CINXE.COM

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sessionStorage.removeItem('facets.search.filters'); </script> <div class="col-sm-12 d-none text-right sidebar-toggle-link-wrapper"> <a class="sidebar-toggle sidebar-toggle-link" href="#main-content-end"> Related Resources <i class="fa fa-angle-right" aria-hidden="true"></i> </a> </div> <div class="main-content col-lg-9 col-md-9 mt-0 mb-0"> <div class="post-card--single"> <div class="row"> <div class="col-lg-12 mb-0"> <div class="card--post-attributes"> <span class="category-name content-s"> <a href="https://developer.nvidia.com/blog/category/generative-ai/" data-wpel-link="internal" target="_blank" rel="follow noopener">Generative AI</a> </span> <span class="post-rate-widget content-s"></span> <span class="post-lang-switcher"> </span> </div> <h1 class="h--large txt-clr--blck mt-2 mb-0">Spotlight: TCS Increases Automotive Software Testing Speeds by 2x Using NVIDIA Generative AI</h1> <div class="post-info"> <div class="post-published-date"> Nov 22, 2024 </div> <div class="post-authors"> By <a href="https://developer.nvidia.com/blog/author/manojcr/" title="Posts by Manoj C R" class="author url fn" rel="author follow noopener" data-wpel-link="internal" target="_blank">Manoj C R</a>, <a href="https://developer.nvidia.com/blog/author/padukakannan/" title="Posts by Paduka Kannan" class="author url fn" rel="author follow noopener" data-wpel-link="internal" target="_blank">Paduka Kannan</a>, <a href="https://developer.nvidia.com/blog/author/arunkrishnathayyilravi/" title="Posts by Arunkrishna Thayyilravi" class="author url fn" rel="author follow noopener" data-wpel-link="internal" target="_blank">Arunkrishna Thayyilravi</a>, <a href="https://developer.nvidia.com/blog/author/sonirajat/" title="Posts by Rajat Soni" class="author url fn" rel="author follow noopener" data-wpel-link="internal" target="_blank">Rajat Soni</a>, <a href="https://developer.nvidia.com/blog/author/saumishra/" title="Posts by Saurav Mishra" class="author url fn" rel="author follow noopener" data-wpel-link="internal" target="_blank">Saurav Mishra</a> and <a href="https://developer.nvidia.com/blog/author/vpai/" title="Posts by Vishwas Pai" class="author url fn" rel="author follow noopener" data-wpel-link="internal" target="_blank">Vishwas Pai</a> </div> <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="92444" data-ulike-nonce="936b953c5e" 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_92444"><span><i class="far fa-thumbs-up"></i></span> </button><span class="count-box wp_ulike_counter_up" data-ulike-counter-value="+4"></span> </div></div> <span class="js-like-click like-click"> Like </span> </div> <div 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you&body=https%3A%2F%2Fdeveloper.nvidia.com%2Fblog%2Fspotlight-tcs-increases-automotive-software-testing-speeds-by-2x-using-nvidia-generative-ai%2F" class="for-mail">E</a></li> </ul> </div> <div class="entry-content"> <p>Generative AI is transforming every aspect of the automotive industry, including software development, testing, user experience, personalization, and safety. With the automotive industry shifting from a mechanically driven approach to a software-driven one, generative AI is unlocking a world of possibilities.</p> <p><a href="https://www.tcs.com/" data-wpel-link="external" target="_blank" rel="follow external noopener">Tata Consultancy Services (TCS)</a> focuses on two major segments for leveraging generative AI in automotive: </p> <ul class="wp-block-list"> <li>Building features to enhance customer experience</li> <li>Accelerating the software engineering lifecycle</li> </ul> <h3 id="building_features_to_enhance_customer_experience" class="wp-block-heading">Building features to enhance customer experience<a href="#building_features_to_enhance_customer_experience" class="heading-anchor-link"><i class="fas fa-link"></i></a></h3> <p>Generative AI is the key to realizing fully autonomous vehicles (AVs) by enhancing AI-based algorithms for better decision-making. It generates and synthesizes<strong> </strong>datasets across all possibilities, from limited real-time data to training and testing data. This technology is instrumental in delivering vehicle personalization and user experiences. This can encompass a range of capabilities, including advanced search functionalities, language translations, in-car personal assistants, and intuitive recommendations for video and audio entertainment.</p> <h3 id="accelerating_the_software_engineering_lifecycle" class="wp-block-heading">Accelerating the software engineering lifecycle<a href="#accelerating_the_software_engineering_lifecycle" class="heading-anchor-link"><i class="fas fa-link"></i></a></h3> <p>The goal of a software-defined vehicle (SDV) is to provide more flexibility and enrich the user experience, enabling customers to upgrade and update vehicle features based on their convenience. This has increased vehicle complexity, resulting in millions of lines of code. There is high demand for enabling feature-as-a-service models, where automotive features need to be developed and deployed within a few weeks. </p> <p>Current processes and tools make this timeline nearly impossible. Here, generative AI has the potential to act as a companion to engineers, accelerating the software engineering lifecycle, including requirement analysis, design, development, and validation.