CINXE.COM

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<div class="card--post-attributes-secondary card--post-attributes-header"> <div class="post--rate secondary--attribute"> <div class="wpulike wpulike-heart " ><div class="wp_ulike_general_class wp_ulike_is_not_liked"><button type="button" aria-label="Like Button" data-ulike-id="95756" data-ulike-nonce="e0111ab8db" data-ulike-type="post" data-ulike-template="wpulike-heart" data-ulike-display-likers="" data-ulike-likers-style="popover" class="wp_ulike_btn wp_ulike_put_text wp_post_btn_95756"><span><i class="far fa-thumbs-up"></i></span> </button><span class="count-box wp_ulike_counter_up" data-ulike-counter-value="+11"></span> </div></div> <span class="js-like-click like-click"> Like </span> </div> <div class="post--comments-count secondary--attribute"> <a href="#entry-content-comments"> <i class="fad fa-comment-alt-lines"></i> Discuss (1) </a> </div> </div> </div> <img width="1999" height="1125" 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href="https://www.linkedin.com/sharing/share-offsite/?url=https%3A%2F%2Fdeveloper.nvidia.com%2Fblog%2Fimproving-translation-quality-with-domain-specific-fine-tuning-and-nvidia-nim%2F" class="for-linkedin" target="_blank" rel="follow external noopener">L</a></li> <li><a data-wpel-link="external" href="https://twitter.com/intent/tweet?text=Improving+Translation+Quality+with+Domain-Specific+Fine-Tuning+and+NVIDIA+NIM+%7C+NVIDIA+Technical+Blog+https%3A%2F%2Fdeveloper.nvidia.com%2Fblog%2Fimproving-translation-quality-with-domain-specific-fine-tuning-and-nvidia-nim%2F" class="for-twitter" target="_blank" rel="follow external noopener">T</a></li> <li><a data-wpel-link="external" href="https://www.facebook.com/sharer/sharer.php?u=https%3A%2F%2Fdeveloper.nvidia.com%2Fblog%2Fimproving-translation-quality-with-domain-specific-fine-tuning-and-nvidia-nim%2F" class="for-facebook" target="_blank" rel="follow external noopener">F</a></li> <li><a data-wpel-link="external" href="https://www.reddit.com/submit?url=https%3A%2F%2Fdeveloper.nvidia.com%2Fblog%2Fimproving-translation-quality-with-domain-specific-fine-tuning-and-nvidia-nim%2F&title=Improving+Translation+Quality+with+Domain-Specific+Fine-Tuning+and+NVIDIA+NIM+%7C+NVIDIA+Technical+Blog" class="for-reddit" target="_blank" rel="follow external noopener">R</a></li> <li><a href="mailto:?subject=I'd like to share a link with you&body=https%3A%2F%2Fdeveloper.nvidia.com%2Fblog%2Fimproving-translation-quality-with-domain-specific-fine-tuning-and-nvidia-nim%2F" class="for-mail">E</a></li> </ul> </div> <div class="entry-content"> <p>Translation plays an essential role in enabling companies to expand across borders, with requirements varying significantly in terms of tone, accuracy, and technical terminology handling. The emergence of <a href="https://blogs.nvidia.com/blog/what-is-sovereign-ai/" data-wpel-link="internal" target="_blank" rel="follow noopener">sovereign AI</a> has highlighted critical challenges in <a href="https://www.nvidia.com/en-us/glossary/large-language-models/" data-wpel-link="internal" target="_blank" rel="follow noopener">large language models (LLMs)</a>, particularly their struggle to capture nuanced cultural and linguistic contexts beyond English-dominant frameworks. As global communication becomes increasingly complex, organizations must carefully evaluate translation solutions that balance technological efficiency with cultural sensitivity and linguistic precision.</p> <p>In this post, we explore how LLMs can address the following two distinct English to Traditional Chinese translation use cases:</p> <ul class="wp-block-list"> <li><strong>Marketing content for websites</strong>: Translating technical text with precision while maintaining a natural promotional tone.</li> <li><strong>Online training courses:</strong> Translating slide text and markdown content used in platforms like Jupyter Notebooks, ensuring accurate technical translation and proper markdown formatting such as headings, sections, and hyperlinks.</li> </ul> <p>These use cases require a specialized approach beyond general translation. While <a href="https://developer.nvidia.com/blog/an-introduction-to-large-language-models-prompt-engineering-and-p-tuning/" data-wpel-link="internal" target="_blank" rel="follow noopener">prompt engineering </a>with instruction-tuned LLMs can handle certain contexts, more refined tasks like these often do not meet expectations. This is where fine-tuning <a href="https://arxiv.org/pdf/2106.09685" data-wpel-link="exclude">Low-Rank Adaptation (LoRA)</a> adapters separately on collected datasets specific to each translation context becomes essential.</p> <h2 id="implementing_lora_adapters_for_domain-specific_translation " class="wp-block-heading">Implementing LoRA adapters for domain-specific translation <a href="#implementing_lora_adapters_for_domain-specific_translation " class="heading-anchor-link"><i class="fas fa-link"></i></a></h2> <p>For this project, we are using <a href="https://build.nvidia.com/meta/llama-3_1-8b-instruct" data-wpel-link="internal" target="_blank" rel="follow noopener">Llama 3.1 8B Instruct</a> as the pretrained model and implementing two models fine-tuned with LoRA adapters using <a href="https://docs.nvidia.com/nemo-framework/index.html" data-wpel-link="internal" target="_blank" rel="follow noopener">NVIDIA NeMo Framework</a>. These adapters were trained on domain-specific datasets—one for marketing website content and one for online training courses. For easy deployment of LLMs with <a href="https://developer.nvidia.com/blog/seamlessly-deploying-a-swarm-of-lora-adapters-with-nvidia-nim/" data-wpel-link="internal" target="_blank" rel="follow noopener">simultaneous use of multiple LoRA adapters</a> on the same pretrained model, we are using <a href="https://build.nvidia.com/search?q=LLM" data-wpel-link="internal" target="_blank" rel="follow noopener">NVIDIA NIM</a>. </p><div class='code-block code-block-2' style='margin: 40px auto; text-align: center; display: block; clear: both;'> <div style="background: #efefef 0% 0% no-repeat padding-box;margin-top: -8px;margin-bottom: -8px;border-top:1px solid #ccc;border-bottom:1px solid #ccc;"><div style="max-width:1360px;margin:auto;"><div id="metaslider-id-96052" style="width: 100%; margin: 0 auto;" class="ml-slider-3-60-1 metaslider metaslider-flex metaslider-96052 ml-slider ms-theme-default nav-hidden" role="region" aria-roledescription="Slideshow" aria-label="New Slideshow"> <div id="metaslider_container_96052"> <div id="metaslider_96052" class="flexslider"> <ul aria-live="polite" class="slides"> <li style="display: block; width: 100%;" class="slide-96054 ms-image" aria-roledescription="slide" aria-label="slide-96054"><a href="https://nvda.ws/3WDuyIk" target="_blank" data-wpel-link="external" rel="follow external noopener"><img width="1360" height="180" src="https://developer-blogs.nvidia.com/wp-content/uploads/2025/02/gtc25-banner-reg-now-1360x180-inpost-1.jpg" class="slider-96052 slide-96054" alt="NVIDIA GTC 2025" rel="" title="NVIDIA GTC 2025" srcset="https://developer-blogs.nvidia.com/wp-content/uploads/2025/02/gtc25-banner-reg-now-1360x180-inpost-1.jpg 1360w, https://developer-blogs.nvidia.com/wp-content/uploads/2025/02/gtc25-banner-reg-now-1360x180-inpost-1-300x40.jpg 300w, https://developer-blogs.nvidia.com/wp-content/uploads/2025/02/gtc25-banner-reg-now-1360x180-inpost-1-625x83.jpg 625w, https://developer-blogs.nvidia.com/wp-content/uploads/2025/02/gtc25-banner-reg-now-1360x180-inpost-1-179x24.jpg 179w, https://developer-blogs.nvidia.com/wp-content/uploads/2025/02/gtc25-banner-reg-now-1360x180-inpost-1-768x102.jpg 768w, https://developer-blogs.nvidia.com/wp-content/uploads/2025/02/gtc25-banner-reg-now-1360x180-inpost-1-645x85.jpg 645w, https://developer-blogs.nvidia.com/wp-content/uploads/2025/02/gtc25-banner-reg-now-1360x180-inpost-1-500x66.jpg 500w, https://developer-blogs.nvidia.com/wp-content/uploads/2025/02/gtc25-banner-reg-now-1360x180-inpost-1-160x21.jpg 160w, https://developer-blogs.nvidia.com/wp-content/uploads/2025/02/gtc25-banner-reg-now-1360x180-inpost-1-362x48.jpg 362w, https://developer-blogs.nvidia.com/wp-content/uploads/2025/02/gtc25-banner-reg-now-1360x180-inpost-1-831x110.jpg 831w, https://developer-blogs.nvidia.com/wp-content/uploads/2025/02/gtc25-banner-reg-now-1360x180-inpost-1-1024x136.jpg 1024w" sizes="(max-width: 1360px) 100vw, 1360px" /></a></li> </ul> </div> </div> </div></div></div></div> <p>Refer to the <a href="https://github.com/brevdev/notebooks/blob/main/llama31_law.ipynb" data-wpel-link="external" target="_blank" rel="follow external noopener">Jupyter Notebook</a> to guide you through executing LoRA fine-tuning with NeMo.