OrobasVault/Vespera-Synapse-31B

VISIONPricing:Input $0.48 / Cached $0.1 / Output $1.44Concurrent Unit Cost:2Model Size:31BQuant:FP8Context Size:32kTool Calling:SupportedPublished:Aug 26, 2026License:apache-2.0Architecture:Transformer0.0K Open Weights Featherless Exclusive Cold

OrobasVault/Vespera-Synapse-31B is a 31 billion parameter language model created by OrobasVault, merged using the karcher_stock method with google/gemma-4-31B as its base. This model integrates components from Vortex5/Glimmering-Citrus-31B, Vortex5/Scarlet-Shadow-31B, and Cyclone-Labs/Twisted-Cyclone-31B. It features a custom adaptive Tanh soft-clamp patch for enhanced stability and performance in merged architectures, making it suitable for general language generation tasks.

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Vespera Synapse 31B Overview

Vespera Synapse 31B is a 31 billion parameter language model developed by OrobasVault, constructed through a sophisticated merge of several pre-trained models. Utilizing the karcher_stock merge method, it builds upon google/gemma-4-31B as its foundational base, integrating contributions from Vortex5/Glimmering-Citrus-31B, Vortex5/Scarlet-Shadow-31B, and Cyclone-Labs/Twisted-Cyclone-31B.

Key Technical Details

  • Merge Method: Employs the karcher_stock method, known for its robust integration of multiple model components.
  • Custom Patch: Incorporates a unique karcher_stock Adaptive Tanh Soft-Clamp v11 patch, designed to prevent negative infinity spikes and smoothly manage positive spikes in the t-factor, enhancing model stability and performance.
  • Base Model: google/gemma-4-31B serves as the primary architectural foundation.
  • Merged Components: Combines distinct characteristics from three additional 31B models to create a more comprehensive and capable language model.

Potential Use Cases

This model is well-suited for a variety of general-purpose language generation and understanding tasks, benefiting from the combined strengths of its constituent models and the stability provided by its custom merge patch. Its 31B parameter count and merged architecture suggest capabilities for nuanced text generation, summarization, and conversational AI.

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