</p> <p>With these focus areas, TCS has built the Automotive Gen-AI Suite, leveraging TCS-patented algorithms developed with NVIDIA technologies. Figure 1 shows the architecture for text-based use cases in an off-board environment. <a href="https://www.nvidia.com/en-us/glossary/large-language-models/?srsltid=AfmBOorZFgWMSdjsgn1Wl0W3QJuDPoND_oOUGViw79w87wObx5DPaQte" data-wpel-link="internal" target="_blank" rel="follow noopener">Large language models (LLMs)</a> are trained with the<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> framework and fine-tuned using automotive domain-specific datasets using <a href="https://www.nvidia.com/en-us/ai/#referrer=ai-subdomain" data-wpel-link="internal" target="_blank" rel="follow noopener">NVIDIA NIM</a> microservices, part of the <a href="https://www.nvidia.com/en-us/data-center/products/ai-enterprise/" data-wpel-link="internal" target="_blank" rel="follow noopener">NVIDIA AI Enterprise</a> software platform.</p> <p>This post explores one use case, unit level test case generation, including the approach and the key performance indicators (KPIs) used to measure success.</p> <div class="wp-block-image"> <figure class="aligncenter size-full"><img decoding="async" width="921" height="393" src="https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/tcs-automotive-gen-ai-suite-architecture-1.png" alt="TCS Automotive Gen-AI solution architecture, including application services, APIs, framework, and training toolkit." class="wp-image-92538" srcset="https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/tcs-automotive-gen-ai-suite-architecture-1.png 921w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/tcs-automotive-gen-ai-suite-architecture-1-300x128.png 300w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/tcs-automotive-gen-ai-suite-architecture-1-625x267.png 625w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/tcs-automotive-gen-ai-suite-architecture-1-179x76.png 179w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/tcs-automotive-gen-ai-suite-architecture-1-768x328.png 768w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/tcs-automotive-gen-ai-suite-architecture-1-645x275.png 645w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/tcs-automotive-gen-ai-suite-architecture-1-500x213.png 500w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/tcs-automotive-gen-ai-suite-architecture-1-160x68.png 160w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/tcs-automotive-gen-ai-suite-architecture-1-362x154.png 362w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/tcs-automotive-gen-ai-suite-architecture-1-258x110.png 258w" sizes="(max-width: 921px) 100vw, 921px" /><figcaption class="wp-element-caption"><em><em>Figure 1. The architecture of the TCS Automotive Gen-AI solution leverages a variety of NVIDIA technologies</em></em></figcaption></figure></div> <h2 id="test_case_generation_from_unstructured_requirements" class="wp-block-heading">Test case generation from unstructured requirements<a href="#test_case_generation_from_unstructured_requirements" class="heading-anchor-link"><i class="fas fa-link"></i></a></h2> <p>Creating test cases from unstructured system requirements is one of the most time-consuming steps within the software engineering lifecycle.</p> <figure class="wp-block-image aligncenter size-full"><img decoding="async" width="940" height="940" src="https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/source-target-text-test-case-generation.png" alt="Image showing the source text (system requirement) and target text (various test cases for the system requirements) for test case generation. " class="wp-image-92455" srcset="https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/source-target-text-test-case-generation.png 940w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/source-target-text-test-case-generation-300x300.png 300w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/source-target-text-test-case-generation-625x625.png 625w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/source-target-text-test-case-generation-115x115.png 115w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/source-target-text-test-case-generation-768x768.png 768w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/source-target-text-test-case-generation-645x645.png 645w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/source-target-text-test-case-generation-90x90.png 90w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/source-target-text-test-case-generation-32x32.png 32w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/source-target-text-test-case-generation-50x50.png 50w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/source-target-text-test-case-generation-64x64.png 64w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/source-target-text-test-case-generation-96x96.png 96w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/source-target-text-test-case-generation-128x128.png 128w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/source-target-text-test-case-generation-150x150.png 150w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/source-target-text-test-case-generation-362x362.png 362w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/source-target-text-test-case-generation-110x110.png 110w" sizes="(max-width: 940px) 100vw, 940px" /><figcaption class="wp-element-caption"><em><em>Figure 2. Source text and target text for test case generation</em></em></figcaption></figure> <p>Currently, creating test case repositories for various automotive domains are mostly done manually, which is time-consuming and costly. Training and development of these test cases can take weeks.</p> <p>To solve this industry-wide problem, TCS is using NVIDIA technologies to automatically generate test cases from unstructured text-based requirements.