</p> <h2 id="optimizing_llm_deployment_with_lora_and_nvidia_nim" class="wp-block-heading">Optimizing LLM deployment with LoRA and NVIDIA NIM<a href="#optimizing_llm_deployment_with_lora_and_nvidia_nim" class="heading-anchor-link"><i class="fas fa-link"></i></a></h2> <p>NVIDIA NIM introduces a new level of performance, reliability, agility, and control for deploying professional LLM services. With prebuilt containers and optimized model engines tailored for different GPU types, you can easily deploy LLMs while boosting service performance. In addition to popular pretrained models including the Meta <a href="https://build.nvidia.com/search?q=llama-3" data-wpel-link="internal" target="_blank" rel="follow noopener">Llama 3</a> family and Mistral AI <a href="https://build.nvidia.com/nim?q=mistral-nemo+OR+mistral-nemo" data-wpel-link="internal" target="_blank" rel="follow noopener">Mistral</a> and <a href="https://build.nvidia.com/nim?q=Mixtral" data-wpel-link="internal" target="_blank" rel="follow noopener">Mixtral</a> models, you can integrate and fine-tune your own models with NIM, further enhancing its capabilities.</p> <p>LoRA is a powerful customization technique that enables efficient fine-tuning by adjusting only a subset of the model’s parameters. This significantly reduces required computational resources. LoRA has become popular due to its effectiveness and efficiency. Unlike full-parameter fine-tuning, LoRA adapter weights are smaller and can be stored separately from the pretrained model, providing greater flexibility in deployment. </p> <p><a href="https://github.com/NVIDIA/TensorRT-LLM" data-wpel-link="external" target="_blank" rel="follow external noopener">NVIDIA TensorRT-LLM</a> has established a mechanism that can simultaneously serve <a href="https://developer.nvidia.com/blog/tune-and-deploy-lora-llms-with-nvidia-tensorrt-llm/" data-wpel-link="internal" target="_blank" rel="follow noopener">multiple LoRA adapters</a> on the same pretrained model. This <a href="https://docs.nvidia.com/nim/large-language-models/latest/peft.html" data-wpel-link="internal" target="_blank" rel="follow noopener">multi-adapter mechanism is also supported by NIM</a>. </p> <p>The following sections demonstrate the advantages of these features and application in multipurpose translation tasks.</p> <h2 id="step-by-step_lora_fine-tuning_deployment_with_nvidia_llm_nim" class="wp-block-heading">Step-by-step LoRA fine-tuning deployment with NVIDIA LLM NIM<a href="#step-by-step_lora_fine-tuning_deployment_with_nvidia_llm_nim" class="heading-anchor-link"><i class="fas fa-link"></i></a></h2> <p>This section describes the three steps involved in LoRA fine-tuning deployment using NVIDIA LLM NIM.</p> <h3 id="step_1_set_up_the_nim_instance_and_lora_models" class="wp-block-heading">Step 1: Set up the NIM instance and LoRA models<a href="#step_1_set_up_the_nim_instance_and_lora_models" class="heading-anchor-link"><i class="fas fa-link"></i></a></h3> <p>First, launch a computational instance equipped with two NVIDIA L40S GPUs as recommended in the <a href="https://docs.nvidia.com/nim/large-language-models/latest/support-matrix.html#llama-3-1-8b-instruct" data-wpel-link="internal" target="_blank" rel="follow noopener">NIM support matrix</a>. </p> <p>Next, upload the two fine-tuned NeMo files to this environment. Detailed examples of LoRA fine-tuning using NeMo Framework are available in the <a href="https://docs.nvidia.com/nemo-framework/user-guide/24.07/playbooks/nemoframeworkpeft.html" data-wpel-link="internal" target="_blank" rel="follow noopener">official documentation</a> and a <a href="https://github.com/brevdev/notebooks/blob/main/llama31_law.ipynb" data-wpel-link="external" target="_blank" rel="follow external noopener">Jupyter Notebook</a>. </p> <p>To organize the environment, use the following command to create directories for storing the LoRA adapters:</p> <div class="wp-block-syntaxhighlighter-code "><pre class="brush: plain; title: ; notranslate" title=""> $ mkdir -p loras/llama-3.1-8b-translate-course $ mkdir -p loras/llama-3.1-8b-translate-web $ export LOCAL_PEFT_DIRECTORY=$(pwd)/loras $ chmod -R 777 $(pwd)/loras $ tree loras loras ├── llama-3.1-8b-translate-course │ └── course.nemo └── llama-3.1-8b-translate-web └── web.nemo 2 directories, 2 files </pre></div> <h3 id="step_2_deploy_nim_and_lora_models " class="wp-block-heading">Step 2: Deploy NIM and LoRA models <a href="#step_2_deploy_nim_and_lora_models " class="heading-anchor-link"><i class="fas fa-link"></i></a></h3> <p>Now, you can proceed to deploy the NIM container. Replace <NGC_API_KEY> with your actual NGC API token. <a href="https://docs.nvidia.com/nim/large-language-models/latest/getting-started.html#generate-an-api-key" data-wpel-link="internal" target="_blank" rel="follow noopener">Generate an API key</a> if needed. Then run the following commands:</p> <div class="wp-block-syntaxhighlighter-code "><pre class="brush: plain; title: ; notranslate" title=""> $ export NGC_API_KEY=<NGC_API_KEY> $ export LOCAL_PEFT_DIRECTORY=$(pwd)/loras $ export NIM_PEFT_SOURCE=/home/nvs/loras $ export CONTAINER_NAME=nim-llama-3.1-8b-instruct $ export NIM_CACHE_PATH=$(pwd)/nim-cache $ mkdir -p "$NIM_CACHE_PATH" $ chmod -R 777 $NIM_CACHE_PATH $ echo "$NGC_API_KEY" | docker login nvcr.io --username '$oauthtoken' --password-stdin $ docker run -it --rm --name=$CONTAINER_NAME \ --runtime=nvidia \ --gpus all \ --shm-size=16GB \ -e NGC_API_KEY=$NGC_API_KEY \ -e NIM_PEFT_SOURCE \ -v $NIM_CACHE_PATH:/opt/nim/.cache \ -v $LOCAL_PEFT_DIRECTORY:$NIM_PEFT_SOURCE \ -p 8000:8000 \ nvcr.io/nim/meta/llama-3.1-8b-instruct:1.1.2 </pre></div> <p>After executing these steps, NIM will load the model. Once complete, you can check the health status and retrieve the model names for both the pretrained model and LoRA models using the following commands:</p> <div class="wp-block-syntaxhighlighter-code "><pre class="brush: plain; title: ; notranslate" title=""> # NIM health status $ curl http://<INSTANCE_URL>:8000/v1/health/ready # Get model names of the base model and LoRA models $ curl http://<INSTANCE_URL>:8000/v1/models | jq { "data" : [ { "created" : 1725516389, "id" : "meta/llama-3.1-8b-instruct", "max_model_len" : 131072, "object" : "model", "owned_by" : "system", "parent" : null, "permission" : [ { "allow_create_engine" : false, "allow_fine_tuning" : false, "allow_logprobs" : true, "allow_sampling" : true, "allow_search_indices" : false, "allow_view" : true, "created" : 