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<div class="mb-3 flex flex-wrap md:mb-4"><a class="tag tag-white" href="/models?pipeline_tag=text-generation"><div class="tag-ico tag-ico-indigo"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 18 18"><path d="M16.2607 8.08202L14.468 6.28928C14.3063 6.12804 14.0873 6.03749 13.859 6.03749C13.6307 6.03749 13.4117 6.12804 13.25 6.28928L5.6375 13.904V16.9125H8.64607L16.2607 9.30002C16.422 9.13836 16.5125 8.91935 16.5125 8.69102C16.5125 8.4627 16.422 8.24369 16.2607 8.08202V8.08202ZM8.1953 15.825H6.725V14.3547L11.858 9.22118L13.3288 10.6915L8.1953 15.825ZM14.0982 9.92262L12.6279 8.45232L13.8606 7.21964L15.3309 8.68994L14.0982 9.92262Z"></path><path d="M6.18125 9.84373H7.26875V6.03748H8.9V4.94998H4.55V6.03748H6.18125V9.84373Z"></path><path d="M4.55 11.475H2.375V2.775H11.075V4.95H12.1625V2.775C12.1625 2.48658 12.0479 2.20997 11.844 2.00602C11.64 1.80208 11.3634 1.6875 11.075 1.6875H2.375C2.08658 1.6875 1.80997 1.80208 1.60602 2.00602C1.40207 2.20997 1.2875 2.48658 1.2875 2.775V11.475C1.2875 11.7634 1.40207 12.04 1.60602 12.244C1.80997 12.4479 2.08658 12.5625 2.375 12.5625H4.55V11.475Z"></path></svg></div>
<span>Text Generation</span>
</a><a class="tag tag-white" href="/models?library=pytorch"><svg class="text-black inline-block ml-2 text-sm" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><defs><clipPath id="a"><rect x="3.05" y="0.5" width="25.73" height="31" fill="none"></rect></clipPath></defs><g clip-path="url(#a)"><path d="M24.94,9.51a12.81,12.81,0,0,1,0,18.16,12.68,12.68,0,0,1-18,0,12.81,12.81,0,0,1,0-18.16l9-9V5l-.84.83-6,6a9.58,9.58,0,1,0,13.55,0ZM20.44,9a1.68,1.68,0,1,1,1.67-1.67A1.68,1.68,0,0,1,20.44,9Z" fill="#ee4c2c"></path></g></svg>
<span>PyTorch</span>
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<span>Transformers</span>
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<pre><!-- HTML_TAG_START --><span class="hljs-attr">extra_gated_heading:</span> <span class="hljs-string">Access</span> <span class="hljs-string">Llama</span> <span class="hljs-number">2</span> <span class="hljs-string">on</span> <span class="hljs-string">Hugging</span> <span class="hljs-string">Face</span>
<span class="hljs-attr">extra_gated_description:</span> <span class="hljs-string">>-</span>
<span class="hljs-string"> This is a form to enable access to Llama 2 on Hugging Face after you have been</span>
<span class="hljs-string"> granted access from Meta. Please visit the [Meta</span>
<span class="hljs-string"> website](https://ai.meta.com/resources/models-and-libraries/llama-downloads)</span>
<span class="hljs-string"> and accept our license terms and acceptable use policy before submitting this</span>
<span class="hljs-string"> form. Requests will be processed in 1-2 days.</span>
<span class="hljs-string"></span><span class="hljs-attr">extra_gated_prompt:</span> <span class="hljs-string">>-</span>
<span class="hljs-string"> **Your Hugging Face account email address MUST match the email you provide on</span>
<span class="hljs-string"> the Meta website, or your request will not be approved.**</span>
<span class="hljs-string"></span><span class="hljs-attr">extra_gated_button_content:</span> <span class="hljs-string">Submit</span>
<span class="hljs-attr">extra_gated_fields:</span>
<span class="hljs-string">I</span> <span class="hljs-string">agree</span> <span class="hljs-string">to</span> <span class="hljs-string">share</span> <span class="hljs-string">my</span> <span class="hljs-string">name,</span> <span class="hljs-attr">email address and username with Meta and confirm that I have already been granted download access on the Meta website:</span> <span class="hljs-string">checkbox</span>
