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<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8">
<meta name="description" content="Visual Instruction Tuning">
<meta name="keywords" content="multimodal chatbot">
<meta name="viewport" content="width=device-width, initial-scale=1">
<title>LLaVA</title>
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<body>
<section class="hero">
<div class="hero-body">
<div class="container is-max-desktop">
<div class="columns is-centered">
<div class="column has-text-centered">
<h1 class="title is-1 publication-title">LLaVA: <span class="is-size-2"><span class="is-size-1">L</span>arge <span class="is-size-1">L</span>anguage <span class="is-size-1">a</span>nd <span class="is-size-1">V</span>ision <span class="is-size-1">A</span>ssistant</span></h1>
<h3 class="title is-3 publication-title">Visual Instruction Tuning</h3>
<h5 class="subtitle is-5 publication-awards">NeurIPS 2023 (Oral)</h5>
<div class="is-size-5 publication-authors">
<span class="author-block">
<a href="https://hliu.cc/" style="color:#f68946;font-weight:normal;">Haotian Liu<sup>*</sup></a>,
</span>
<span class="author-block">
<a href="https://chunyuan.li/" style="color:#008AD7;font-weight:normal;">Chunyuan Li<sup>*</sup></a>,
</span>
<span class="author-block">
<a href="https://qywu.github.io/about.html" style="color:#F2A900;font-weight:normal;">Qingyang Wu</a>,
</span>
<span class="author-block">
<a href="https://pages.cs.wisc.edu/~yongjaelee/" style="color:#f68946;font-weight:normal;">Yong Jae
Lee</a>
</span>
</div>
<div class="is-size-5 publication-authors">
<span class="author-block"><b style="color:#f68946; font-weight:normal">▶ </b> University of
Wisconsin-Madison</b></span>
<span class="author-block"><b style="color:#008AD7; font-weight:normal">▶ </b> Microsoft Research</span>
<span class="author-block"><b style="color:#F2A900; font-weight:normal">▶ </b>Columbia
University</span>
</div>
<div class="is-size-6 publication-authors">
<span class="author-block"><sup>*</sup>Equal Contribution</span>
</div>
<!-- <div class="column has-text-centered">
<h3 class="title is-3 publication-title">Improved Baselines with Visual Instruction Fine-tuning</h3>
<div class="is-size-5 publication-authors">
<span class="author-block">
<a href="https://hliu.cc/" style="color:#f68946;font-weight:normal;">Haotian Liu<sup>*</sup></a>,
</span>
<span class="author-block">
<a href="https://chunyuan.li/" style="color:#008AD7;font-weight:normal;">Chunyuan Li<sup>*</sup></a>,
</span>
<span class="author-block">
<a href="https://yuheng-li.github.io" style="color:#008AD7;font-weight:normal;">Yuheng Li</a>,
</span>
<span class="author-block">
<a href="https://pages.cs.wisc.edu/~yongjaelee/" style="color:#f68946;font-weight:normal;">Yong Jae
Lee</a>
</span>
</div>
<div class="is-size-5 publication-authors">
<span class="author-block"><b style="color:#f68946; font-weight:normal">▶ </b> University of
Wisconsin-Madison</b></span>
<span class="author-block"><b style="color:#008AD7; font-weight:normal">▶ </b> Microsoft Research</span>
</div> -->
<div class="column has-text-centered">
<div class="publication-links">
<span class="link-block">
<a href="https://arxiv.org/abs/2304.08485" target="_blank"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="ai ai-arxiv"></i>
</span>
<span>arXiv</span>
</a>
</span>
<span class="link-block">
<a href="https://arxiv.org/abs/2310.03744" target="_blank"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="ai ai-arxiv"></i>
</span>
<span>arXiv (LLaVA-1.5)</span>
</a>
</span>
<span class="link-block">
<a href="https://github.com/haotian-liu/LLaVA" target="_blank"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="fab fa-github"></i>
</span>
<span>Code</span>
</a>
</span>
<span class="link-block">
<a href="https://llava.hliu.cc" target="_blank"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="far fa-images"></i>
</span>
<span>Demo</span>
</a>
</span>
<span class="link-block">
<a href="https://huggingface.co/datasets/liuhaotian/LLaVA-Instruct-150K" target="_blank"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="fas fa-database"></i>
</span>
<span>Dataset</span>
</a>
</span>
<span class="link-block">
<a href="https://github.com/haotian-liu/LLaVA/blob/main/docs/MODEL_ZOO.md" target="_blank"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="fas fa-share-square"></i>
</span>
<span>Model</span>
</a>
</span>
<!-- <span class="link-block">
<a href="#"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="fab fa-youtube"></i>
</span>
<span>Video</span>
</a>
</span> -->
</div>
</div>
</div>
</div>
</div>
</div>
</section>
<section class="hero teaser">
<div class="container is-max-desktop">
<div class="hero-body">
<h4 class="subtitle has-text-centered">
🔥<span style="color: #ff3860">[NEW!]</span> LLaVA-1.5 achieves SoTA on 11 benchmarks, with just simple modifications to the original LLaVA, utilizes all public data, completes training in ~1 day on a single 8-A100 node, and surpasses methods that use billion-scale data.