1725516389, "group" : null, "id" : "modelperm-2274791587e4456b9ce921621377becb", "is_blocking" : false, "object" : "model_permission", "organization" : "*" } ], "root" : "meta/llama-3.1-8b-instruct" }, { "created" : 1725516389, "id" : "llama-3.1-8b-translate-course", "max_model_len" : null, "object" : "model", "owned_by" : "system", "parent" : null, "permission" : [ { "allow_create_engine" : false, "allow_fine_tuning" : false, "allow_logprobs" : true, "allow_sampling" : true, "allow_search_indices" : false, "allow_view" : true, "created" : 1725516389, "group" : null, "id" : "modelperm-cb8be2bce8db442d8347f259966e2c02", "is_blocking" : false, "object" : "model_permission", "organization" : "*" } ], "root" : "meta/llama-3.1-8b-instruct" }, { "created" : 1725516389, "id" : "llama-3.1-8b-translate-web", "max_model_len" : null, "object" : "model", "owned_by" : "system", "parent" : null, "permission" : [ { "allow_create_engine" : false, "allow_fine_tuning" : false, "allow_logprobs" : true, "allow_sampling" : true, "allow_search_indices" : false, "allow_view" : true, "created" : 1725516389, "group" : null, "id" : "modelperm-8e404c4d9f504e5fae92bf6caf04e93c", "is_blocking" : false, "object" : "model_permission", "organization" : "*" } ], "root" : "meta/llama-3.1-8b-instruct" } ], "object" : "list" } </pre></div> <p>The output will display details of the models available for deployment.</p> <h3 id="step_3_evaluate_translation_quality_of_fine-tuned_lora_models" class="wp-block-heading">Step 3: Evaluate translation quality of fine-tuned LoRA models<a href="#step_3_evaluate_translation_quality_of_fine-tuned_lora_models" class="heading-anchor-link"><i class="fas fa-link"></i></a></h3> <p>With NIM running, you can use it to perform English to Traditional Chinese translation, specifying the appropriate LoRA model name in the request body. </p> <h2 id="fine-tuning_results_and_performance_metrics" class="wp-block-heading">Fine-tuning results and performance metrics<a href="#fine-tuning_results_and_performance_metrics" class="heading-anchor-link"><i class="fas fa-link"></i></a></h2> <p>We evaluated the translation quality of two test datasets using the pretrained model and the two fine-tuned LoRA models. The BLEU and COMET scores from these evaluations are shown in Figures 1 and 2.</p> <figure class="wp-block-image aligncenter size-full"><img decoding="async" width="1200" height="742" src="https://developer-blogs.nvidia.com/wp-content/uploads/2025/02/bleu-scores-test-datasets-base-model-lora-fine-tuned.png" alt="A graph showing BLEU scores for the Course and Web dataset, comparing base model with LoRA. " class="wp-image-95776" srcset="https://developer-blogs.nvidia.com/wp-content/uploads/2025/02/bleu-scores-test-datasets-base-model-lora-fine-tuned.png 1200w, https://developer-blogs.nvidia.com/wp-content/uploads/2025/02/bleu-scores-test-datasets-base-model-lora-fine-tuned-300x186.png 300w, https://developer-blogs.nvidia.com/wp-content/uploads/2025/02/bleu-scores-test-datasets-base-model-lora-fine-tuned-625x386.png 625w, https://developer-blogs.nvidia.com/wp-content/uploads/2025/02/bleu-scores-test-datasets-base-model-lora-fine-tuned-179x111.png 179w, https://developer-blogs.nvidia.com/wp-content/uploads/2025/02/bleu-scores-test-datasets-base-model-lora-fine-tuned-768x475.png 768w, https://developer-blogs.nvidia.com/wp-content/uploads/2025/02/bleu-scores-test-datasets-base-model-lora-fine-tuned-645x399.png 645w, https://developer-blogs.nvidia.com/wp-content/uploads/2025/02/bleu-scores-test-datasets-base-model-lora-fine-tuned-485x300.png 485w, https://developer-blogs.nvidia.com/wp-content/uploads/2025/02/bleu-scores-test-datasets-base-model-lora-fine-tuned-146x90.png 146w, https://developer-blogs.nvidia.com/wp-content/uploads/2025/02/bleu-scores-test-datasets-base-model-lora-fine-tuned-362x224.png 362w, https://developer-blogs.nvidia.com/wp-content/uploads/2025/02/bleu-scores-test-datasets-base-model-lora-fine-tuned-178x110.png 178w, https://developer-blogs.nvidia.com/wp-content/uploads/2025/02/bleu-scores-test-datasets-base-model-lora-fine-tuned-1024x633.png 1024w" sizes="(max-width: 1200px) 100vw, 1200px" /><figcaption class="wp-element-caption"><em><em>Figure 1. BLEU scores (higher is better) of different test datasets using the base model and two LoRA fine-tuned models</em></em></figcaption></figure> <figure class="wp-block-image aligncenter size-full"><img decoding="async" width="1200" height="742" src="https://developer-blogs.nvidia.com/wp-content/uploads/2025/02/comet-scores-test-datasets-base-model-lora.png" alt="A graph showing COMET scores for the Course and Web dataset, comparing base model with LoRA. " class="wp-image-95777" srcset="https://developer-blogs.nvidia.com/wp-content/uploads/2025/02/comet-scores-test-datasets-base-model-lora.png 1200w, https://developer-blogs.nvidia.com/wp-content/uploads/2025/02/comet-scores-test-datasets-base-model-lora-300x186.png 300w, https://developer-blogs.nvidia.com/wp-content/uploads/2025/02/comet-scores-test-datasets-base-model-lora-625x386.png 625w, https://developer-blogs.nvidia.com/wp-content/uploads/2025/02/comet-scores-test-datasets-base-model-lora-179x111.png 179w, https://developer-blogs.nvidia.com/wp-content/uploads/2025/02/comet-scores-test-datasets-base-model-lora-768x475.png 768w, https://developer-blogs.nvidia.com/wp-content/uploads/2025/02/comet-scores-test-datasets-base-model-lora-645x399.png 645w, https://developer-blogs.nvidia.com/wp-content/uploads/2025/02/comet-scores-test-datasets-base-model-lora-485x300.png 485w, https://developer-blogs.nvidia.com/wp-content/uploads/2025/02/comet-scores-test-datasets-base-model-lora-146x90.png 146w, https://developer-blogs.nvidia.com/wp-content/uploads/2025/02/comet-scores-test-datasets-base-model-lora-362x224.png 362w, https://developer-blogs.nvidia.com/wp-content/uploads/2025/02/comet-scores-test-datasets-base-model-lora-178x110.png 178w, https://developer-blogs.nvidia.com/wp-content/uploads/2025/02/comet-scores-test-datasets-base-model-lora-1024x633.png 1024w" sizes="(max-width: 1200px) 100vw, 1200px" /><figcaption class="wp-element-caption"><em><em>Figure 2. COMET scores (higher is better) of different test datasets using the base model and two LoRA models</em></em></figcaption></figure> <p>These results demonstrate that fine-tuning specific datasets using the LoRA mechanism significantly enhances translation quality within their respective domains. To achieve the best performance, domain-specific datasets should leverage their corresponding fine-tuned LoRA models.</p> <p>The LoRA model fine-tuned on the web dataset demonstrated a more noticeable improvement in evaluation scores for web-related translations. Table 1 provides a comparison of translation examples.</p> <figure class="wp-block-table aligncenter"><table class="has-fixed-layout"><tbody><tr><td><strong>Source Text</strong></td><td><strong>Pretrained Model Output</strong></td><td><strong>LoRA Fine-Tuned Model (on Web Dataset) Output </strong></td></tr><tr><td>NVIDIA extends lead on MLPerf inference benchmark for computer vision, conversational AI, and recommender workloads.</td><td>NVIDIA 在計算視覺、對話式人工智慧和建議系統工作負載方面，領先於 MLPerf 推理基準。</td><td>NVIDIA 在電腦視覺、對話式人工智慧和推薦系統工作負載的 MLPerf 推論基準中擴大領先地位。</td></tr><tr><td>Built on NVIDIA Omniverse, NVIDIA RTX Remix enables modders to easily capture game assets, automatically enhance materials with generative AI tools, and quickly create stunning RTX remasters with full ray tracing and DLSS 3.</td><td>在 NVIDIA Omniverse 上建立，RTX Remix 允許模組開發者輕鬆捕捉遊戲資源、自動使用生成 AI 工具增強材質，並快速創建具有全實時光線追蹤和 DLSS 3 的 RTX 重製版。