<span class="hljs-attr">language:</span>
<span class="hljs-bullet">-</span> <span class="hljs-string">en</span>
<span class="hljs-attr">pipeline_tag:</span> <span class="hljs-string">text-generation</span>
<span class="hljs-attr">inference:</span> <span class="hljs-literal">false</span>
<span class="hljs-attr">tags:</span>
<span class="hljs-bullet">-</span> <span class="hljs-string">facebook</span>
<span class="hljs-bullet">-</span> <span class="hljs-string">meta</span>
<span class="hljs-bullet">-</span> <span class="hljs-string">pytorch</span>
<span class="hljs-bullet">-</span> <span class="hljs-string">llama</span>
<span class="hljs-bullet">-</span> <span class="hljs-string">llama-2</span>
<!-- HTML_TAG_END --></pre></div>
<!-- HTML_TAG_START --><h1 class="relative group flex items-center">
<a rel="noopener nofollow" href="#llama-2" class="block pr-1.5 text-lg md:absolute md:p-1.5 md:opacity-0 md:group-hover:opacity-100 md:right-full" id="llama-2">
<span class="header-link"><svg viewBox="0 0 256 256" preserveAspectRatio="xMidYMid meet" height="1em" width="1em" role="img" aria-hidden="true" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" class="text-gray-500 hover:text-black w-4"><path fill="currentColor" d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z"></path></svg></span>
</a>
<span>
<strong>Llama 2</strong>
</span>
</h1>
<p>Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the 70B pretrained model, converted for the Hugging Face Transformers format. Links to other models can be found in the index at the bottom.</p>
<h2 class="relative group flex items-center">
<a rel="noopener nofollow" href="#model-details" class="block pr-1.5 text-lg md:absolute md:p-1.5 md:opacity-0 md:group-hover:opacity-100 md:right-full" id="model-details">
<span class="header-link"><svg viewBox="0 0 256 256" preserveAspectRatio="xMidYMid meet" height="1em" width="1em" role="img" aria-hidden="true" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" class="text-gray-500 hover:text-black w-4"><path fill="currentColor" d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z"></path></svg></span>
</a>
<span>
Model Details
</span>
</h2>
<p><em>Note: Use of this model is governed by the Meta license. In order to download the model weights and tokenizer, please visit the <a rel="noopener nofollow" href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/">website</a> and accept our License before requesting access here.</em></p>
<p>Meta developed and publicly released the Llama 2 family of large language models (LLMs), a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama-2-Chat, are optimized for dialogue use cases. Llama-2-Chat models outperform open-source chat models on most benchmarks we tested, and in our human evaluations for helpfulness and safety, are on par with some popular closed-source models like ChatGPT and PaLM.</p>
<p><strong>Model Developers</strong> Meta</p>
<p><strong>Variations</strong> Llama 2 comes in a range of parameter sizes — 7B, 13B, and 70B — as well as pretrained and fine-tuned variations.</p>
<p><strong>Input</strong> Models input text only.</p>
<p><strong>Output</strong> Models generate text only.</p>
<p><strong>Model Architecture</strong> Llama 2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align to human preferences for helpfulness and safety.</p>
<div class="max-w-full overflow-auto">
<table>
<thead><tr>
<th></th>
<th>Training Data</th>
<th>Params</th>
<th>Content Length</th>
<th>GQA</th>
<th>Tokens</th>
<th>LR</th>
</tr>
</thead><tbody><tr>
<td>Llama 2</td>
