LLaMa_V2-13B-Instruct-Uncensored-HF

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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> <div style="height: 1px; background-color:#666; margin: 30px 0 -5px 0; width:100%"></div>

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