</td><td>RTX Remix 是以 NVIDIA Omniverse 為基礎，讓模組玩家能輕鬆擷取遊戲素材、使用生成式人工智慧工具自動增強材質，並快速創造出充分採用全光線追蹤技術和 DLSS 3 的令人驚豔的 RTX 重製版。</td></tr><tr><td colspan="3">Note that the prompt template is as follows:<br><em><|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\nYou are an AI assistant who translates an English context to traditional Chinese. The context will be given after a prompt “Translate the following input text to traditional Chinese”. Never respond text other than translation results.<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nTranslate the following input text to traditional Chinese.\n\n</em><strong><em>{English Text}</em></strong><em>.\n<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n</em></td></tr></tbody></table><figcaption class="wp-element-caption"><em><em>Table 1. Sentence translation comparison for the web dataset</em></em></figcaption></figure> <h2 id="explore_further_with_nvidia_nim_and_lora_fine-tuning" class="wp-block-heading">Explore further with NVIDIA NIM and LoRA fine-tuning<a href="#explore_further_with_nvidia_nim_and_lora_fine-tuning" class="heading-anchor-link"><i class="fas fa-link"></i></a></h2> <p>Fine-tuning <a href="https://github.com/brevdev/notebooks/blob/main/llama31_law.ipynb" data-wpel-link="external" target="_blank" rel="follow external noopener">LoRA adapters</a> separately on marketing content from websites and online training course datasets significantly improved translation quality. This demonstrates that domain-specific datasets achieve better results when paired with their own LoRA models, which efficiently adjust the weights of pretrained models to enhance performance. Deploying these fine-tuned models within a single NVIDIA NIM instance provides a GPU-efficient solution for serving multiple specialized tasks simultaneously. </p> <p>Ready to take it further? Explore how <a href="https://www.nvidia.com/en-us/ai/" data-wpel-link="internal" target="_blank" rel="follow noopener">NVIDIA NIM</a> microservices can help you deploy and fine-tune LLMs for your specific tasks. With <a href="https://www.nvidia.com/en-us/ai-data-science/products/nemo/" data-wpel-link="internal" target="_blank" rel="follow noopener">NVIDIA NeMo</a>, you can fine-tune popular models such as <a href="https://build.nvidia.com/nim?q=Llama-3" data-wpel-link="internal" target="_blank" rel="follow noopener">Llama 3</a>, <a href="https://build.nvidia.com/nim?q=mistral-nemo+OR+mistral-nemo" data-wpel-link="internal" target="_blank" rel="follow noopener">Mistral</a>, and <a href="https://build.nvidia.com/search?q=phi" data-wpel-link="internal" target="_blank" rel="follow noopener">Phi</a> using <a href="https://docs.nvidia.com/nim/large-language-models/latest/peft.html" data-wpel-link="internal" target="_blank" rel="follow noopener">LoRA adapters</a>, unlocking greater development efficiency and enhancing the performance of your applications.</p>  </div> <div class="block--prospero-assets"> <h2 class="h--smaller txt-clr--blck"> Related resources </h2> <ul><li>GTC session: <a class='wpel-ignore' target='_blank' href='https://www.nvidia.com/gtc/session-catalog/?tab.catalogallsessionstab=1700692987788001F1cG&search=DLIW73640&ncid=em-even-124008-vt33-23spring#/'>Efficient Large Language Model Customization</a></li><li>GTC session: <a class='wpel-ignore' target='_blank' href='https://www.nvidia.com/gtc/session-catalog/?tab.catalogallsessionstab=1700692987788001F1cG&search=S71886&ncid=em-even-124008-vt33-23spring#/'>Multi-Domain Large Language Model Adaptation Using Synthetic Data Generation</a></li><li>GTC session: <a class='wpel-ignore' target='_blank' href='https://www.nvidia.com/gtc/session-catalog/?tab.catalogallsessionstab=1700692987788001F1cG&search=S72330&ncid=em-even-124008-vt33-23spring#/'>Accelerate Inference on NVIDIA GPUs</a></li><li>NGC Containers: <a class='wpel-ignore' target='_blank' href='https://catalog.ngc.nvidia.com/orgs/nim/teams/microsoft/containers/phi-3-mini-4k-instruct?ncid=em-nurt-245273-vt33'>Phi-3-Mini-4K-Instruct</a></li><li>NGC Containers: <a class='wpel-ignore' target='_blank' href='https://catalog.ngc.nvidia.com/orgs/nim/teams/phind/containers/phind-codellama-34b-v2-instruct?ncid=em-nurt-245273-vt33'>Phind-CodeLlama-34B-v2-Instruct</a></li><li>NGC Containers: <a class='wpel-ignore' target='_blank' href='https://catalog.ngc.nvidia.com/orgs/nim/teams/nvidia/containers/llama-3.1-nemotron-70b-instruct?ncid=em-nurt-245273-vt33'>Llama-3.1-Nemotron-70B-Instruct</a></li></ul> </div> <div class="card--post-attributes-secondary"> <div class="post--comments-count secondary--attribute"> <a href="#entry-content-comments"> <i class="fad fa-comment-alt-lines"></i> Discuss (1) </a> </div> <div class="post--rate secondary--attribute"> <div class="wpulike wpulike-heart " ><div class="wp_ulike_general_class wp_ulike_is_not_liked"><button type="button" aria-label="Like Button" data-ulike-id="95756" data-ulike-nonce="e0111ab8db" data-ulike-type="post" data-ulike-template="wpulike-heart" data-ulike-display-likers="" 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href="https://developer.nvidia.com/blog/recent-posts/?products=NeMo" data-wpel-link="internal" target="_blank" rel="follow noopener">NeMo</a> | <a href="https://developer.nvidia.com/blog/recent-posts/?products=NIM" data-wpel-link="internal" target="_blank" rel="follow noopener">NIM</a> | <a href="https://developer.nvidia.com/blog/recent-posts/?products=TensorRT-LLM" data-wpel-link="internal" target="_blank" rel="follow noopener">TensorRT-LLM</a> | <a href="https://developer.nvidia.com/blog/recent-posts/?learning_levels=Intermediate+Technical" data-wpel-link="internal" target="_blank" rel="follow noopener">Intermediate Technical</a> | <a href="https://developer.nvidia.com/blog/recent-posts/?content_types=Deep+dive" data-wpel-link="internal" target="_blank" rel="follow noopener">Deep dive</a> | <a href="https://developer.nvidia.com/blog/recent-posts/?content_types=Tutorial" data-wpel-link="internal" target="_blank" rel="follow noopener">Tutorial</a> | <a href="https://developer.nvidia.com/blog/tag/featured/" data-wpel-link="internal" target="_blank" rel="follow noopener">featured</a> | <a href="https://developer.nvidia.com/blog/tag/fine-tuning/" data-wpel-link="internal" target="_blank" rel="follow noopener">Fine-Tuning</a> | <a href="https://developer.nvidia.com/blog/tag/large-language-models/" data-wpel-link="internal" target="_blank" rel="follow noopener">LLMs</a> | <a href="https://developer.nvidia.com/blog/tag/translation/" data-wpel-link="internal" target="_blank" rel="follow noopener">Translation</a> </div> </div> <div class="post-authors-list"> <div class="entry-content-author"> <div class="caption"> <h2 class="h--smaller txt-clr--blck"> About the Authors </h2> </div> <div class="media author-info"> <div class="author-media-left media-left"> <img alt='Avatar photo' src='https://developer-blogs.nvidia.com/wp-content/uploads/2025/02/cropped-cheng-han-du-131x131.jpg' srcset='https://developer-blogs.nvidia.com/wp-content/uploads/2025/02/cropped-cheng-han-du-262x262.jpg 2x' class='avatar avatar-131 photo' height='131' width='131' loading='lazy' decoding='async'/> </div> <div class="author-media-body media-body"> <b> About Cheng-Han (Hank) Du </b> <br/> Cheng-Han (Hank) Du is a senior solution engineer at NVIDIA. He focuses on large language models and inference service deployment. He received his bachelor’s degree in Computer Science from National Taiwan University and master’s degree and PhD from the Institute of Photonics and Optoelectronics, National Taiwan University, during which he studied the