<td><em>A new mix of publicly available online data</em></td>
<td>7B</td>
<td>4k</td>
<td>✗</td>
<td>2.0T</td>
<td>3.0 x 10<sup>-4</sup></td>
</tr>
<tr>
<td>Llama 2</td>
<td><em>A new mix of publicly available online data</em></td>
<td>13B</td>
<td>4k</td>
<td>✗</td>
<td>2.0T</td>
<td>3.0 x 10<sup>-4</sup></td>
</tr>
<tr>
<td>Llama 2</td>
<td><em>A new mix of publicly available online data</em></td>
<td>70B</td>
<td>4k</td>
<td>✔</td>
<td>2.0T</td>
<td>1.5 x 10<sup>-4</sup></td>
</tr>
</tbody>
</table>
</div>
<p><em>Llama 2 family of models.</em> Token counts refer to pretraining data only. All models are trained with a global batch-size of 4M tokens. Bigger models - 70B -- use Grouped-Query Attention (GQA) for improved inference scalability.</p>
<p><strong>Model Dates</strong> Llama 2 was trained between January 2023 and July 2023.</p>
<p><strong>Status</strong> This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback.</p>
<p><strong>License</strong> A custom commercial license is available at: <a rel="noopener nofollow" href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/">https://ai.meta.com/resources/models-and-libraries/llama-downloads/</a></p>
<p><strong>Research Paper</strong> <a rel="noopener nofollow" href="/stabilityai/FreeWilly2/blob/main/llama2/arxiv.org/abs/2307.09288">"Llama-2: Open Foundation and Fine-tuned Chat Models"</a></p>
<h2 class="relative group flex items-center">
<a rel="noopener nofollow" href="#intended-use" class="block pr-1.5 text-lg md:absolute md:p-1.5 md:opacity-0 md:group-hover:opacity-100 md:right-full" id="intended-use">
<span class="header-link"><svg viewBox="0 0 256 256" preserveAspectRatio="xMidYMid meet" height="1em" width="1em" role="img" aria-hidden="true" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" class="text-gray-500 hover:text-black w-4"><path fill="currentColor" d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z"></path></svg></span>
</a>
<span>
Intended Use
</span>
</h2>
<p><strong>Intended Use Cases</strong> Llama 2 is intended for commercial and research use in English. Tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks.</p>
<p>To get the expected features and performance for the chat versions, a specific formatting needs to be followed, including the <code>INST</code> and <code><<SYS>></code> tags, <code>BOS</code> and <code>EOS</code> tokens, and the whitespaces and breaklines in between (we recommend calling <code>strip()</code> on inputs to avoid double-spaces). See our reference code in github for details: <a rel="noopener nofollow" href="https://github.com/facebookresearch/llama/blob/main/llama/generation.py#L212"><code>chat_completion</code></a>.</p>
<p><strong>Out-of-scope Uses</strong> Use in any manner that violates applicable laws or regulations (including trade compliance laws).Use in languages other than English. Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Llama 2.</p>
<h2 class="relative group flex items-center">
<a rel="noopener nofollow" href="#hardware-and-software" class="block pr-1.5 text-lg md:absolute md:p-1.5 md:opacity-0 md:group-hover:opacity-100 md:right-full" id="hardware-and-software">
<span class="header-link"><svg viewBox="0 0 256 256" preserveAspectRatio="xMidYMid meet" height="1em" width="1em" role="img" aria-hidden="true" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" class="text-gray-500 hover:text-black w-4"><path fill="currentColor" d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z"></path></svg></span>
</a>
<span>
Hardware and Software
</span>
</h2>
<p><strong>Training Factors</strong> We used custom training libraries, Meta's Research Super Cluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute.</p>
