, all based onMistral-Nemo-Instruct-2407. - Intensive Processing: The merging process for this model took 26 hours despite using a
max_iter: 32setting, indicating a significant computational footprint. - Unique Output Focus: While its performance relative to less extreme merges like Morbid-Miasma-12B is noted as potentially different due to the extreme processing, the model is expected to yield "interesting results" from its complex integration.
Good For
- Exploratory Research: Ideal for researchers and developers interested in the effects of highly complex and experimental model merging techniques.
- Generating Unique Outputs: Suitable for use cases where novel or unconventional text generation is desired, stemming from its extreme processing and multi-model donor base.
- Understanding Merge Dynamics: Provides a case study for observing the outcomes of combining numerous models with significant computational effort.