improvement of numerical simulation methods for wave optics. His work experience includes evaluating the performance of multi-GPU accelerated numerical simulation, consulting software computing technology, and integrating platform software. <div id="author-link"> <a href="https://developer.nvidia.com/blog/author/chenghand/" rel="author follow noopener" data-wpel-link="internal" target="_blank"> View all posts by Cheng-Han (Hank) Du<svg width="16" height="16" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 320 612"><path d="M305 239c9.4 9.4 9.4 24.6 0 33.9L113 465c-9.4 9.4-24.6 9.4-33.9 0s-9.4-24.6 0-33.9l175-175L79 81c-9.4-9.4-9.4-24.6 0-33.9s24.6-9.4 33.9 0L305 239z"/></svg> </a> </div> </div> </div> <div class="media author-info"> <div class="author-media-left media-left"> <img alt='Amit Bleiweiss' src='https://developer-blogs.nvidia.com/wp-content/uploads/2024/01/cropped-amit-bleiweiss-131x131.png' srcset='https://developer-blogs.nvidia.com/wp-content/uploads/2024/01/cropped-amit-bleiweiss.png 2x' class='avatar avatar-131 photo' height='131' width='131' loading='lazy' decoding='async'/> </div> <div class="author-media-body media-body"> <b> About Amit Bleiweiss </b> <br/> Amit Bleiweiss is a senior data scientist at NVIDIA, where he focuses on large language models and generative AI. He has 25 years of experience in applied machine learning and deep learning, with over 50 patents and publications in the domain. Amit received his MSc from Hebrew University of Jerusalem, where he specialized in machine learning. <div id="author-link"> <a href="https://developer.nvidia.com/blog/author/ambleiweiss/" rel="author follow noopener" data-wpel-link="internal" target="_blank"> View all posts by Amit Bleiweiss<svg width="16" height="16" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 320 612"><path d="M305 239c9.4 9.4 9.4 24.6 0 33.9L113 465c-9.4 9.4-24.6 9.4-33.9 0s-9.4-24.6 0-33.9l175-175L79 81c-9.4-9.4-9.4-24.6 0-33.9s24.6-9.4 33.9 0L305 239z"/></svg> </a> </div> </div> </div> </div> </div> <div class="entry-content-comments" id="entry-content-comments"> <div class="container" id="pf-disqus-thread"> <div class="row" id="respond"> <div class="col-md-12 related-posts-comments mb-0"><h2 class="h--smaller txt-clr--blck mb-0">Comments</h2></div> </div> <div class="row"> <div class="col-lg-12 col-md-12 col-sm-12 col-xs-12"> <div class="wpdc-comments-loading" id="wpdc-comments" data-post-id="95756"></div> 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T-I9105|GT-I8510|GT-S6790N|SM-G7105|SM-N9005|GT-S5301|GT-I9295|GT-I9195|SM-C101|GT-S7392|GT-S7560|GT-B7610|GT-I5510|GT-S7582|GT-S7530E|GT-I8750|SM-G9006V|SM-G9008V|SM-G9009D|SM-G900A|SM-G900D|SM-G900F|SM-G900H|SM-G900I|SM-G900J|SM-G900K|SM-G900L|SM-G900M|SM-G900P|SM-G900R4|SM-G900S|SM-G900T|SM-G900V|SM-G900W8|SHV-E160K|SCH-P709|SCH-P729|SM-T2558|GT-I9205|SM-G9350|SM-J120F|SM-G920F|SM-G920V|SM-G930F|SM-N910C|SM-A310F|GT-I9190|SM-J500FN|SM-G903F|SM-J330F|SM-G610F|SM-G981B|SM-G892A|SM-A530F",LG:"\\bLG\\b;|LG[- ]?(C800|C900|E400|E610|E900|E-900|F160|F180K|F180L|F180S|730|855|L160|LS740|LS840|LS970|LU6200|MS690|MS695|MS770|MS840|MS870|MS910|P500|P700|P705|VM696|AS680|AS695|AX840|C729|E970|GS505|272|C395|E739BK|E960|L55C|L75C|LS696|LS860|P769BK|P350|P500|P509|P870|UN272|US730|VS840|VS950|LN272|LN510|LS670|LS855|LW690|MN270|MN510|P509|P769|P930|UN200|UN270|UN510|UN610|US670|US740|US760|UX265|UX840|VN271|VN530|VS660|VS700|VS740|VS750|VS910|VS920|VS930|VX9200|VX11000|AX840A|LW770|P506|P925|P999|E612|D955|D802|MS323|M257)|LM-G710",Sony:"SonyST|SonyLT|SonyEricsson|SonyEricssonLT15iv|LT18i|E10i|LT28h|LT26w|SonyEricssonMT27i|C5303|C6902|C6903|C6906|C6943|D2533|SOV34|601SO|F8332",Asus:"Asus.*Galaxy|PadFone.*Mobile",Xiaomi:"^(?!.*\\bx11\\b).*xiaomi.*$|POCOPHONE F1|MI 8|Redmi Note 9S|Redmi Note 5A Prime|N2G47H|M2001J2G|M2001J2I|M1805E10A|M2004J11G|M1902F1G|M2002J9G|M2004J19G|M2003J6A1G",NokiaLumia:"Lumia [0-9]{3,4}",Micromax:"Micromax.*\\b(A210|A92|A88|A72|A111|A110Q|A115|A116|A110|A90S|A26|A51|A35|A54|A25|A27|A89|A68|A65|A57|A90)\\b",Palm:"PalmSource|Palm",Vertu:"Vertu|Vertu.*Ltd|Vertu.*Ascent|Vertu.*Ayxta|Vertu.*Constellation(F|Quest)?|Vertu.*Monika|Vertu.*Signature",Pantech:"PANTECH|IM-A850S|IM-A840S|IM-A830L|IM-A830K|IM-A830S|IM-A820L|IM-A810K|IM-A810S|IM-A800S|IM-T100K|IM-A725L|IM-A780L|IM-A775C|IM-A770K|IM-A760S|IM-A750K|IM-A740S|IM-A730S|IM-A720L|IM-A710K|IM-A690L|IM-A690S|IM-A650S|IM-A630K|IM-A600S|VEGA PTL21|PT003|P8010|ADR910L|P6030|P6020|P9070|P4100|P9060|P5000|CDM8992|TXT8045|ADR8995|IS11PT|P2030|P6010|P8000|PT002|IS06|CDM8999|P9050|PT001|TXT8040|P2020|P9020|P2000|P7040|P7000|C790",Fly:"IQ230|IQ444|IQ450|IQ440|IQ442|IQ441|IQ245|IQ256|IQ236|IQ255|IQ235|IQ245|IQ275|IQ240|IQ285|IQ280|IQ270|IQ260|IQ250",Wiko:"KITE 4G|HIGHWAY|GETAWAY|STAIRWAY|DARKSIDE|DARKFULL|DARKNIGHT|DARKMOON|SLIDE|WAX 4G|RAINBOW|BLOOM|SUNSET|GOA(?!nna)|LENNY|BARRY|IGGY|OZZY|CINK FIVE|CINK PEAX|CINK PEAX 2|CINK SLIM|CINK SLIM 2|CINK +|CINK KING|CINK PEAX|CINK SLIM|SUBLIM",iMobile:"i-mobile (IQ|i-STYLE|idea|ZAA|Hitz)",SimValley:"\\b(SP-80|XT-930|SX-340|XT-930|SX-310|SP-360|SP60|SPT-800|SP-120|SPT-800|SP-140|SPX-5|SPX-8|SP-100|SPX-8|SPX-12)\\b",Wolfgang:"AT-B24D|AT-AS50HD|AT-AS40W|AT-AS55HD|AT-AS45q2|AT-B26D|AT-AS50Q",Alcatel:"Alcatel",Nintendo:"Nintendo (3DS|Switch)",Amoi:"Amoi",INQ:"INQ",OnePlus:"ONEPLUS",GenericPhone:"Tapatalk|PDA;|SAGEM|\\bmmp\\b|pocket|\\bpsp\\b|symbian|Smartphone|smartfon|treo|up.browser|up.link|vodafone|\\bwap\\b|nokia|Series40|Series60|S60|SonyEricsson|N900|MAUI.*WAP.*Browser"},tablets:{iPad:"iPad|iPad.*Mobile",NexusTablet:"Android.*Nexus[\\s]+(7|9|10)",GoogleTablet:"Android.*Pixel C",SamsungTablet:"SAMSUNG.*Tablet|Galaxy.*Tab|SC-01C|GT-P1000|GT-P1003|GT-P1010|GT-P3105|GT-P6210|GT-P6800|GT-P6810|GT-P7100|GT-P7300|GT-P7310|GT-P7500|GT-P7510|SCH-I800|SCH-I815|SCH-I905|SGH-I957|SGH-I987|SGH-T849|SGH-T859|SGH-T869|SPH-P100|GT-P3100|GT-P3108|GT-P3110|GT-P5100|GT-P5110|GT-P6200|GT-P7320|GT-P7511|GT-N8000|GT-P8510|SGH-I497|SPH-P500|SGH-T779|SCH-I705|SCH-I915|GT-N8013|GT-P3113|GT-P5113|GT-P8110|GT-N8010|GT-N8005|GT-N8020|GT-P1013|GT-P6201|GT-P7501|GT-N5100|GT-N5105|GT-N5110|SHV-E140K|SHV-E140L|SHV-E140S|SHV-E150S|SHV-E230K|SHV-E230L|SHV-E230S|SHW-M180K|SHW-M180L|SHW-M180S|SHW-M180W|SHW-M300W|SHW-M305W|SHW-M380K|SHW-M380S|SHW-M380W|SHW-M430W|SHW-M480K|SHW-M480S|SHW-M480W|SHW-M485W|SHW-M486W|SHW-M500W|GT-I9228|SCH-P739|SCH-I925|GT-I9200|GT-P5200|GT-P5210|GT-P5210X|SM-T311|SM-T310|SM-T310X|SM-T210|SM-T210R|SM-T211|SM-P600|SM-P601|SM-P605|SM-P900|SM-P901|SM-T217|SM-T217A|SM-T217S|SM-P6000|SM-T3100|SGH-I467|XE500|SM-T110|GT-P5220|GT-I9200X|GT-N5110X|GT-N5120|SM-P905|SM-T111|SM-T2105|SM-T315|SM-T320|SM-T320X|SM-T321|SM-T520|SM-T525|SM-T530NU|SM-T230NU|SM-T330NU|SM-T900|XE500T1C|SM-P605V|SM-P905V|SM-T337V|SM-T537V|SM-T707V|SM-T807V|SM-P600X|SM-P900X|SM-T210X|SM-T230|SM-T230X|SM-T325|GT-P7503|SM-T531|SM-T330|SM-T530|SM-T705|SM-T705C|SM-T535|SM-T331|SM-T800|SM-T700|SM-T537|SM-T807|SM-P907A|SM-T337A|SM-T537A|SM-T707A|SM-T807A|SM-T237|SM-T807P|SM-P607T|SM-T217T|SM-T337T|SM-T807T|SM-T116NQ|SM-T116BU|SM-P550|SM-T350|SM-T550|SM-T9000|SM-P9000|SM-T705Y|SM-T805|GT-P3113|SM-T710|SM-T810|SM-T815|SM-T360|SM-T533|SM-T113|SM-T335|SM-T715|SM-T560|SM-T670|SM-T677|SM-T377|SM-T567|SM-T357T|SM-T555|SM-T561|SM-T713|SM-T719|SM-T813|SM-T819|SM-T580|SM-T355Y?|SM-T280|SM-T817A|SM-T820|SM-W700|SM-P580|SM-T587|SM-P350|SM-P555M|SM-P355M|SM-T113NU|SM-T815Y|SM-T585|SM-T285|SM-T825|SM-W708|SM-T835|SM-T830|SM-T837V|SM-T720|SM-T510|SM-T387V|SM-P610|SM-T290|SM-T515|SM-T590|SM-T595|SM-T725|SM-T817P|SM-P585N0|SM-T395|SM-T295|SM-T865|SM-P610N|SM-P615|SM-T970|SM-T380|SM-T5950|SM-T905|SM-T231|SM-T500|SM-T860",Kindle:"Kindle|Silk.