<p><strong>Carbon Footprint</strong> Pretraining utilized a cumulative 3.3M GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 539 tCO2eq, 100% of which were offset by Meta’s sustainability program.</p>
<div class="max-w-full overflow-auto">
<table>
<thead><tr>
<th></th>
<th>Time (GPU hours)</th>
<th>Power Consumption (W)</th>
<th>Carbon Emitted(tCO<sub>2</sub>eq)</th>
</tr>
</thead><tbody><tr>
<td>Llama 2 7B</td>
<td>184320</td>
<td>400</td>
<td>31.22</td>
</tr>
<tr>
<td>Llama 2 13B</td>
<td>368640</td>
<td>400</td>
<td>62.44</td>
</tr>
<tr>
<td>Llama 2 70B</td>
<td>1720320</td>
<td>400</td>
<td>291.42</td>
</tr>
<tr>
<td>Total</td>
<td>3311616</td>
<td></td>
<td>539.00</td>
</tr>
</tbody>
</table>
</div>
<p><strong>CO<sub>2</sub> emissions during pretraining.</strong> Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others.</p>
<h2 class="relative group flex items-center">
<a rel="noopener nofollow" href="#training-data" class="block pr-1.5 text-lg md:absolute md:p-1.5 md:opacity-0 md:group-hover:opacity-100 md:right-full" id="training-data">
<span class="header-link"><svg viewBox="0 0 256 256" preserveAspectRatio="xMidYMid meet" height="1em" width="1em" role="img" aria-hidden="true" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" class="text-gray-500 hover:text-black w-4"><path fill="currentColor" d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z"></path></svg></span>
</a>
<span>
Training Data
</span>
</h2>
<p><strong>Overview</strong> Llama 2 was pretrained on 2 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over one million new human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data.</p>
<p><strong>Data Freshness</strong> The pretraining data has a cutoff of September 2022, but some tuning data is more recent, up to July 2023.</p>
<h2 class="relative group flex items-center">
<a rel="noopener nofollow" href="#evaluation-results" class="block pr-1.5 text-lg md:absolute md:p-1.5 md:opacity-0 md:group-hover:opacity-100 md:right-full" id="evaluation-results">
<span class="header-link"><svg viewBox="0 0 256 256" preserveAspectRatio="xMidYMid meet" height="1em" width="1em" role="img" aria-hidden="true" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" class="text-gray-500 hover:text-black w-4"><path fill="currentColor" d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z"></path></svg></span>
</a>
<span>
Evaluation Results
</span>
</h2>
<p>In this section, we report the results for the Llama 1 and Llama 2 models on standard academic benchmarks.For all the evaluations, we use our internal evaluations library.</p>
<div class="max-w-full overflow-auto">
<table>
<thead><tr>
<th>Model</th>
<th>Size</th>
<th>Code</th>
<th>Commonsense Reasoning</th>
<th>World Knowledge</th>
<th>Reading Comprehension</th>
<th>Math</th>
<th>MMLU</th>
<th>BBH</th>
<th>AGI Eval</th>
</tr>
</thead><tbody><tr>
<td>Llama 1</td>
<td>7B</td>
<td>14.1</td>
<td>60.8</td>
<td>46.2</td>
<td>58.5</td>
<td>6.95</td>
<td>35.1</td>
<td>30.3</td>
<td>23.9</td>
</tr>
<tr>
<td>Llama 1</td>
<td>13B</td>
<td>18.9</td>
<td>66.1</td>
<td>52.6</td>
<td>62.3</td>
<td>10.9</td>
<td>46.9</td>
<td>37.0</td>
<td>33.9</td>
</tr>
<tr>
<td>Llama 1</td>
<td>33B</td>
<td>26.0</td>
<td>70.0</td>
<td>58.4</td>
<td>67.6</td>
<td>21.4</td>
<td>57.8</td>
<td>39.8</td>
<td>41.7</td>
</tr>
<tr>
<td>Llama 1</td>
<td>65B</td>
<td>30.7</td>
<td>70.7</td>
<td>60.5</td>
<td>68.6</td>
<td>30.8</td>
<td>63.4</td>
<td>43.5</td>
<td>47.6</td>
</tr>
<tr>
<td>Llama 2</td>
<td>7B</td>
<td>16.8</td>
<td>63.9</td>
<td>48.9</td>
<td>61.3</td>
