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shen-shanshan committed Dec 22, 2024
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4 changes: 4 additions & 0 deletions content/about/index.md
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I am a contributor to many open source projects on GitHub, which are shown below.

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<br>

{{< github repo="ggerganov/llama.cpp" >}}

<br>
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<time datetime="2024-11-02T23:54:20&#43;00:00">2024-11-02</time><span class="px-2 text-primary-500">&middot;</span><span>582 words</span><span class="px-2 text-primary-500">&middot;</span>
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Expand Down Expand Up @@ -1046,7 +1046,7 @@ <h2 class="relative group">Open Source Contribution

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<p>I am a contributor to many open source projects on GitHub, which are shown below.</p>
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Expand All @@ -1064,20 +1064,20 @@ <h2 class="relative group">Open Source Contribution
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<p id="github-af0f68a4623992f3d547da57b51b7fe4-description" class="m-0 mt-2 text-md text-neutral-800 dark:text-neutral">
LLM inference in C/C++
A high-throughput and memory-efficient inference and serving engine for LLMs
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C&#43;&#43;
Python
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Expand All @@ -1090,7 +1090,7 @@ <h2 class="relative group">Open Source Contribution

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A generative speech model for daily dialogue.
LLM inference in C/C++
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Expand All @@ -1170,7 +1170,7 @@ <h2 class="relative group">Open Source Contribution

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<time datetime="2024-12-05T10:00:40&#43;08:00">2024-12-05</time><span class="px-2 text-primary-500">&middot;</span><span>1146 words</span><span class="px-2 text-primary-500">&middot;</span>
<time datetime="2024-12-05T10:00:40&#43;08:00">2024-12-05</time><span class="px-2 text-primary-500">&middot;</span><span>1157 words</span><span class="px-2 text-primary-500">&middot;</span>


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Expand Down Expand Up @@ -991,6 +991,8 @@ <h2 class="relative group">四、大模型微调的方法有哪些
<li><strong>参数高效微调（Parameter-Efficient Fine-Tuning，PEFT）</strong>：只对部分参数进行更新，训练速度快，消耗机器资源少。</li>
</ul>
<p>此外，还有一种不需要更新模型权重就可以完成微调的方法，叫做 <strong>In-Context Learning</strong>，通过在输入的 prompt 中提供与任务相关的上下文和例子，从而让模型能够更好地了理解我们的意图。</p>
<p><strong>最新进展</strong>：</p>
<p>在 OpenAI 最新的发布会中，还提出了一种叫做 <strong>RFT（Reinforcement Fine-Tuning）</strong> 的微调技术，能够以奖励驱动的方式不断完善大模型所掌握的知识，更多细节可以参考这篇文章：<a href="https://www.datacamp.com/blog/reinforcement-fine-tuning" target="_blank"><u>What Is OpenAI&rsquo;s Reinforcement Fine-Tuning?</u></a>。</p>


<h3 class="relative group">4.1 FFT 的优缺点
Expand Down Expand Up @@ -1079,7 +1081,7 @@ <h3 class="relative group">4.3 PEFT 的分类
<li><strong>Hybrid methods</strong>：根据实际情况，可以对上述方法进行组合，从而达到更好的效果。</li>
</ul>
<p>目前比较主流的几种参数高效微调方法包括：Prompt Tuning、Prefix Tuning、LoRA、QLoRA 等。</p>
<p>论文<a href="https://arxiv.org/abs/2303.15647" target="_blank">《Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning》</a>中展示了各类参数高效微调方法及其所属的类别，如下所示：</p>
<p>论文<a href="https://arxiv.org/abs/2303.15647" target="_blank"><u>《Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning》</u></a>中展示了各类参数高效微调方法及其所属的类别，如下所示：</p>
<p>
<figure>
<img class="my-0 rounded-md" loading="lazy" src="./images/peft%E5%88%86%E7%B1%BB.png" alt="1" />
Expand Down Expand Up @@ -2326,8 +2328,8 @@ <h2 class="mt-8 text-2xl font-extrabold mb-10">Related</h2>


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