*Accelerated|Android.*\\b(KFOT|KFTT|KFJWI|KFJWA|KFOTE|KFSOWI|KFTHWI|KFTHWA|KFAPWI|KFAPWA|WFJWAE|KFSAWA|KFSAWI|KFASWI|KFARWI|KFFOWI|KFGIWI|KFMEWI)\\b|Android.*Silk/[0-9.]+ like Chrome/[0-9.]+ (?!Mobile)",SurfaceTablet:"Windows NT [0-9.]+; ARM;.*(Tablet|ARMBJS)",HPTablet:"HP Slate (7|8|10)|HP ElitePad 900|hp-tablet|EliteBook.*Touch|HP 8|Slate 21|HP SlateBook 10",AsusTablet:"^.*PadFone((?!Mobile).)*$|Transformer|TF101|TF101G|TF300T|TF300TG|TF300TL|TF700T|TF700KL|TF701T|TF810C|ME171|ME301T|ME302C|ME371MG|ME370T|ME372MG|ME172V|ME173X|ME400C|Slider SL101|\\bK00F\\b|\\bK00C\\b|\\bK00E\\b|\\bK00L\\b|TX201LA|ME176C|ME102A|\\bM80TA\\b|ME372CL|ME560CG|ME372CG|ME302KL| K010 | K011 | K017 | K01E |ME572C|ME103K|ME170C|ME171C|\\bME70C\\b|ME581C|ME581CL|ME8510C|ME181C|P01Y|PO1MA|P01Z|\\bP027\\b|\\bP024\\b|\\bP00C\\b",BlackBerryTablet:"PlayBook|RIM Tablet",HTCtablet:"HTC_Flyer_P512|HTC Flyer|HTC Jetstream|HTC-P715a|HTC EVO View 4G|PG41200|PG09410",MotorolaTablet:"xoom|sholest|MZ615|MZ605|MZ505|MZ601|MZ602|MZ603|MZ604|MZ606|MZ607|MZ608|MZ609|MZ615|MZ616|MZ617",NookTablet:"Android.*Nook|NookColor|nook browser|BNRV200|BNRV200A|BNTV250|BNTV250A|BNTV400|BNTV600|LogicPD Zoom2",AcerTablet:"Android.*; \\b(A100|A101|A110|A200|A210|A211|A500|A501|A510|A511|A700|A701|W500|W500P|W501|W501P|W510|W511|W700|G100|G100W|B1-A71|B1-710|B1-711|A1-810|A1-811|A1-830)\\b|W3-810|\\bA3-A10\\b|\\bA3-A11\\b|\\bA3-A20\\b|\\bA3-A30|A3-A40",ToshibaTablet:"Android.*(AT100|AT105|AT200|AT205|AT270|AT275|AT300|AT305|AT1S5|AT500|AT570|AT700|AT830)|TOSHIBA.*FOLIO",LGTablet:"\\bL-06C|LG-V909|LG-V900|LG-V700|LG-V510|LG-V500|LG-V410|LG-V400|LG-VK810\\b",FujitsuTablet:"Android.*\\b(F-01D|F-02F|F-05E|F-10D|M532|Q572)\\b",PrestigioTablet:"PMP3170B|PMP3270B|PMP3470B|PMP7170B|PMP3370B|PMP3570C|PMP5870C|PMP3670B|PMP5570C|PMP5770D|PMP3970B|PMP3870C|PMP5580C|PMP5880D|PMP5780D|PMP5588C|PMP7280C|PMP7280C3G|PMP7280|PMP7880D|PMP5597D|PMP5597|PMP7100D|PER3464|PER3274|PER3574|PER3884|PER5274|PER5474|PMP5097CPRO|PMP5097|PMP7380D|PMP5297C|PMP5297C_QUAD|PMP812E|PMP812E3G|PMP812F|PMP810E|PMP880TD|PMT3017|PMT3037|PMT3047|PMT3057|PMT7008|PMT5887|PMT5001|PMT5002",LenovoTablet:"Lenovo TAB|Idea(Tab|Pad)( A1|A10| K1|)|ThinkPad([ ]+)?Tablet|YT3-850M|YT3-X90L|YT3-X90F|YT3-X90X|Lenovo.*(S2109|S2110|S5000|S6000|K3011|A3000|A3500|A1000|A2107|A2109|A1107|A5500|A7600|B6000|B8000|B8080)(-|)(FL|F|HV|H|)|TB-X103F|TB-X304X|TB-X304F|TB-X304L|TB-X505F|TB-X505L|TB-X505X|TB-X605F|TB-X605L|TB-8703F|TB-8703X|TB-8703N|TB-8704N|TB-8704F|TB-8704X|TB-8704V|TB-7304F|TB-7304I|TB-7304X|Tab2A7-10F|Tab2A7-20F|TB2-X30L|YT3-X50L|YT3-X50F|YT3-X50M|YT-X705F|YT-X703F|YT-X703L|YT-X705L|YT-X705X|TB2-X30F|TB2-X30L|TB2-X30M|A2107A-F|A2107A-H|TB3-730F|TB3-730M|TB3-730X|TB-7504F|TB-7504X|TB-X704F|TB-X104F|TB3-X70F|TB-X705F|TB-8504F|TB3-X70L|TB3-710F|TB-X704L",DellTablet:"Venue 11|Venue 8|Venue 7|Dell Streak 10|Dell Streak 7",YarvikTablet:"Android.*\\b(TAB210|TAB211|TAB224|TAB250|TAB260|TAB264|TAB310|TAB360|TAB364|TAB410|TAB411|TAB420|TAB424|TAB450|TAB460|TAB461|TAB464|TAB465|TAB467|TAB468|TAB07-100|TAB07-101|TAB07-150|TAB07-151|TAB07-152|TAB07-200|TAB07-201-3G|TAB07-210|TAB07-211|TAB07-212|TAB07-214|TAB07-220|TAB07-400|TAB07-485|TAB08-150|TAB08-200|TAB08-201-3G|TAB08-201-30|TAB09-100|TAB09-211|TAB09-410|TAB10-150|TAB10-201|TAB10-211|TAB10-400|TAB10-410|TAB13-201|TAB274EUK|TAB275EUK|TAB374EUK|TAB462EUK|TAB474EUK|TAB9-200)\\b",MedionTablet:"Android.*\\bOYO\\b|LIFE.*(P9212|P9514|P9516|S9512)|LIFETAB",ArnovaTablet:"97G4|AN10G2|AN7bG3|AN7fG3|AN8G3|AN8cG3|AN7G3|AN9G3|AN7dG3|AN7dG3ST|AN7dG3ChildPad|AN10bG3|AN10bG3DT|AN9G2",IntensoTablet:"INM8002KP|INM1010FP|INM805ND|Intenso Tab|TAB1004",IRUTablet:"M702pro",MegafonTablet:"MegaFon V9|\\bZTE V9\\b|Android.*\\bMT7A\\b",EbodaTablet:"E-Boda (Supreme|Impresspeed|Izzycomm|Essential)",AllViewTablet:"Allview.*(Viva|Alldro|City|Speed|All TV|Frenzy|Quasar|Shine|TX1|AX1|AX2)",ArchosTablet:"\\b(101G9|80G9|A101IT)\\b|Qilive 97R|Archos5|\\bARCHOS (70|79|80|90|97|101|FAMILYPAD|)(b|c|)(G10| Cobalt| TITANIUM(HD|)| Xenon| Neon|XSK| 2| XS 2| PLATINUM| CARBON|GAMEPAD)\\b",AinolTablet:"NOVO7|NOVO8|NOVO10|Novo7Aurora|Novo7Basic|NOVO7PALADIN|novo9-Spark",NokiaLumiaTablet:"Lumia 2520",SonyTablet:"Sony.*Tablet|Xperia Tablet|Sony Tablet S|SO-03E|SGPT12|SGPT13|SGPT114|SGPT121|SGPT122|SGPT123|SGPT111|SGPT112|SGPT113|SGPT131|SGPT132|SGPT133|SGPT211|SGPT212|SGPT213|SGP311|SGP312|SGP321|EBRD1101|EBRD1102|EBRD1201|SGP351|SGP341|SGP511|SGP512|SGP521|SGP541|SGP551|SGP621|SGP641|SGP612|SOT31|SGP771|SGP611|SGP612|SGP712",PhilipsTablet:"\\b(PI2010|PI3000|PI3100|PI3105|PI3110|PI3205|PI3210|PI3900|PI4010|PI7000|PI7100)\\b",CubeTablet:"Android.*(K8GT|U9GT|U10GT|U16GT|U17GT|U18GT|U19GT|U20GT|U23GT|U30GT)|CUBE U8GT",CobyTablet:"MID1042|MID1045|MID1125|MID1126|MID7012|MID7014|MID7015|MID7034|MID7035|MID7036|MID7042|MID7048|MID7127|MID8042|MID8048|MID8127|MID9042|MID9740|MID9742|MID7022|MID7010",MIDTablet:"M9701|M9000|M9100|M806|M1052|M806|T703|MID701|MID713|MID710|MID727|MID760|MID830|MID728|MID933|MID125|MID810|MID732|MID120|MID930|MID800|MID731|MID900|MID100|MID820|MID735|MID980|MID130|MID833|MID737|MID960|MID135|MID860|MID736|MID140|MID930|MID835|MID733|MID4X10",MSITablet:"MSI \\b(Primo 73K|Primo 73L|Primo 81L|Primo 77|Primo 93|Primo 75|Primo 76|Primo 73|Primo 81|Primo 91|Primo 90|Enjoy 71|Enjoy 7|Enjoy 10)\\b",SMiTTablet:"Android.*(\\bMID\\b|MID-560|MTV-T1200|MTV-PND531|MTV-P1101|MTV-PND530)",RockChipTablet:"Android.*(RK2818|RK2808A|RK2918|RK3066)|RK2738|RK2808A",FlyTablet:"IQ310|Fly Vision",bqTablet:"Android.*(bq)?.*\\b(Elcano|Curie|Edison|Maxwell|Kepler|Pascal|Tesla|Hypatia|Platon|Newton|Livingstone|Cervantes|Avant|Aquaris ([E|M]10|M8))\\b|Maxwell.*Lite|Maxwell.*Plus",HuaweiTablet:"MediaPad|MediaPad 7 Youth|IDEOS S7|S7-201c|S7-202u|S7-101|S7-103|S7-104|S7-105|S7-106|S7-201|S7-Slim|M2-A01L|BAH-L09|BAH-W09|AGS-L09|CMR-AL19",NecTablet:"\\bN-06D|\\bN-08D",PantechTablet:"Pantech.*P4100",BronchoTablet:"Broncho.*(N701|N708|N802|a710)",VersusTablet:"TOUCHPAD.*[78910]|\\bTOUCHTAB\\b",ZyncTablet:"z1000|Z99 2G|z930|z990|z909|Z919|z900",PositivoTablet:"TB07STA|TB10STA|TB07FTA|TB10FTA",NabiTablet:"Android.*\\bNabi",KoboTablet:"Kobo Touch|\\bK080\\b|\\bVox\\b Build|\\bArc\\b Build",DanewTablet:"DSlide.*\\b(700|701R|702|703R|704|802|970|971|972|973|974|1010|1012)\\b",TexetTablet:"NaviPad|TB-772A|TM-7045|TM-7055|TM-9750|TM-7016|TM-7024|TM-7026|TM-7041|TM-7043|TM-7047|TM-8041|TM-9741|TM-9747|TM-9748|TM-9751|TM-7022|TM-7021|TM-7020|TM-7011|TM-7010|TM-7023|TM-7025|TM-7037W|TM-7038W|TM-7027W|TM-9720|TM-9725|TM-9737W|TM-1020|TM-9738W|TM-9740|TM-9743W|TB-807A|TB-771A|TB-727A|TB-725A|TB-719A|TB-823A|TB-805A|TB-723A|TB-715A|TB-707A|TB-705A|TB-709A|TB-711A|TB-890HD|TB-880HD|TB-790HD|TB-780HD|TB-770HD|TB-721HD|TB-710HD|TB-434HD|TB-860HD|TB-840HD|TB-760HD|TB-750HD|TB-740HD|TB-730HD|TB-722HD|TB-720HD|TB-700HD|TB-500HD|TB-470HD|TB-431HD|TB-430HD|TB-506|TB-504|TB-446|TB-436|TB-416|TB-146SE|TB-126SE",PlaystationTablet:"Playstation.*(Portable|Vita)",TrekstorTablet:"ST10416-1|VT10416-1|ST70408-1|ST702xx-1|ST702xx-2|ST80208|ST97216|ST70104-2|VT10416-2|ST10216-2A|SurfTab",PyleAudioTablet:"\\b(PTBL10CEU|PTBL10C|PTBL72BC|PTBL72BCEU|PTBL7CEU|PTBL7C|PTBL92BC|PTBL92BCEU|PTBL9CEU|PTBL9CUK|PTBL9C)\\b",AdvanTablet:"Android.* \\b(E3A|T3X|T5C|T5B|T3E|T3C|T3B|T1J|T1F|T2A|T1H|T1i|E1C|T1-E|T5-A|T4|E1-B|T2Ci|T1-B|T1-D|O1-A|E1-A|T1-A|T3A|T4i)\\b ",DanyTechTablet:"Genius Tab G3|Genius Tab S2|Genius Tab Q3|Genius Tab G4|Genius Tab Q4|Genius Tab G-II|Genius TAB GII|Genius TAB GIII|Genius Tab S1",GalapadTablet:"Android [0-9.]+; [a-z-]+; \\bG1\\b",MicromaxTablet:"Funbook|Micromax.*\\b(P250|P560|P360|P362|P600|P300|P350|P500|P275)\\b",KarbonnTablet:"Android.