<td>14.6</td>
<td>45.3</td>
<td>32.6</td>
<td>29.3</td>
</tr>
<tr>
<td>Llama 2</td>
<td>13B</td>
<td>24.5</td>
<td>66.9</td>
<td>55.4</td>
<td>65.8</td>
<td>28.7</td>
<td>54.8</td>
<td>39.4</td>
<td>39.1</td>
</tr>
<tr>
<td>Llama 2</td>
<td>70B</td>
<td><strong>37.5</strong></td>
<td><strong>71.9</strong></td>
<td><strong>63.6</strong></td>
<td><strong>69.4</strong></td>
<td><strong>35.2</strong></td>
<td><strong>68.9</strong></td>
<td><strong>51.2</strong></td>
<td><strong>54.2</strong></td>
</tr>
</tbody>
</table>
</div>
<p><strong>Overall performance on grouped academic benchmarks.</strong> <em>Code:</em> We report the average pass@1 scores of our models on HumanEval and MBPP. <em>Commonsense Reasoning:</em> We report the average of PIQA, SIQA, HellaSwag, WinoGrande, ARC easy and challenge, OpenBookQA, and CommonsenseQA. We report 7-shot results for CommonSenseQA and 0-shot results for all other benchmarks. <em>World Knowledge:</em> We evaluate the 5-shot performance on NaturalQuestions and TriviaQA and report the average. <em>Reading Comprehension:</em> For reading comprehension, we report the 0-shot average on SQuAD, QuAC, and BoolQ. <em>MATH:</em> We report the average of the GSM8K (8 shot) and MATH (4 shot) benchmarks at top 1.</p>
<div class="max-w-full overflow-auto">
<table>
<thead><tr>
<th></th>
<th></th>
<th>TruthfulQA</th>
<th>Toxigen</th>
</tr>
</thead><tbody><tr>
<td>Llama 1</td>
<td>7B</td>
<td>27.42</td>
<td>23.00</td>
</tr>
<tr>
<td>Llama 1</td>
<td>13B</td>
<td>41.74</td>
<td>23.08</td>
</tr>
<tr>
<td>Llama 1</td>
<td>33B</td>
<td>44.19</td>
<td>22.57</td>
</tr>
<tr>
<td>Llama 1</td>
<td>65B</td>
<td>48.71</td>
<td>21.77</td>
</tr>
<tr>
<td>Llama 2</td>
<td>7B</td>
<td>33.29</td>
<td><strong>21.25</strong></td>
</tr>
<tr>
<td>Llama 2</td>
<td>13B</td>
<td>41.86</td>
<td>26.10</td>
</tr>
<tr>
<td>Llama 2</td>
<td>70B</td>
<td><strong>50.18</strong></td>
<td>24.60</td>
</tr>
</tbody>
</table>
</div>
<p><strong>Evaluation of pretrained LLMs on automatic safety benchmarks.</strong> For TruthfulQA, we present the percentage of generations that are both truthful and informative (the higher the better). For ToxiGen, we present the percentage of toxic generations (the smaller the better).</p>
<div class="max-w-full overflow-auto">
<table>
<thead><tr>
<th></th>
<th></th>
<th>TruthfulQA</th>
<th>Toxigen</th>
</tr>
</thead><tbody><tr>
<td>Llama-2-Chat</td>
<td>7B</td>
<td>57.04</td>
<td><strong>0.00</strong></td>
</tr>
<tr>
<td>Llama-2-Chat</td>
<td>13B</td>
<td>62.18</td>
<td><strong>0.00</strong></td>
</tr>
<tr>
<td>Llama-2-Chat</td>
<td>70B</td>
<td><strong>64.14</strong></td>
<td>0.01</td>
</tr>
</tbody>
</table>
</div>
<p><strong>Evaluation of fine-tuned LLMs on different safety datasets.</strong> Same metric definitions as above.</p>
<h2 class="relative group flex items-center">
<a rel="noopener nofollow" href="#ethical-considerations-and-limitations" class="block pr-1.5 text-lg md:absolute md:p-1.5 md:opacity-0 md:group-hover:opacity-100 md:right-full" id="ethical-considerations-and-limitations">
<span class="header-link"><svg viewBox="0 0 256 256" preserveAspectRatio="xMidYMid meet" height="1em" width="1em" role="img" aria-hidden="true" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" class="text-gray-500 hover:text-black w-4"><path fill="currentColor" d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z"></path></svg></span>
</a>
<span>
Ethical Considerations and Limitations
</span>
</h2>
<p>Llama 2 is a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Llama 2’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 2, developers should perform safety testing and tuning tailored to their specific applications of the model.</p>