<br><br>
LLaVA represents a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna
for general-purpose visual and language understanding,
achieving impressive chat capabilities mimicking spirits of the multimodal GPT-4 and setting a new state-of-the-art accuracy on Science QA.
</h4>
</div>
</div>
</section>
<section class="section" style="background-color:#efeff081">
<div class="container is-max-desktop" id="gradio">
<gradio-app src="https://llava.hliu.cc"></gradio-app>
</div>
</section>
<section class="section" style="background-color:#efeff081">
<div class="container is-max-desktop">
<!-- Abstract. -->
<div class="columns is-centered has-text-centered">
<div class="column is-six-fifths">
<h2 class="title is-3">Abstract</h2>
<div class="content has-text-justified">
<p>
Instruction tuning large language models (LLMs) using machine-generated instruction-following data has improved zero-shot capabilities on new tasks in the language domain, but the idea is less explored in the multimodal field.
<ol type="1">
<li><b>Multimodal Instruct Data</b>. <span style="font-size: 95%;">We present the first attempt to use <a href="https://openai.com/research/gpt-4">language-only GPT-4</a> to generate multimodal language-image instruction-following data. </span></li>
<li><b>LLaVA Model</b>. <span style="font-size: 95%;">We introduce <it><b>LLaVA</b> (<b>L</b>arge <b>L</b>anguage-<b>a</b>nd-<b>V</b>ision <b>A</b>ssistant)</it>, an end-to-end trained large multimodal model that connects a vision encoder and LLM for general-purpose visual and language understanding.</li>
<li><b>Performance</b>. <span style="font-size: 95%;">Our early experiments show that LLaVA demonstrates impressive multimodel chat abilities, sometimes exhibiting the behaviors of multimodal GPT-4 on unseen images/instructions, and yields a 85.1% relative score compared with GPT-4 on a synthetic multimodal instruction-following dataset.
When fine-tuned on <a href="https://scienceqa.github.io/">Science QA</a>, the synergy of LLaVA and GPT-4 achieves a new state-of-the-art accuracy of 92.53%.</li>
<li><b>Open-source</b>. <span style="font-size: 95%;">We make GPT-4 generated visual instruction tuning data, our model and code base publicly available.</li>
</ol>
</p>
</div>
</div>
</div>
</div>
</section>
<section class="section">
<!-- Results. -->
<div class="columns is-centered has-text-centered">
<div class="column is-six-fifths">
<h2 class="title is-3"><img id="painting_icon" width="3%" src="https://cdn-icons-png.flaticon.com/512/5886/5886212.png"> Multimodal Instrucion-Following Data</h2>
</div>
</div>
<!-- </div> -->
<!--/ Results. -->
<div class="container is-max-desktop">
<div class="columns is-centered">
<div class="column is-full-width">
<div class="content has-text-justified">
<p>
Based on the COCO dataset, we interact with language-only GPT-4, and collect 158K unique language-image instruction-following samples in total, including 58K in conversations, 23K in detailed description, and 77k in complex reasoning, respectively. Please check out ``LLaVA-Instruct-150K''' on
<a href="https://huggingface.co/datasets/liuhaotian/LLaVA-Instruct-150K">[HuggingFace Dataset]</a>.
