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<!DOCTYPE html>
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<title>LLM360 | Community-Driven AGI via Open-Source LLMs 🚀</title>
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<h2>LLM360</h2>
<ul>
<li><a href="#one">Models</a></li>
<li><a href="evaluation.html">Performance and Evaluation</a></li>
<li><a href="#two">LLM360 Suites</a></li>
<li><a href="#three">Papers</a></li>
<li><a href="#four">Blogs</a></li>
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<a href="blog/several-new-releases-to-further-our-mission.html" target="_blank">Announcing K2-65B: Learn More Here.</a>
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<h1><strong>LLM360</strong> enables <strong>community-owned AGI</strong> through <strong>open-source large model</strong> research and development.</h1>
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<h2>K2</h2>
<p>A <strong>65B parameter</strong> language model trained on <strong>1.4T tokens</strong>. It outperforms <strong>Llama 2 70B</strong>, but uses approximately <strong>35% less</strong> compute to train.</p>
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<li><a href="https://huggingface.co/collections/LLM360/k2-6622ae6911e3eb6219690039" target="_blank" class="button style1 fit">Model</a></li>
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<h2>CrystalCoder</h2>
<p> A 7B parameter language model, distinctively trained on the SlimPajama and StarCoder datasets,
eclipsing the <strong>Llama 2</strong> frontier, skillfully <strong>balances</strong> language and coding.
Its instruction-following variant, <a href="https://huggingface.co/LLM360/CrystalChat" target="_blank">CrystalChat</a>, stands out as a <strong>top-scoring</strong> 7B chat model, trained on a carefully selected mix publicly available language and code datasets.</p>
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<h2>Amber</h2>
<p>A 7B parameter English language model based on the <strong>LLaMA</strong> architecture has two fine-tuned instruction-following models named <a href="https://huggingface.co/LLM360/AmberChat" target="_blank">AmberChat</a> and <a href="https://huggingface.co/LLM360/AmberSafe" target="_blank">AmberSafe</a>.</p>
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<li><a href="https://wandb.ai/llm360/Amber/table?nw=lnzi8o2g4z" target="_blank" class="button style3 fit">W&B</a></li>
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<h2>LLM360 Suites</h2>
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<h3>LLM360 Research Suite</h3>
<p>The Research Suite is a comprehensive set of large language model (LLM) artifacts from each of our models, for academic and industry researchers to explore LLM training dynamics.</p>
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<li><a href="research.html" class="button bottom">Learn more</a></li>
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<h3>LLM360 Pretraining Suite</h3>
<p>The Pretraining Suite is a series of step-by-step guides to reproduce each of our models, for tech enthusiasts, AI practitioners, and academic or industry researchers, to transfer knowledge on LLM pretraining techniques.</p>
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<li><a href="pretraining.html" class="button bottom">Learn more</a></li>
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<h3>LLM360 Developer Suite</h3>
<p>The Developer Suite is a series of fine-tuning and inference tutorials for tech enthusiasts, AI practitioners, and academic or industry researchers, who are interested in general model usage or downstream task evaluation and research.</p>
<ul class="actions">
<li><a href="developer.html" class="button bottom">Learn more</a></li>
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<h2>Papers</h2>
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<h3>LLM360 K2-65B: Scaling Up Fully Transparent Open-Source LLMs</h3>
<span class="image right">
<img src="images/paper2.png">
</span>
<p>In this paper, we present LLM360 K2-65B, the most powerful fully transparent open-source large language model (LLM) released to date. K2 is a 65 billion parameter LLM, which follows best practices for reproducibility from the LLM360 project. Despite numerous efforts to develop and release open-source LLMs, full transparency around the training process still remains limited... </p>
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<h3>LLM360: Towards Fully Transparent Open-Source LLMs</h3>
<p>The recent surge in open-source Large Language Models (LLMs), such as LLaMA,
Falcon, and Mistral, provides diverse options for AI practitioners and researchers.
However, most LLMs have only released partial artifacts, such as the final model
weights or inference code, and technical reports increasingly limit their scope to
high-level design choices and surface statistics... </p>
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<li><a href="paper.pdf" target="_blank" class="button">Learn more</a></li>
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<section>
<h3>Inspired Research:</h3>
<ul>
<li>
<a href="https://arxiv.org/pdf/2402.19465.pdf" target="_blank">Towards Tracing Trustworthiness Dynamics: Revisiting Pre-training Period of Large Language Models</a>
</li>
<li>
<a href="https://arxiv.org/pdf/2401.12255.pdf" target="_blank">Instructional Fingerprinting of Large Language Models</a>
</li>
</ul>
</section>
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</section>
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<h2>Blogs</h2>
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<h3>Introducing K2-65B: Charting the Blueprint Towards Open-Source Artificial General Intelligence</h3>
<p>LLM360 is excited to announce several new releases to further our mission enabling community-owned AGI by creating standards and tools to advance the bleeding edge of LLM capability and empower knowledge transfer, research, and development.</p>
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<li><a href="blog/several-new-releases-to-further-our-mission.html" class="button">Learn more</a></li>
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<h3>Introducing LLM360: Fully Transparent Open-Source LLMs</h3>
<p>In recent months, the open-source large language model (LLM) community has seen tremendous model contributions. However, model weight releases and overview technical reports do not contain enough information to cover the complexity of LLM training, which hinders openness and transparency, the mechanisms behind trustworthy and innovative research and science for decades.</p>
<ul class="actions">
<li><a href="blog/introducing-llm360-fully-transparent-open-source-llms.html" class="button">Learn more</a></li>
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<h2>Connect with Us</h2>
<p>The LLM360 team is here to solve the most challenging AI problems. Reach out if you'd like to discuss.</p>
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<p>
LLM360, proudly sponsored by Petuum and MBZUAI, is dedicated to advancing the field of AI by providing comprehensive access to large language models.<br>
LLM360 enables community-owned AGI by creating standards and tools to advance the bleeding edge of LLM capability and empower knowledge transfer, research, and development.
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