<p>Please see the Responsible Use Guide available at <a rel="noopener nofollow" href="https://ai.meta.com/llama/responsible-use-guide">https://ai.meta.com/llama/responsible-use-guide/</a></p>
<h2 class="relative group flex items-center">
<a rel="noopener nofollow" href="#reporting-issues" class="block pr-1.5 text-lg md:absolute md:p-1.5 md:opacity-0 md:group-hover:opacity-100 md:right-full" id="reporting-issues">
<span class="header-link"><svg viewBox="0 0 256 256" preserveAspectRatio="xMidYMid meet" height="1em" width="1em" role="img" aria-hidden="true" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" class="text-gray-500 hover:text-black w-4"><path fill="currentColor" d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z"></path></svg></span>
</a>
<span>
Reporting Issues
</span>
</h2>
<p>Please report any software “bug,” or other problems with the models through one of the following means:</p>
<ul>
<li>Reporting issues with the model: <a rel="noopener nofollow" href="http://github.com/facebookresearch/llama">github.com/facebookresearch/llama</a></li>
<li>Reporting problematic content generated by the model: <a rel="noopener nofollow" href="http://developers.facebook.com/llama_output_feedback">developers.facebook.com/llama_output_feedback</a></li>
<li>Reporting bugs and security concerns: <a rel="noopener nofollow" href="http://facebook.com/whitehat/info">facebook.com/whitehat/info</a></li>
</ul>
<h2 class="relative group flex items-center">
<a rel="noopener nofollow" href="#llama-model-index" class="block pr-1.5 text-lg md:absolute md:p-1.5 md:opacity-0 md:group-hover:opacity-100 md:right-full" id="llama-model-index">
<span class="header-link"><svg viewBox="0 0 256 256" preserveAspectRatio="xMidYMid meet" height="1em" width="1em" role="img" aria-hidden="true" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" class="text-gray-500 hover:text-black w-4"><path fill="currentColor" d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z"></path></svg></span>
</a>
<span>
Llama Model Index
</span>
</h2>
<div class="max-w-full overflow-auto">
<table>
<thead><tr>
<th>Model</th>
<th>Llama2</th>
<th>Llama2-hf</th>
<th>Llama2-chat</th>
<th>Llama2-chat-hf</th>
</tr>
</thead><tbody><tr>
<td>7B</td>
<td><a rel="noopener nofollow" href="https://huggingface.co/llamaste/Llama-2-7b">Link</a></td>
<td><a rel="noopener nofollow" href="https://huggingface.co/llamaste/Llama-2-7b-hf">Link</a></td>
<td><a rel="noopener nofollow" href="https://huggingface.co/llamaste/Llama-2-7b-chat">Link</a></td>
<td><a rel="noopener nofollow" href="https://huggingface.co/llamaste/Llama-2-7b-chat-hf">Link</a></td>
</tr>
<tr>
<td>13B</td>
<td><a rel="noopener nofollow" href="https://huggingface.co/llamaste/Llama-2-13b">Link</a></td>
<td><a rel="noopener nofollow" href="https://huggingface.co/llamaste/Llama-2-13b-hf">Link</a></td>
<td><a rel="noopener nofollow" href="https://huggingface.co/llamaste/Llama-2-13b-chat">Link</a></td>
<td><a rel="noopener nofollow" href="https://huggingface.co/llamaste/Llama-2-13b-hf">Link</a></td>
</tr>
<tr>
<td>70B</td>
<td><a rel="noopener nofollow" href="https://huggingface.co/llamaste/Llama-2-70b">Link</a></td>
<td><a rel="noopener nofollow" href="https://huggingface.co/llamaste/Llama-2-70b-hf">Link</a></td>
<td><a rel="noopener nofollow" href="https://huggingface.co/llamaste/Llama-2-70b-chat">Link</a></td>
<td><a rel="noopener nofollow" href="https://huggingface.co/llamaste/Llama-2-70b-hf">Link</a></td>
</tr>
</tbody>
</table>
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