*\\b(A39|A37|A34|ST8|ST10|ST7|Smart Tab3|Smart Tab2)\\b",AllFineTablet:"Fine7 Genius|Fine7 Shine|Fine7 Air|Fine8 Style|Fine9 More|Fine10 Joy|Fine11 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1027",HCLTablet:"HCL.*Tablet|Connect-3G-2.0|Connect-2G-2.0|ME Tablet U1|ME Tablet U2|ME Tablet G1|ME Tablet X1|ME Tablet Y2|ME Tablet Sync",DPSTablet:"DPS Dream 9|DPS Dual 7",VistureTablet:"V97 HD|i75 3G|Visture V4( HD)?|Visture V5( HD)?|Visture V10",CrestaTablet:"CTP(-)?810|CTP(-)?818|CTP(-)?828|CTP(-)?838|CTP(-)?888|CTP(-)?978|CTP(-)?980|CTP(-)?987|CTP(-)?988|CTP(-)?989",MediatekTablet:"\\bMT8125|MT8389|MT8135|MT8377\\b",ConcordeTablet:"Concorde([ ]+)?Tab|ConCorde ReadMan",GoCleverTablet:"GOCLEVER TAB|A7GOCLEVER|M1042|M7841|M742|R1042BK|R1041|TAB A975|TAB A7842|TAB A741|TAB A741L|TAB M723G|TAB M721|TAB A1021|TAB I921|TAB R721|TAB I720|TAB T76|TAB R70|TAB R76.2|TAB R106|TAB R83.2|TAB M813G|TAB I721|GCTA722|TAB I70|TAB I71|TAB S73|TAB R73|TAB R74|TAB R93|TAB R75|TAB R76.1|TAB A73|TAB A93|TAB A93.2|TAB T72|TAB R83|TAB R974|TAB R973|TAB A101|TAB A103|TAB A104|TAB A104.2|R105BK|M713G|A972BK|TAB A971|TAB R974.2|TAB R104|TAB R83.3|TAB A1042",ModecomTablet:"FreeTAB 9000|FreeTAB 7.4|FreeTAB 7004|FreeTAB 7800|FreeTAB 2096|FreeTAB 7.5|FreeTAB 1014|FreeTAB 1001 |FreeTAB 8001|FreeTAB 9706|FreeTAB 9702|FreeTAB 7003|FreeTAB 7002|FreeTAB 1002|FreeTAB 7801|FreeTAB 1331|FreeTAB 1004|FreeTAB 8002|FreeTAB 8014|FreeTAB 9704|FreeTAB 1003",VoninoTablet:"\\b(Argus[ _]?S|Diamond[ _]?79HD|Emerald[ _]?78E|Luna[ _]?70C|Onyx[ _]?S|Onyx[ _]?Z|Orin[ _]?HD|Orin[ _]?S|Otis[ _]?S|SpeedStar[ _]?S|Magnet[ _]?M9|Primus[ _]?94[ _]?3G|Primus[ _]?94HD|Primus[ _]?QS|Android.*\\bQ8\\b|Sirius[ _]?EVO[ _]?QS|Sirius[ _]?QS|Spirit[ _]?S)\\b",ECSTablet:"V07OT2|TM105A|S10OT1|TR10CS1",StorexTablet:"eZee[_']?(Tab|Go)[0-9]+|TabLC7|Looney Tunes Tab",VodafoneTablet:"SmartTab([ ]+)?[0-9]+|SmartTabII10|SmartTabII7|VF-1497|VFD 1400",EssentielBTablet:"Smart[ ']?TAB[ ]+?[0-9]+|Family[ ']?TAB2",RossMoorTablet:"RM-790|RM-997|RMD-878G|RMD-974R|RMT-705A|RMT-701|RME-601|RMT-501|RMT-711",iMobileTablet:"i-mobile i-note",TolinoTablet:"tolino tab [0-9.]+|tolino shine",AudioSonicTablet:"\\bC-22Q|T7-QC|T-17B|T-17P\\b",AMPETablet:"Android.* A78 ",SkkTablet:"Android.* (SKYPAD|PHOENIX|CYCLOPS)",TecnoTablet:"TECNO P9|TECNO DP8D",JXDTablet:"Android.* \\b(F3000|A3300|JXD5000|JXD3000|JXD2000|JXD300B|JXD300|S5800|S7800|S602b|S5110b|S7300|S5300|S602|S603|S5100|S5110|S601|S7100a|P3000F|P3000s|P101|P200s|P1000m|P200m|P9100|P1000s|S6600b|S908|P1000|P300|S18|S6600|S9100)\\b",iJoyTablet:"Tablet (Spirit 7|Essentia|Galatea|Fusion|Onix 7|Landa|Titan|Scooby|Deox|Stella|Themis|Argon|Unique 7|Sygnus|Hexen|Finity 7|Cream|Cream X2|Jade|Neon 7|Neron 7|Kandy|Scape|Saphyr 7|Rebel|Biox|Rebel|Rebel 8GB|Myst|Draco 7|Myst|Tab7-004|Myst|Tadeo Jones|Tablet Boing|Arrow|Draco Dual Cam|Aurix|Mint|Amity|Revolution|Finity 9|Neon 9|T9w|Amity 4GB Dual Cam|Stone 4GB|Stone 8GB|Andromeda|Silken|X2|Andromeda II|Halley|Flame|Saphyr 9,7|Touch 8|Planet|Triton|Unique 10|Hexen 10|Memphis 4GB|Memphis 8GB|Onix 10)",FX2Tablet:"FX2 PAD7|FX2 PAD10",XoroTablet:"KidsPAD 701|PAD[ ]?712|PAD[ ]?714|PAD[ ]?716|PAD[ ]?717|PAD[ ]?718|PAD[ ]?720|PAD[ ]?721|PAD[ ]?722|PAD[ ]?790|PAD[ ]?792|PAD[ ]?900|PAD[ ]?9715D|PAD[ ]?9716DR|PAD[ ]?9718DR|PAD[ ]?9719QR|PAD[ ]?9720QR|TelePAD1030|Telepad1032|TelePAD730|TelePAD731|TelePAD732|TelePAD735Q|TelePAD830|TelePAD9730|TelePAD795|MegaPAD 1331|MegaPAD 1851|MegaPAD 2151",ViewsonicTablet:"ViewPad 10pi|ViewPad 10e|ViewPad 10s|ViewPad E72|ViewPad7|ViewPad E100|ViewPad 7e|ViewSonic VB733|VB100a",VerizonTablet:"QTAQZ3|QTAIR7|QTAQTZ3|QTASUN1|QTASUN2|QTAXIA1",OdysTablet:"LOOX|XENO10|ODYS[ -](Space|EVO|Xpress|NOON)|\\bXELIO\\b|Xelio10Pro|XELIO7PHONETAB|XELIO10EXTREME|XELIOPT2|NEO_QUAD10",CaptivaTablet:"CAPTIVA PAD",IconbitTablet:"NetTAB|NT-3702|NT-3702S|NT-3702S|NT-3603P|NT-3603P|NT-0704S|NT-0704S|NT-3805C|NT-3805C|NT-0806C|NT-0806C|NT-0909T|NT-0909T|NT-0907S|NT-0907S|NT-0902S|NT-0902S",TeclastTablet:"T98 4G|\\bP80\\b|\\bX90HD\\b|X98 Air|X98 Air 3G|\\bX89\\b|P80 3G|\\bX80h\\b|P98 Air|\\bX89HD\\b|P98 3G|\\bP90HD\\b|P89 3G|X98 3G|\\bP70h\\b|P79HD 3G|G18d 3G|\\bP79HD\\b|\\bP89s\\b|\\bA88\\b|\\bP10HD\\b|\\bP19HD\\b|G18 3G|\\bP78HD\\b|\\bA78\\b|\\bP75\\b|G17s 3G|G17h 3G|\\bP85t\\b|\\bP90\\b|\\bP11\\b|\\bP98t\\b|\\bP98HD\\b|\\bG18d\\b|\\bP85s\\b|\\bP11HD\\b|\\bP88s\\b|\\bA80HD\\b|\\bA80se\\b|\\bA10h\\b|\\bP89\\b|\\bP78s\\b|\\bG18\\b|\\bP85\\b|\\bA70h\\b|\\bA70\\b|\\bG17\\b|\\bP18\\b|\\bA80s\\b|\\bA11s\\b|\\bP88HD\\b|\\bA80h\\b|\\bP76s\\b|\\bP76h\\b|\\bP98\\b|\\bA10HD\\b|\\bP78\\b|\\bP88\\b|\\bA11\\b|\\bA10t\\b|\\bP76a\\b|\\bP76t\\b|\\bP76e\\b|\\bP85HD\\b|\\bP85a\\b|\\bP86\\b|\\bP75HD\\b|\\bP76v\\b|\\bA12\\b|\\bP75a\\b|\\bA15\\b|\\bP76Ti\\b|\\bP81HD\\b|\\bA10\\b|\\bT760VE\\b|\\bT720HD\\b|\\bP76\\b|\\bP73\\b|\\bP71\\b|\\bP72\\b|\\bT720SE\\b|\\bC520Ti\\b|\\bT760\\b|\\bT720VE\\b|T720-3GE|T720-WiFi",OndaTablet:"\\b(V975i|Vi30|VX530|V701|Vi60|V701s|Vi50|V801s|V719|Vx610w|VX610W|V819i|Vi10|VX580W|Vi10|V711s|V813|V811|V820w|V820|Vi20|V711|VI30W|V712|V891w|V972|V819w|V820w|Vi60|V820w|V711|V813s|V801|V819|V975s|V801|V819|V819|V818|V811|V712|V975m|V101w|V961w|V812|V818|V971|V971s|V919|V989|V116w|V102w|V973|Vi40)\\b[\\s]+|V10 \\b4G\\b",JaytechTablet:"TPC-PA762",BlaupunktTablet:"Endeavour 800NG|Endeavour 1010",DigmaTablet:"\\b(iDx10|iDx9|iDx8|iDx7|iDxD7|iDxD8|iDsQ8|iDsQ7|iDsQ8|iDsD10|iDnD7|3TS804H|iDsQ11|iDj7|iDs10)\\b",EvolioTablet:"ARIA_Mini_wifi|Aria[ _]Mini|Evolio X10|Evolio X7|Evolio X8|\\bEvotab\\b|\\bNeura\\b",LavaTablet:"QPAD E704|\\bIvoryS\\b|E-TAB IVORY|\\bE-TAB\\b",AocTablet:"MW0811|MW0812|MW0922|MTK8382|MW1031|MW0831|MW0821|MW0931|MW0712",MpmanTablet:"MP11 OCTA|MP10 OCTA|MPQC1114|MPQC1004|MPQC994|MPQC974|MPQC973|MPQC804|MPQC784|MPQC780|\\bMPG7\\b|MPDCG75|MPDCG71|MPDC1006|MP101DC|MPDC9000|MPDC905|MPDC706HD|MPDC706|MPDC705|MPDC110|MPDC100|MPDC99|MPDC97|MPDC88|MPDC8|MPDC77|MP709|MID701|MID711|MID170|MPDC703|MPQC1010",CelkonTablet:"CT695|CT888|CT[\\s]?910|CT7 Tab|CT9 Tab|CT3 Tab|CT2 Tab|CT1 Tab|C820|C720|\\bCT-1\\b",WolderTablet:"miTab \\b(DIAMOND|SPACE|BROOKLYN|NEO|FLY|MANHATTAN|FUNK|EVOLUTION|SKY|GOCAR|IRON|GENIUS|POP|MINT|EPSILON|BROADWAY|JUMP|HOP|LEGEND|NEW AGE|LINE|ADVANCE|FEEL|FOLLOW|LIKE|LINK|LIVE|THINK|FREEDOM|CHICAGO|CLEVELAND|BALTIMORE-GH|IOWA|BOSTON|SEATTLE|PHOENIX|DALLAS|IN 101|MasterChef)\\b",MediacomTablet:"M-MPI10C3G|M-SP10EG|M-SP10EGP|M-SP10HXAH|M-SP7HXAH|M-SP10HXBH|M-SP8HXAH|M-SP8MXA",MiTablet:"\\bMI PAD\\b|\\bHM NOTE 1W\\b",NibiruTablet:"Nibiru M1|Nibiru Jupiter One",NexoTablet:"NEXO NOVA|NEXO 10|NEXO AVIO|NEXO FREE|NEXO GO|NEXO EVO|NEXO 3G|NEXO SMART|NEXO KIDDO|NEXO MOBI",LeaderTablet:"TBLT10Q|TBLT10I|TBL-10WDKB|TBL-10WDKBO2013|TBL-W230V2|TBL-W450|TBL-W500|SV572|TBLT7I|TBA-AC7-8G|TBLT79|TBL-8W16|TBL-10W32|TBL-10WKB|TBL-W100",UbislateTablet:"UbiSlate[\\s]?7C",PocketBookTablet:"Pocketbook",KocasoTablet:"\\b(TB-1207)\\b",HisenseTablet:"\\b(F5281|E2371)\\b",Hudl:"Hudl HT7S3|Hudl 2",TelstraTablet:"T-Hub2",GenericTablet:"Android.*\\b97D\\b|Tablet(?!.*PC)|BNTV250A|MID-WCDMA|LogicPD Zoom2|\\bA7EB\\b|CatNova8|A1_07|CT704|CT1002|\\bM721\\b|rk30sdk|\\bEVOTAB\\b|M758A|ET904|ALUMIUM10|Smartfren Tab|Endeavour 1010|Tablet-PC-4|Tagi Tab|\\bM6pro\\b|CT1020W|arc 10HD|\\bTP750\\b|\\bQTAQZ3\\b|WVT101|TM1088|KT107"},oss:{AndroidOS:"Android",BlackBerryOS:"blackberry|\\bBB10\\b|rim tablet os",PalmOS:"PalmOS|avantgo|blazer|elaine|hiptop|palm|plucker|xiino",SymbianOS:"Symbian|SymbOS|Series60|Series40|SYB-[0-9]+|\\bS60\\b",WindowsMobileOS:"Windows CE.*(PPC|Smartphone|Mobile|[0-9]{3}x[0-9]{3})|Windows Mobile|Windows Phone [0-9.]