Create README.md

README.md

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+---
+datasets:
+- ehartford/WizardLM_evol_instruct_V2_196k_unfiltered_merged_split
+language:
+- en
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- text-generation
+- nlp
+- uncensored
+- llama2
+---
+
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+  </div>
+       
+         
+<div style="height: 1px; background-color:#666; margin-bottom:20px; width:100%"></div>
+
+<div style="width: 100%;display:flex; justify-content:space-between;align-items:center">
+
+<div><h1 style="font-size:23px;">LLaMa_V2-13B-Instruct-Uncensored-HF</h1></div>
+<div> 
+  <a href="https://gitlab.com/a4to" style="display: flex; align-items:flex-end; flex-direction:row" class="gitlab">
+    <svg xmlns="http://www.w3.org/2000/svg" width="48" height="48" viewBox="0 0 256 256" style="margin-top:-30px"><path fill="#e60" d="M230.15 117.1L210.25 41a11.94 11.94 0 0 0-22.79-1.11L169.78 88H86.22L68.54 39.87A11.94 11.94 0 0 0 45.75 41l-19.9 76.1a57.19 57.19 0 0 0 22 61l73.27 51.76a11.91 11.91 0 0 0 13.74 0l73.27-51.76a57.19 57.19 0 0 0 22.02-61ZM58 57.5l15.13 41.26a8 8 0 0 0 7.51 5.24h94.72a8 8 0 0 0 7.51-5.24L198 57.5l13.07 50L128 166.21L44.9 107.5Zm-17.32 66.61L114.13 176l-20.72 14.65L57.09 165a41.06 41.06 0 0 1-16.41-40.89Zm87.32 91l-20.73-14.65L128 185.8l20.73 14.64ZM198.91 165l-36.32 25.66L141.87 176l73.45-51.9a41.06 41.06 0 0 1-16.41 40.9Z"></path></svg>
+  </a>
+</div>
+</div>
+
+
+<div style="height: 1px; background-color:#666; width:100%; margin: -0px 0 25px 0"></div>
+
+<h1 style="font-size:20px;margin-bottom:35px">
+  LLaMa.V2 13B unquantized model weights - Instruction based
+</h1>
+
+
+<p style="font-size:18px;">
+  Big thank you to Eric Hartford for his work aiding the creation of datasets such as:<br>
+    <span style="font-size:22px; font-weight:bold;margin-left:10px; margin-right:5px">
+      -</span> wizardlm_evol_instruct_V2_196k_unfiltered_merged_split<br>
+    <span style="font-size:22px; font-weight:bold;margin-left:10px; margin-right:5px">
+      -</span> wizard_vicuna_70k_unfiltered<br>
+
+  Such datasets play a big role in accurately addressing model biases, without sacrificing performance
+</p>
+
+ 
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+
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