<!-- CSS Code: Place this code in the document's head (between the 'head' tags) -->
<style>
table.GeneratedTable {
width: 100%;
background-color: #ffffff;
border-collapse: collapse;
border-width: 2px;
border-color: #c1c4c5;
border-style: solid;
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padding: 3px;
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</style>
<!-- HTML Code: Place this code in the document's body (between the 'body' tags) where the table should appear -->
<div class="column is-six-fifths" width="80%">
<table class="GeneratedTable">
<thead>
<tr>
<th>Data file name</th>
<th>File Size</th>
<th>Sample Size</th>
</tr>
</thead>
<tbody>
<tr>
<td><a href="https://huggingface.co/datasets/liuhaotian/LLaVA-Instruct-150K/blob/main/conversation_58k.json">conversation_58k.json</a> </td>
<td>126 MB</td>
<td>58K</td>
</tr>
<tr>
<td><a href="https://huggingface.co/datasets/liuhaotian/LLaVA-Instruct-150K/blob/main/detail_23k.json">detail_23k.json</a></td>
<td>20.5 MB</td>
<td>23K</td>
</tr>
<tr>
<td><a href="https://huggingface.co/datasets/liuhaotian/LLaVA-Instruct-150K/blob/main/complex_reasoning_77k.json">complex_reasoning_77k.json</a></td>
<td>79.6 MB</td>
<td>77K</td>
</tr>
</tbody>
</table>
</div>
<!-- Codes by Quackit.com -->
</p>
<p>
For each subset, we visualize the root noun-verb pairs for the instruction and response. For each chart, please click the link for the interactive page to check out the noun-verb pairs whose frequency is higher the given number.
</p>
</div>
<div class="columns is-centered has-text-centered">
<div class="column is-six-fifths" style="display: flex; align-items: flex-start; justify-content: center;">
<figure style="text-align: center;">
<img id="teaser" width="100%" src="images/LLaVA-Instruct-150K_noun_verb/conversation_58k_instruction_verb_noun_50.png">
<figcaption>
Instruction: Conversation [<a href="images/LLaVA-Instruct-150K_noun_verb/conversation_58k_instruction_verb_noun_0.html">0</a>, <a href="images/LLaVA-Instruct-150K_noun_verb/conversation_58k_instruction_verb_noun_20.html">20</a>, <a href="images/LLaVA-Instruct-150K_noun_verb/conversation_58k_instruction_verb_noun_50.html">50</a>]
</figcaption>
</figure>
<figure style="text-align: center;">
<img id="teaser" width="100%" src="images/LLaVA-Instruct-150K_noun_verb/detail_23k_instruction_verb_noun_0.png">
<figcaption>
Instruction: Detailed Description [<a href="images/LLaVA-Instruct-150K_noun_verb/detail_23k_instruction_verb_noun_0.html">0</a>]
</figcaption>
</figure>
<figure style="text-align: center;">
<img id="teaser" width="100%" src="images/LLaVA-Instruct-150K_noun_verb/complex_reasoning_77k_instruction_verb_noun_50.png">
<figcaption>
Instruction: Complex Reasoning [<a href="images/LLaVA-Instruct-150K_noun_verb/complex_reasoning_77k_instruction_verb_noun_0.html">0</a>, <a href="images/LLaVA-Instruct-150K_noun_verb/complex_reasoning_77k_instruction_verb_noun_20.html">20</a>, <a href="images/LLaVA-Instruct-150K_noun_verb/complex_reasoning_77k_instruction_verb_noun_50.html">50</a>]
</figcaption>
</figure>
</div>
</div>
<div class="columns is-centered has-text-centered">
<div class="column is-six-fifths" style="display: flex; align-items: flex-start; justify-content: center;">
<figure style="text-align: center;">
<img id="teaser" width="100%" src="images/LLaVA-Instruct-150K_noun_verb/conversation_58k_response_verb_noun_50.png">
<figcaption>
Response: Conversation [<a href="images/LLaVA-Instruct-150K_noun_verb/conversation_58k_response_verb_noun_0.html">0</a>, <a href="images/LLaVA-Instruct-150K_noun_verb/conversation_58k_response_verb_noun_20.html">20</a>, <a href="images/LLaVA-Instruct-150K_noun_verb/conversation_58k_response_verb_noun_50.html">50</a>]
</figcaption>
</figure>
<figure style="text-align: center;">
<img id="teaser" width="100%" src="images/LLaVA-Instruct-150K_noun_verb/detail_23k_response_verb_noun_50.png">
<figcaption>
Response: Detailed Description [<a href="images/LLaVA-Instruct-150K_noun_verb/detail_23k_response_verb_noun_0.html">0</a>, <a href="images/LLaVA-Instruct-150K_noun_verb/detail_23k_response_verb_noun_20.html">20</a>, <a href="images/LLaVA-Instruct-150K_noun_verb/detail_23k_response_verb_noun_50.html">50</a>]
