Model card
From the Hugging Face model README
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<div class="draconic-wrap">
<div class="title-container">
<h1 class="title-main">Emerald Wyvern</h1>
</div>
<div class="hero">
<img src="https://cdn-uploads.huggingface.co/production/uploads/6669a3a617b838fda45637b8/2Md7RGyGnEaASRPyYVfba.png" alt="Shadowed-Wyvern"/>
</div>
<div class="section">
<div class="section-header"><h2 class="section-title">Overview</h2></div>
<div class="section-content">
<p><b>Emerald-Wyvern-12B</b> was made with a custom method, it merges —
<a href="https://huggingface.co/Vortex5/Luminous-Shadow-12B">Luminous-Shadow-12B</a>,
<a href="https://huggingface.co/Vortex5/Velvet-Orchid-12B">Velvet-Orchid-12B</a>, and
<a href="https://huggingface.co/Vortex5/Crimson-Twilight-12B">Crimson-Twilight-12B</a>,
using <a href="https://huggingface.co/Vortex5/Harmony-Bird-12B">Harmony-Bird-12B</a> as a base.
</p>
</div>
</div>
<div class="section">
<div class="section-header"><h2 class="section-title">Merge Configuration</h2></div>
<div class="section-content">
<details>
<summary>Expand YAML Configuration</summary>
<pre><code>
models:
- model: Vortex5/Luminous-Shadow-12B
parameters:
weight:
- filter: self_attn
value: [0.20, 0.35, 0.55, 0.75, 1.00, 0.90, 0.25, 0.25]
- value: 0.33
- model: Vortex5/Velvet-Orchid-12B
parameters:
weight:
- filter: mlp
value: [0.20, 0.30, 0.20, 0.7, 0.65, 0.66, 0.0, 0.30]
- value: 0.33
- model: Vortex5/Crimson-Twilight-12B
parameters:
weight:
- filter: self_attn
value: [0.25, 0.35, 0.45, 0.55, 0.60, 0.65, 0.88, 0.88]
- filter: mlp
value: [0.20, 0.30, 0.45, 0.65, 0.80, 0.90, 0.95, 0.85]
- value: 0.33
merge_method: flowsync
base_model: Vortex5/Harmony-Bird-12B
dtype: bfloat16
parameters:
strength: 1.0
balance: 0.5
tokenizer:
source: Vortex5/Harmony-Bird-12B
</code></pre>
</details>
</div>
</div>
<div class="section">
<div class="section-header"><h2 class="section-title">Custom Merge Method</h2></div>
<div class="section-content">
<details>
<summary>About</summary>
<h1 style="margin-top: 30px;">FlowSync</h1>
<p class="concept">
<strong>Concept:</strong> A coherence-weighted tensor merge algorithm that aligns donor model deltas
relative to a shared base, computes pairwise cosine similarities to measure inter-model agreement,
and aggregates them along the highest-consensus direction in parameter space. It reinforces semantically
consistent weight shifts while suppressing incoherent or noisy deviations using adaptive temperature scaling,
semantic gating (Nyström eigenvector extraction), and MAD-based normalization.
</p>
<h2>Key Parameters</h2>
<ul class="param-list">
<li><code>strength</code> (global): Scales how far the merged tensor moves from the base toward the consensus vector. Higher values emphasize donor influence.</li>
<li><code>balance</code> (global): Modulates bias between logic-dominant (attention/MLP) and style-dominant (embedding/output) layers.</li>
</ul>
</details>
</div>
</div>
<div class="section">
<div class="section-header"><h2 class="section-title">Credits</h2></div>
<div class="section-content">
<ul>
<li><strong><a href="https://huggingface.co/mradermacher" target="_blank">Team Mradermacher</a></strong> — Static & imatrix quants</li>
<li><strong><a href="https://huggingface.co/DeathGodlike" target="_blank">DeathGodlike</a></strong> — EXL3 quants</li>
<li><strong><a>Original creators and model authors</a></strong></li>
</ul>
</div>
</div>
</div>