+|WCE;",WindowsPhoneOS:"Windows Phone 10.0|Windows Phone 8.1|Windows Phone 8.0|Windows Phone OS|XBLWP7|ZuneWP7|Windows NT 6.[23]; ARM;",iOS:"\\biPhone.*Mobile|\\biPod|\\biPad|AppleCoreMedia",iPadOS:"CPU OS 13",SailfishOS:"Sailfish",MeeGoOS:"MeeGo",MaemoOS:"Maemo",JavaOS:"J2ME/|\\bMIDP\\b|\\bCLDC\\b",webOS:"webOS|hpwOS",badaOS:"\\bBada\\b",BREWOS:"BREW"},uas:{Chrome:"\\bCrMo\\b|CriOS|Android.*Chrome/[.0-9]* (Mobile)?",Dolfin:"\\bDolfin\\b",Opera:"Opera.*Mini|Opera.*Mobi|Android.*Opera|Mobile.*OPR/[0-9.]+$|Coast/[0-9.]+",Skyfire:"Skyfire",Edge:"\\bEdgiOS\\b|Mobile Safari/[.0-9]* Edge",IE:"IEMobile|MSIEMobile",Firefox:"fennec|firefox.*maemo|(Mobile|Tablet).*Firefox|Firefox.*Mobile|FxiOS",Bolt:"bolt",TeaShark:"teashark",Blazer:"Blazer",Safari:"Version((?!\\bEdgiOS\\b).)*Mobile.*Safari|Safari.*Mobile|MobileSafari",WeChat:"\\bMicroMessenger\\b",UCBrowser:"UC.*Browser|UCWEB",baiduboxapp:"baiduboxapp",baidubrowser:"baidubrowser",DiigoBrowser:"DiigoBrowser",Mercury:"\\bMercury\\b",ObigoBrowser:"Obigo",NetFront:"NF-Browser",GenericBrowser:"NokiaBrowser|OviBrowser|OneBrowser|TwonkyBeamBrowser|SEMC.*Browser|FlyFlow|Minimo|NetFront|Novarra-Vision|MQQBrowser|MicroMessenger",PaleMoon:"Android.*PaleMoon|Mobile.*PaleMoon"},props:{Mobile:"Mobile/[VER]",Build:"Build/[VER]",Version:"Version/[VER]",VendorID:"VendorID/[VER]",iPad:"iPad.*CPU[a-z ]+[VER]",iPhone:"iPhone.*CPU[a-z ]+[VER]",iPod:"iPod.*CPU[a-z ]+[VER]",Kindle:"Kindle/[VER]",Chrome:["Chrome/[VER]","CriOS/[VER]","CrMo/[VER]"],Coast:["Coast/[VER]"],Dolfin:"Dolfin/[VER]",Firefox:["Firefox/[VER]","FxiOS/[VER]"],Fennec:"Fennec/[VER]",Edge:"Edge/[VER]",IE:["IEMobile/[VER];","IEMobile [VER]","MSIE [VER];","Trident/[0-9.]+;.*rv:[VER]"],NetFront:"NetFront/[VER]",NokiaBrowser:"NokiaBrowser/[VER]",Opera:[" OPR/[VER]","Opera Mini/[VER]","Version/[VER]"],"Opera Mini":"Opera Mini/[VER]","Opera Mobi":"Version/[VER]",UCBrowser:["UCWEB[VER]","UC.*Browser/[VER]"],MQQBrowser:"MQQBrowser/[VER]",MicroMessenger:"MicroMessenger/[VER]",baiduboxapp:"baiduboxapp/[VER]",baidubrowser:"baidubrowser/[VER]",SamsungBrowser:"SamsungBrowser/[VER]",Iron:"Iron/[VER]",Safari:["Version/[VER]","Safari/[VER]"],Skyfire:"Skyfire/[VER]",Tizen:"Tizen/[VER]",Webkit:"webkit[ /][VER]",PaleMoon:"PaleMoon/[VER]",SailfishBrowser:"SailfishBrowser/[VER]",Gecko:"Gecko/[VER]",Trident:"Trident/[VER]",Presto:"Presto/[VER]",Goanna:"Goanna/[VER]",iOS:" \\bi?OS\\b [VER][ ;]{1}",Android:"Android [VER]",Sailfish:"Sailfish [VER]",BlackBerry:["BlackBerry[\\w]+/[VER]","BlackBerry.*Version/[VER]","Version/[VER]"],BREW:"BREW [VER]",Java:"Java/[VER]","Windows Phone OS":["Windows Phone OS [VER]","Windows Phone [VER]"],"Windows Phone":"Windows Phone [VER]","Windows CE":"Windows CE/[VER]","Windows NT":"Windows NT [VER]",Symbian:["SymbianOS/[VER]","Symbian/[VER]"],webOS:["webOS/[VER]","hpwOS/[VER];"]},utils:{Bot:"Googlebot|facebookexternalhit|Google-AMPHTML|s~amp-validator|AdsBot-Google|Google Keyword Suggestion|Facebot|YandexBot|YandexMobileBot|bingbot|ia_archiver|AhrefsBot|Ezooms|GSLFbot|WBSearchBot|Twitterbot|TweetmemeBot|Twikle|PaperLiBot|Wotbox|UnwindFetchor|Exabot|MJ12bot|YandexImages|TurnitinBot|Pingdom|contentkingapp|AspiegelBot",MobileBot:"Googlebot-Mobile|AdsBot-Google-Mobile|YahooSeeker/M1A1-R2D2",DesktopMode:"WPDesktop",TV:"SonyDTV|HbbTV",WebKit:"(webkit)[ /]([\\w.]+)",Console:"\\b(Nintendo|Nintendo WiiU|Nintendo 3DS|Nintendo Switch|PLAYSTATION|Xbox)\\b",Watch:"SM-V700"}},g.detectMobileBrowsers={fullPattern:/(android|bb\d+|meego).+mobile|avantgo|bada\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\/|plucker|pocket|psp|series(4|6)0|symbian|treo|up\.(browser|link)|vodafone|wap|windows ce|xda|xiino/i, shortPattern:/1207|6310|6590|3gso|4thp|50[1-6]i|770s|802s|a wa|abac|ac(er|oo|s\-)|ai(ko|rn)|al(av|ca|co)|amoi|an(ex|ny|yw)|aptu|ar(ch|go)|as(te|us)|attw|au(di|\-m|r |s )|avan|be(ck|ll|nq)|bi(lb|rd)|bl(ac|az)|br(e|v)w|bumb|bw\-(n|u)|c55\/|capi|ccwa|cdm\-|cell|chtm|cldc|cmd\-|co(mp|nd)|craw|da(it|ll|ng)|dbte|dc\-s|devi|dica|dmob|do(c|p)o|ds(12|\-d)|el(49|ai)|em(l2|ul)|er(ic|k0)|esl8|ez([4-7]0|os|wa|ze)|fetc|fly(\-|_)|g1 u|g560|gene|gf\-5|g\-mo|go(\.w|od)|gr(ad|un)|haie|hcit|hd\-(m|p|t)|hei\-|hi(pt|ta)|hp( i|ip)|hs\-c|ht(c(\-| |_|a|g|p|s|t)|tp)|hu(aw|tc)|i\-(20|go|ma)|i230|iac( |\-|\/)|ibro|idea|ig01|ikom|im1k|inno|ipaq|iris|ja(t|v)a|jbro|jemu|jigs|kddi|keji|kgt( |\/)|klon|kpt |kwc\-|kyo(c|k)|le(no|xi)|lg( g|\/(k|l|u)|50|54|\-[a-w])|libw|lynx|m1\-w|m3ga|m50\/|ma(te|ui|xo)|mc(01|21|ca)|m\-cr|me(rc|ri)|mi(o8|oa|ts)|mmef|mo(01|02|bi|de|do|t(\-| |o|v)|zz)|mt(50|p1|v )|mwbp|mywa|n10[0-2]|n20[2-3]|n30(0|2)|n50(0|2|5)|n7(0(0|1)|10)|ne((c|m)\-|on|tf|wf|wg|wt)|nok(6|i)|nzph|o2im|op(ti|wv)|oran|owg1|p800|pan(a|d|t)|pdxg|pg(13|\-([1-8]|c))|phil|pire|pl(ay|uc)|pn\-2|po(ck|rt|se)|prox|psio|pt\-g|qa\-a|qc(07|12|21|32|60|\-[2-7]|i\-)|qtek|r380|r600|raks|rim9|ro(ve|zo)|s55\/|sa(ge|ma|mm|ms|ny|va)|sc(01|h\-|oo|p\-)|sdk\/|se(c(\-|0|1)|47|mc|nd|ri)|sgh\-|shar|sie(\-|m)|sk\-0|sl(45|id)|sm(al|ar|b3|it|t5)|so(ft|ny)|sp(01|h\-|v\-|v )|sy(01|mb)|t2(18|50)|t6(00|10|18)|ta(gt|lk)|tcl\-|tdg\-|tel(i|m)|tim\-|t\-mo|to(pl|sh)|ts(70|m\-|m3|m5)|tx\-9|up(\.b|g1|si)|utst|v400|v750|veri|vi(rg|te)|vk(40|5[0-3]|\-v)|vm40|voda|vulc|vx(52|53|60|61|70|80|81|83|85|98)|w3c(\-| )|webc|whit|wi(g |nc|nw)|wmlb|wonu|x700|yas\-|your|zeto|zte\-/i,tabletPattern:/android|ipad|playbook|silk/i};var h,i=Object.prototype.hasOwnProperty;return g.FALLBACK_PHONE="UnknownPhone",g.FALLBACK_TABLET="UnknownTablet",g.FALLBACK_MOBILE="UnknownMobile",h="isArray"in Array?Array.isArray:function(a){return"[object Array]"===Object.prototype.toString.call(a)},function(){var a,b,c,e,f,j,k=g.mobileDetectRules;for(a in k.props)if(i.call(k.props,a)){for(b=k.props[a],h(b)||(b=[b]),f=b.length,e=0;e<f;++e)c=b[e],j=c.indexOf("[VER]"),j>=0&&(c=c.substring(0,j)+"([\\w._\\+]+)"+c.substring(j+5)),b[e]=new RegExp(c,"i");k.props[a]=b}d(k.oss),d(k.phones),d(k.tablets),d(k.uas),d(k.utils),k.oss0={WindowsPhoneOS:k.oss.WindowsPhoneOS,WindowsMobileOS:k.oss.WindowsMobileOS}}(),g.findMatch=function(a,b){for(var c in a)if(i.call(a,c)&&a[c].test(b))return c;return null},g.findMatches=function(a,b){var c=[];for(var d in a)i.call(a,d)&&a[d].test(b)&&c.push(d);return c},g.getVersionStr=function(a,b){var c,d,e,f,h=g.mobileDetectRules.props;if(i.call(h,a))for(c=h[a],e=c.length,d=0;d<e;++d)if(f=c[d].exec(b),null!==f)return f[1];return null},g.getVersion=function(a,b){var c=g.getVersionStr(a,b);return c?g.prepareVersionNo(c):NaN},g.prepareVersionNo=function(a){var b;return b=a.split(/[a-z._ \/\-]/i),1===b.length&&(a=b[0]),b.length>1&&(a=b[0]+".",b.shift(),a+=b.join("")),Number(a)},g.isMobileFallback=function(a){return g.detectMobileBrowsers.fullPattern.test(a)||g.detectMobileBrowsers.shortPattern.test(a.substr(0,4))},g.isTabletFallback=function(a){return g.detectMobileBrowsers.tabletPattern.test(a)},g.prepareDetectionCache=function(a,c,d){if(a.mobile===b){var e,h,i;return(h=g.findMatch(g.mobileDetectRules.tablets,c))?(a.mobile=a.tablet=h,void(a.phone=null)):(e=g.findMatch(g.mobileDetectRules.phones,c))?(a.mobile=a.phone=e,void(a.tablet=null)):void(g.isMobileFallback(c)?(i=f.isPhoneSized(d),i===b?(a.mobile=g.FALLBACK_MOBILE,a.tablet=a.phone=null):i?(a.mobile=a.phone=g.FALLBACK_PHONE,a.tablet=null):(a.mobile=a.tablet=g.FALLBACK_TABLET,a.phone=null)):g.isTabletFallback(c)?(a.mobile=a.tablet=g.FALLBACK_TABLET,a.phone=null):a.mobile=a.tablet=a.phone=null)}},g.mobileGrade=function(a){var b=null!==a.mobile();return a.os("iOS")&&a.version("iPad")>=4.3||a.os("iOS")&&a.version("iPhone")>=3.1||a.os("iOS")&&a.version("iPod")>=3.1||a.version("Android")>2.1&&a.is("Webkit")||a.version("Windows Phone 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