</figcaption>
</figure>
<figure style="text-align: center;">
<img id="teaser" width="100%" src="images/LLaVA-Instruct-150K_noun_verb/complex_reasoning_77k_response_verb_noun_50.png">
<figcaption>
Response: Complex Reasoning [<a href="images/LLaVA-Instruct-150K_noun_verb/complex_reasoning_77k_response_verb_noun_0.html">0</a>, <a href="images/LLaVA-Instruct-150K_noun_verb/complex_reasoning_77k_response_verb_noun_20.html">20</a>, <a href="images/LLaVA-Instruct-150K_noun_verb/complex_reasoning_77k_response_verb_noun_50.html">50</a>]
</figcaption>
</figure>
</div>
</div>
</div>
</div>
</section>
<section class="section">
<!-- Results. -->
<div class="columns is-centered has-text-centered">
<div class="column is-six-fifths">
<h2 class="title is-3"><img id="painting_icon" width="3%" src="https://cdn-icons-png.flaticon.com/512/5379/5379860.png"> LLaVA: Large Language-and-Vision Assistant</h2>
</div>
</div>
<!-- </div> -->
<!--/ Results. -->
<div class="container is-max-desktop">
<div class="columns is-centered">
<div class="column is-full-width">
<div class="content has-text-justified">
<p>
LLaVa connects pre-trained <a href="https://openai.com/research/clip">CLIP ViT-L/14</a> visual encoder and large language model <a href="https://github.com/lm-sys/FastChat">Vicuna</a>, using a simple projection matrix. We consider a two-stage instruction-tuning procedure:
<ul type="1">
<li><b>Stage 1: Pre-training for Feature Alignment</b>. <span style="font-size: 95%;">Only the projection matrix is updated, based on a subset of CC3M.</span></li>
<li><b>Stage 2: Fine-tuning End-to-End</b>. <span style="font-size: 95%;">Both the projection matrix and LLM are updated for two different use senarios:
<ul type="1">
<li> <b>Visual Chat</b>: LLaVA is fine-tuned on our generated multimodal instruction-following data for daily user-oriented applications.
<li> <b>Science QA</b>: LLaVA is fine-tuned on this multimodal reasonsing dataset for the science domain.</span></li>
</ul>
</ul>
Please check out our
<a href="https://github.com/haotian-liu/LLaVA/blob/main/docs/MODEL_ZOO.md">[Model Zoo]</a>.
</p>
</div>
<centering>
<div style="text-align: center;">
<img id="teaser" width="70%" src="images/llava_arch.png">
</div>
</centering>
</div>
</div>
</section>
<section class="section">
<!-- Results. -->
<div class="columns is-centered has-text-centered">
<div class="column is-six-fifths">
<h2 class="title is-3"><img id="painting_icon" width="3%" src="https://cdn-icons-png.flaticon.com/512/3515/3515174.png"> Performance</h2>
</div>
</div>
<!-- </div> -->
<!--/ Results. -->
<div class="container is-max-desktop">
<!-- Grounedtext2img. -->
<div class="columns is-centered">
<div class="column is-full-width">
<h2 class="title is-4"><img id="painting_icon" width="4%" src="https://cdn-icons-png.flaticon.com/512/1698/1698535.png"> <span style="font-size: 100%;">Visual Chat:</span> Towards building multimodal GPT-4 level chatbot </h2>
<div>
<a href="https://plotly.com/~lichunyuan24/5/?share_key=d78QObaCAYCIy8PJpe3gd1" target="_blank" title="llava_gpt4_pie" style="display: block; text-align: center;"> <img id="painting_icon" width="90%" src="images/pie_llava_gpt4.png"> </a>
</div>
<p style="font-family:Times New Roman"><b>An evaluation dataset with 30 unseen images is constructed: each image is assocaited with three types of instructions: conversation, detailed description and complex reasoning. This leads to 90 new language-image instructions, on which we test LLaVA and GPT-4, and use GPT-4 to rate their responses from score 1 to 10. The summed score and relative score per type is reported. Overall, LLaVA achieves 85.1% relative score compared with GPT-4, indicating the effectinvess of the proposed self-instruct method in multimodal settings</b>
</div>
</div>
<!-- Grounedtext2img. -->
<div class="columns is-centered">
<div class="column is-full-width">
