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---
license: apache-2.0
tags:
- merge
- mergekit
base_model:
- NousResearch/Meta-Llama-3-8B-Instruct
---
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<title>Aura-llama-3 Data Card</title>
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<div class="header">
<h1>Aura-llama-3</h1> </div> <div class="info">
<img src="https://cdn-uploads.huggingface.co/production/uploads/64545af5ec40bbbd01242ca6/QYpWMEXTe0_X3A7HyeBm0.webp" alt="Aura-llama image">
<p>Now that the cute anime girl has your attention.</p>
<p>UPDATE: Model has been fixed</p>
<p>Aura-llama is using the methodology presented by SOLAR for scaling LLMs called depth up-scaling (DUS), which encompasses architectural modifications with continued pretraining. Using the solar paper as a base, I integrated Llama-3 weights into the upscaled layers, and In the future plan to continue training the model.</p>
<p>Aura-llama is a merge of the following models to create a base model to work from:</p>
<ul>
<li><a href="https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct">meta-llama/Meta-Llama-3-8B-Instruct</a></li>
<li><a href="https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct">meta-llama/Meta-Llama-3-8B-Instruct</a></li>
</ul>
</div>
<div class="update-section">
<h2>Abliterated Merged Evals (Has Not Been Finetuned):</h2>
<p>Aura-llama</p>
<ul>
<li>Avg: ?</li>
<li>ARC: ?</li>
<li>HellaSwag: ?</li>
<li>MMLU: ?</li>
<li>T-QA: ?</li>
<li>Winogrande: ?</li>
<li>GSM8K: ?</li>
</ul>
<h2>Non Abliterated Merged Evals (Has Not Been Finetuned):</h2>
<p>Aura-llama</p>
<ul>
<li>Avg: 63.13</li>
<li>ARC: 58.02</li>
<li>HellaSwag: 77.82</li>
<li>MMLU: 65.61</li>
<li>T-QA: 51.94</li>
<li>Winogrande: 73.40</li>
<li>GSM8K: 52.01</li>
</ul>
</div>
<div class="update-section">
<h2>🧩 Configuration</h2>
<pre><code>
dtype: bfloat16
merge_method: passthrough
slices:
- sources:
- layer_range: [0, 12]
model: failspy/Llama-3-8B-Instruct-abliterated
- sources:
- layer_range: [8, 20]
model: failspy/Llama-3-8B-Instruct-abliterated
- sources:
- layer_range: [16, 28]
model: failspy/Llama-3-8B-Instruct-abliterated
- sources:
- layer_range: [24, 32]
model: failspy/Llama-3-8B-Instruct-abliterated
</code></pre>
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