---
license: apache-2.0
library_name: transformers
language:
- en
base_model:
- google/gemma-4-31B
- Vortex5/Glimmering-Citrus-31B
- Vortex5/Scarlet-Shadow-31B
- Cyclone-Labs/Twisted-Cyclone-31B
pipeline_tag: text-generation
tags:
- gemma4
- 31B
- roleplay
- instruct
- creative
- mergekit
- merge
- karcher_stock
widget:
  - text: "Vespera Synapse 31B"
    output:
      url: https://cdn-uploads.huggingface.co/production/uploads/6a6224ff09ac9553a11a5a08/7W7YizaOh9Y8dFxKvNTpP.png
---

# 🌃 Vespera Synapse 31B

![Vespera Synapse](https://cdn-uploads.huggingface.co/production/uploads/6a6224ff09ac9553a11a5a08/7W7YizaOh9Y8dFxKvNTpP.png)

This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).

The following patch was required for this merge

<details><summary><b><code>karcher_stock</code> Adaptive Tanh Soft-Clamp v11</b></summary>

```py
# ── 11. Model Stock t factor with Adaptive Soft-Clamp ─────────────
        N = len(ws_2d)
        ct = cos_theta.unsqueeze(-1) if cos_theta.dim() > 0 else cos_theta
        
        # Raw Model Stock formula
        denom = 1.0 + (N - 1) * ct
        # Add a tiny epsilon to prevent literal division by zero
        t_raw = (N * ct) / denom.clamp(min=1e-6) 

        # --- BULLETPROOF TANH CLAMP ---
        # 1. Prevent negative infinity spikes (fallback to base model)
        t_clamped_bottom = torch.clamp(t_raw, min=0.0)
        
        # 2. Smoothly asymptote positive spikes to L (Maximum allowed t-factor)
        L = 1.5 
        excess = torch.clamp(t_clamped_bottom - 1.0, min=0.0)
        t_soft_top = 1.0 + (L - 1.0) * torch.tanh(excess / (L - 1.0))
        
        # 3. Apply: If t <= 1.0, use exact math. If t > 1.0, use soft curve.
        t = torch.where(t_clamped_bottom <= 1.0, t_clamped_bottom, t_soft_top)
        # ------------------------------
```

</details>

## Merge Audit
![31B-Audit](https://cdn-uploads.huggingface.co/production/uploads/6a6224ff09ac9553a11a5a08/1rqLdZ0BB33-lQWoV42Ns.png)

## Merge Details
### Merge Method

This model was merged using the `karcher_stock` merge method using google/gemma-4-31B as a base.

### Models Merged

The following models were included in the merge:
- google/gemma-4-31B
- Vortex5/Glimmering-Citrus-31B
- Vortex5/Scarlet-Shadow-31B
- Cyclone-Labs/Twisted-Cyclone-31B

### Configuration

The following YAML configuration was used to produce this model:

```yaml
architecture: Gemma4ForConditionalGeneration
base_model: /workspace/models/google--gemma-4-31B # densenet--Gemma-4-31B-StyleTune-heretic-ara
models:
  - model: /workspace/models/Vortex5--Glimmering-Citrus-31B
  - model: /workspace/models/Vortex5--Scarlet-Shadow-31B
  - model: /workspace/models/Cyclone-Labs--Twisted-Cyclone-31B
merge_method: karcher_stock # v37
parameters:  
  filter_wise: true
  max_iter: 1000
  min_iter: 100
  tol: 1.0e-9
dtype: float32
out_dtype: bfloat16
tokenizer:
  source: union
chat_template: auto
name: 🌃 Vespera Synapse 31B
```