<instructions>Your goal is to analyze the following comment and estimate how highly it will be upvoted by the Hacker News community.</instructions><comment><author>brap</author><text>I wonder if my clone will also respond 3 days later as if nothing happened</text><parent_chain></parent_chain></comment><story><title>Show HN: WhatsApp-Llama: A clone of yourself from your WhatsApp conversations</title><url>https://github.com/Ads-cmu/WhatsApp-Llama</url><text>Hello HN!&lt;p&gt;I&amp;#x27;ve been thinking about the idea of a LLM thats a clone of me - instead of generating replies to be a helpful assistant, it generates replies that are exactly like mine. The concept&amp;#x27;s appeared in fiction numerous times (the talking paintings in Harry Potter that mimic the person painted, the clones in The Prestige), and I think with LLMs, there might actually be a possibility of us doing something like this!&lt;p&gt;I&amp;#x27;ve just released a fork of the facebookresearch&amp;#x2F;llama-recipes which allows you to fine-tune a Llama model on your personal WhatsApp conversations. This adaptation can train the model (using QLoRA) to respond in a way that&amp;#x27;s eerily similar to your own texting style.&lt;p&gt;What I&amp;#x27;ve figured out so far:&lt;p&gt;Quick Learning: The model quickly adapts to personal nuances, emoji usage, and phrases that you use. I&amp;#x27;ve trained just 1 epoch on a P100 GPU using QLoRA and 4 bit quantization, and its already captured my mannerisms&lt;p&gt;Turing Tests: As an experiment, I asked my friends to ask me 3 questions, and responded with 2 candidate responses (one from me and one from llama). My friends then had to guess which candidate response was mine and which one was Llama&amp;#x27;s. Llama managed to fool 10% of my friends, but with more compute, I think it can do way better.&lt;p&gt;Here&amp;#x27;s the GitHub repository: &lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;github.com&amp;#x2F;Ads-cmu&amp;#x2F;WhatsApp-Llama&amp;#x2F;&quot;&gt;https:&amp;#x2F;&amp;#x2F;github.com&amp;#x2F;Ads-cmu&amp;#x2F;WhatsApp-Llama&amp;#x2F;&lt;/a&gt;&lt;p&gt;Would love to hear feedback, suggestions, and any cool experiences if you decide to give it a try! I&amp;#x27;d love to see how far we can push this by training bigger models for more epochs (I ran out of compute credits)</text></story>
<instructions>Your goal is to analyze the following comment and estimate how highly it will be upvoted by the Hacker News community.</instructions><comment><author>dools</author><text>&amp;gt; The concept&amp;#x27;s appeared in fiction numerous times (the talking paintings in Harry Potter that mimic the person painted, the clones in The Prestige)&lt;p&gt;How is your most notable example not when Gilfoyle does exactly this so he doesn’t have to talk to Dinesh in Silicon Valley??</text><parent_chain></parent_chain></comment><story><title>Show HN: WhatsApp-Llama: A clone of yourself from your WhatsApp conversations</title><url>https://github.com/Ads-cmu/WhatsApp-Llama</url><text>Hello HN!&lt;p&gt;I&amp;#x27;ve been thinking about the idea of a LLM thats a clone of me - instead of generating replies to be a helpful assistant, it generates replies that are exactly like mine. The concept&amp;#x27;s appeared in fiction numerous times (the talking paintings in Harry Potter that mimic the person painted, the clones in The Prestige), and I think with LLMs, there might actually be a possibility of us doing something like this!&lt;p&gt;I&amp;#x27;ve just released a fork of the facebookresearch&amp;#x2F;llama-recipes which allows you to fine-tune a Llama model on your personal WhatsApp conversations. This adaptation can train the model (using QLoRA) to respond in a way that&amp;#x27;s eerily similar to your own texting style.&lt;p&gt;What I&amp;#x27;ve figured out so far:&lt;p&gt;Quick Learning: The model quickly adapts to personal nuances, emoji usage, and phrases that you use. I&amp;#x27;ve trained just 1 epoch on a P100 GPU using QLoRA and 4 bit quantization, and its already captured my mannerisms&lt;p&gt;Turing Tests: As an experiment, I asked my friends to ask me 3 questions, and responded with 2 candidate responses (one from me and one from llama). My friends then had to guess which candidate response was mine and which one was Llama&amp;#x27;s. Llama managed to fool 10% of my friends, but with more compute, I think it can do way better.&lt;p&gt;Here&amp;#x27;s the GitHub repository: &lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;github.com&amp;#x2F;Ads-cmu&amp;#x2F;WhatsApp-Llama&amp;#x2F;&quot;&gt;https:&amp;#x2F;&amp;#x2F;github.com&amp;#x2F;Ads-cmu&amp;#x2F;WhatsApp-Llama&amp;#x2F;&lt;/a&gt;&lt;p&gt;Would love to hear feedback, suggestions, and any cool experiences if you decide to give it a try! I&amp;#x27;d love to see how far we can push this by training bigger models for more epochs (I ran out of compute credits)</text></story>