<h2 class="title is-4"> <img id="painting_icon" width="3%" src="https://scienceqa.github.io/img/logo.png"><span style="font-size: 100%;"> Science QA:</span> New SoTA with the synergy of LLaVA with GPT-4</h2>
<div>
<a href="https://plotly.com/~lichunyuan24/1/?share_key=v4opE3TJpxqQ08RYsDD4iv" target="_blank" title="Plot 1" style="display: block; text-align: center;"><img id="painting_icon" width="65%" src="images/bar_llava_gpt4_scienceqa.png"></a>
<script data-plotly="lichunyuan24:1" sharekey-plotly="v4opE3TJpxqQ08RYsDD4iv" src="https://plotly.com/embed.js" async></script>
</div>
<p style="font-family:Times New Roman"><b>LLaVA alones achieve 90.92%. We use the text-only GPT-4 as the judge, to predict the final answer based on its own previous answers and the LLaVA answers. This "GPT-4 as judge" scheme yields a new SOTA 92.53%.</b>
</div>
</div>
</section>
<section class="section">
<div class="columns is-centered has-text-centered">
<div class="column is-six-fifths">
<h2 class="title is-3"> Examples on Visual Instruction Following</h2>
</div>
</div>
<div class="columns is-centered has-text-centered">
<div class="column is-six-fifths">
<h2 class="title is-4">Visual Reasoning on two examples from <a href="https://arxiv.org/abs/2303.08774">OpenAI GPT-4 Technical Report</a></h2>
</div>
</div>
<div class="columns is-centered has-text-centered">
<div class="column is-six-fifths">
<img id="teaser" width="35%" src="images/cmp_ironing.png">
<img id="teaser" width="38%" src="images/cmp_chicken_nugget.png">
</div>
</div>
<div class="columns is-centered has-text-centered">
<div class="column is-six-fifths">
<h2 class="title is-4">Optical character recognition (OCR)</a></h2>
</div>
</div>
<div class="columns is-centered has-text-centered">
<div class="column is-six-fifths" style="display: flex; align-items: flex-start; justify-content: center;">
<img id="teaser" width="32%" src="images/ocr/llava_example_cvpr2023.png">
<img id="teaser" width="32%" src="images/ocr/llava_example_cvinw_logo.png">
<img id="teaser" width="32%" src="images/ocr/example_llava_exmaple.png">
</div>
</div>
<div class="container mt-5">
<!-- <h2 class="text-center mb-5">Who's GPT-4's favorite? Battles between State-of-the-Art Chatbots</h2> -->
<!-- Selection -->
<div class="form-row" style="justify-content: flex-end;">
<div class="form-group col-md-1">
<div class="col-md-2" style="width: 100%"><label> </label></div>
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<button type="button" class="form-control btn btn-primary" id="next-question"><i
class="material-icons">keyboard_arrow_right</i></button>
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</div>
</div>
<!-- Question Card -->
<div style="display: flex; justify-content: center; align-items: center;">
<div class="card mb-4" style="width: 100%; display: flex; align-items: center;">
<!-- <p><b>Description:</b> Monalisa is a famous painting by Leonardo da Vinci. </p> -->
<div class="card-body" id="selected-question" style="display: flex; height: 80vh;">
<div class="chat-history">
<!-- Add your chat messages here -->
</div>
</div>
</div>
</div>
</div>
</section>
<section class="section" id="BibTeX">
<div class="container is-max-desktop content">
<h2 class="title">BibTeX</h2>
<pre><code>
@misc{liu2023improvedllava,
author={Liu, Haotian and Li, Chunyuan and Li, Yuheng and Lee, Yong Jae},
title={Improved Baselines with Visual Instruction Tuning},
publisher={arXiv:2310.03744},
year={2023},
}
@inproceedings{liu2023llava,
author = {Liu, Haotian and Li, Chunyuan and Wu, Qingyang and Lee, Yong Jae},
title = {Visual Instruction Tuning},
booktitle = {NeurIPS},
year = {2023}
}
</code></pre>
</div>
</section>
<section class="section" id="Acknowledgement">
<div class="container is-max-desktop content">
<h2 class="title">Acknowledgement</h2>
<p>
This website is adapted from <a
href="https://github.com/nerfies/nerfies.github.io">Nerfies</a>, licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/">Creative
Commons Attribution-ShareAlike 4.0 International License</a>. We thank the LLaMA team for giving us access to their models, and open-source projects, including Alpaca and Vicuna.
