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Update README.md

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  Follow the org to sign up
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  </p>
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  </td>
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- </tr>
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- </table>
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-
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- ![Fine-Tuning Course thumbnail](https://github.com/huggingface/smol-course/blob/9cecab3b1eee7262413b4dfd97e92ad75ffe903e/banner.png?raw=true)
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- # Welcome to the 🤗 smol-course
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- <table width="100%" style="border-collapse: collapse;">
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- <tr>
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  <td style="
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  background: linear-gradient(90deg, lightgreen, #a855f7);
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  border-radius: 8px;
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  text-align: center;
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  ">
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  <p style="margin: 0; font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Helvetica, Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji'; font-size: 16px; font-weight: 500; color: #FFFFFF;">
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- 🚀 New Course is LIVE! &nbsp;|&nbsp;
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  <a href="https://huggingface.co/learn/smol-course/unit0/1" style="color: #FFFFFF; font-weight: bold; text-decoration: none;">
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- ✨ Follow the Org for Updates! &rarr;
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  </a>
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  </p>
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  </td>
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  </tr>
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  </table>
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  Welcome to the comprehensive (and smollest) course to **Fine-Tuning Language Models**!
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  This free course will take you on a journey, **from beginner to expert**, in understanding, implementing, and optimizing fine-tuning techniques for large language models.
 
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  Follow the org to sign up
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  </p>
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  </td>
 
 
 
 
 
 
 
 
 
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  <td style="
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  background: linear-gradient(90deg, lightgreen, #a855f7);
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  border-radius: 8px;
 
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  text-align: center;
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  ">
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  <p style="margin: 0; font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Helvetica, Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji'; font-size: 16px; font-weight: 500; color: #FFFFFF;">
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+ 🚀 New Course material is LIVE! &nbsp;|&nbsp;
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  <a href="https://huggingface.co/learn/smol-course/unit0/1" style="color: #FFFFFF; font-weight: bold; text-decoration: none;">
 
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  </a>
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  </p>
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  </td>
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  </tr>
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  </table>
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+ ![Fine-Tuning Course thumbnail](https://github.com/huggingface/smol-course/blob/9cecab3b1eee7262413b4dfd97e92ad75ffe903e/banner.png?raw=true)
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  Welcome to the comprehensive (and smollest) course to **Fine-Tuning Language Models**!
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  This free course will take you on a journey, **from beginner to expert**, in understanding, implementing, and optimizing fine-tuning techniques for large language models.