</p>
<p>
<b>Usage and License Notices</b>: The data, code and checkpoint is intended and licensed for research use only. They are also restricted to uses that follow the license agreement of CLIP, LLaMA, Vicuna and GPT-4. The dataset is CC BY NC 4.0 (allowing only non-commercial use) and models trained using the dataset should not be used outside of research purposes.
</p>
<p>
<a href='https://github.com/Computer-Vision-in-the-Wild/'><img id="painting_icon" width="10%" src="https://avatars.githubusercontent.com/u/97258247?s=200&v=4"></a>
Related Links:
<a href='https://react-vl.github.io/'>[REACT]</a>
<a href='https://gligen.github.io/'>[GLIGEN]</a>
<a href='https://github.com/Computer-Vision-in-the-Wild/'>[Computer Vision in the Wild (CVinW)]</a>
<a href='https://instruction-tuning-with-gpt-4.github.io/'>[Insutrction Tuning with GPT-4]</a>
</p>
</div>
</section>
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},
{
"description": "Fridge",
"turns": [
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["User", "What are the meals that I can cook with these?"],
["LLaVA", "With the variety of food items stored in the refrigerator, you can prepare several meals. For example, you can create a fruit salad using the strawberries, blueberries, and carrots. Additionally, you can make a delicious smoothie using the fruits and vegetables, which would be a healthy and nutritious snack or breakfast option. The milk and eggs can be used to create various dishes, such as a milk-based smoothie, a baked casserole, or scrambled eggs. The juice can also be used to make a fresh fruit and juice mix or as a refreshing homemade popsicle. The possibilities are vast, and the meals you can create will depend on your preferences and dietary needs."],
["User", "I would like to do the fruit salad. Show me the recipe."],
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`Certainly! Here's a simple fruit salad recipe using the items in your refrigerator:
Ingredients:
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- 1 cup fresh blueberries
- 1 large carrot, grated
- Juice of half a lemon
- A handful of chopped fresh parsley or mint
- A few ice cubes
- Optional: honey or maple syrup (adjust to taste)
Instructions:
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2. Drizzle the juice of half a lemon over the fruit and vegetables to prevent them from oxidizing and add a refreshing citrus flavor.
3. Sprinkle the chopped fresh parsley or mint over the fruit and gently toss once more.
4. If desired, add a drizzle of honey or maple syrup for a touch of sweetness. Adjust the amount according to your taste preferences.
5. Cover the bowl and refrigerate the fruit salad for at least 30 minutes or up to a few hours. This allows the flavors to meld and creates a more refreshing salad.
6. Serve the fruit salad in cups or bowls, garnished with a sprinkle of fresh parsley or mint, if desired.
This fruit salad recipe is perfect for a healthy snack or side dish, and with the variety of ingredients available in your refrigerator, it should be easy to put together quickly.`]]
},
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"description": "Beautiful View",
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["User", "What are the things I should be cautious about when I visit here?"],
["LLaVA", "When visiting this peaceful location with a pier extending over a serene lake, surrounded by mountains, there are several things to be cautious about. First, pay attention to the weather conditions, as sudden changes in weather, especially strong winds or storms, can cause the water to become rough and pose risks to the stability of the pier. Second, be mindful of the structural integrity of the pier; avoid overloading it with too many people or excessive weight, as this could potentially damage the pier or cause it to collapse. Additionally, be aware of the surrounding wildlife and respect their space; do not harm or disturb them in any way. Finally, if you decide to go fishing, ensure that you adhere to any local fishing regulations and use appropriate fishing techniques to keep the lake ecosystem healthy and maintain the balance of the food chain."],
["User", "Compose an engaging travel blog post about my trip to this place, highlighting the beautiful sceneries, joyful experiences, and include possible anecdotes."],
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One of the highlights of my trip was undoubtedly the fishing experience. The area is teeming with life, and I was thrilled to land a sizeable catch, despite the modest size of the lake. The joy of fishing in such a beautiful setting was truly rewarding, and it allowed me to take a break from my busy life and truly immerse myself in the tranquility of nature.
While exploring the surrounding area, I came across a family of birds that had built a nest close to the pier, adding to the charm of the location. Interacting with the wildlife and observing their behavior was an enriching experience that I'll cherish for years to come.
In conclusion, this idyllic destination provided me with an opportunity to unwind and recharge, surrounded by mesmerizing natural beauty and the gentle sounds of water and wildlife. If you're looking for a serene escape from the hustle and bustle of everyday life, this pier overlooking the lake is the perfect place to find solace and reconnect with nature.`
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