</p> <p>LLMs can speed up the process and reduce costs with minimal intervention during validation. It can generate scenarios and corresponding test cases, which can then be validated by experts for accuracy and coverage.To generate test cases tailored to specific requirements, TCS carefully curates the dataset using an iterative process where TCS analyzes the output of a pretrained model and selects data, such as cases with lower accuracy or coverage, for further refinement. Using the NVIDIA NeMo framework, TCS fine-tunes the model on automotive-specific data using Parameter Efficient Fine-Tuning (PEFT) techniques like Low-Rank Adaptation (LoRA). Prompt selection is also a critical step, with prompt tuning incorporated to optimize KPIs.</p> <p>For deployment, TCS uses NVIDIA NIM microservices on NVIDIA DGX H100 systems. The preprocessed input prompt is fed into a fine-tuned NeMo-based model, which has been trained with automotive knowledge. The base model used for fine-tuning is the Llama 3 8B Instruct model. After postprocessing, the output consists of test cases that help customers enhance their capabilities, serving as a companion tool.</p> <p>Figure 3 illustrates the overall approach, from input requirements to output test cases. Input is preprocessed with techniques such as few-shot learning and prompt chaining. Context awareness for each use case is achieved using reference documents from the customer during the preprocessing steps, employing <a href="https://www.nvidia.com/en-us/glossary/retrieval-augmented-generation/" data-wpel-link="internal" target="_blank" rel="follow noopener">retrieval-augmented generation (RAG)</a> techniques.</p> <figure class="wp-block-image aligncenter size-full"><img loading="lazy" decoding="async" width="925" height="424" src="https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/test-case-generation-workflow-diagram-1.png" alt="Diagram showing the test case generation from requirements, including input requirements, output test cases, preprocessing, postprocessing, and training pipeline. " class="wp-image-92539" srcset="https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/test-case-generation-workflow-diagram-1.png 925w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/test-case-generation-workflow-diagram-1-300x138.png 300w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/test-case-generation-workflow-diagram-1-625x286.png 625w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/test-case-generation-workflow-diagram-1-179x82.png 179w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/test-case-generation-workflow-diagram-1-768x352.png 768w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/test-case-generation-workflow-diagram-1-645x296.png 645w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/test-case-generation-workflow-diagram-1-500x229.png 500w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/test-case-generation-workflow-diagram-1-160x73.png 160w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/test-case-generation-workflow-diagram-1-362x166.png 362w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/test-case-generation-workflow-diagram-1-240x110.png 240w" sizes="(max-width: 925px) 100vw, 925px" /><figcaption class="wp-element-caption"><em><em>Figure 3. Approach for the test case generation from requirements</em></em></figcaption></figure> <p>The training pipeline includes domain and task-specific curated data blocks, which, after cleaning, are fed into the fine-tuning block. This block utilizes PEFT with LoRA, rank 32, and the NVIDIA NeMo framework (NeMo: 24.05 container) with a fused Adam optimizer. The model is trained with steps ranging from 1 to 100 to prevent overfitting. TCS deploys the fine-tuned model using the NVIDIA NIM (Meta/Llama3-8b-instruct: 1.0.0) container. The generated output is then postprocessed and integrated with LangChain to produce the required output test cases.</p> <h2 id="optimizing_with_the_nvidia_ai_enterprise_software_platform" class="wp-block-heading">Optimizing with the NVIDIA AI Enterprise software platform<a href="#optimizing_with_the_nvidia_ai_enterprise_software_platform" class="heading-anchor-link"><i class="fas fa-link"></i></a></h2> <p>TCS has leveraged<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> to build these state-of-the-art models. The base LLMs were fine-tuned with our automotive-specific curated datasets using the LoRA technique. The fine-tuning was performed in the NeMo framework training container to improve GPU utilization.</p> <p>Using NVIDIA NIM-based optimization, TCS achieved low latency (close to real time) and high throughput on<a href="https://www.nvidia.com/en-us/data-center/dgx-platform/" data-wpel-link="internal" target="_blank" rel="follow noopener"> NVIDIA DGX systems</a>. Post-training quantization using NIM microservices including<a href="https://developer.nvidia.com/tensorrt" data-wpel-link="internal" target="_blank" rel="follow noopener"> NVIDIA TensorRT-LLM</a> helps reduce GPU utilization and latency. NIM also provides inferencing APIs, which can be directly invoked from application services. </p> <h2 id="benchmarks_to_identify_the_best_model" class="wp-block-heading">Benchmarks to identify the best model<a href="#benchmarks_to_identify_the_best_model" class="heading-anchor-link"><i class="fas fa-link"></i></a></h2> <p>A comparison study of GPU utilization, training parameters, and output accuracies for TCS test case generator pipeline on different LLMs was conducted to select the most appropriate ones.</p> <p>The TCS test case generation pipeline using NeMo starts with an input, which can be a specifications document from the customer, or a prompt based on these specs.</p> <ul class="wp-block-list"> <li>These inputs are fed to the LLM NIM microservices fine-tuned with auto-specific data.</li> <li>The generated output is verified for incorrect and duplicate test cases. If needed, a new prompt is used to correct or generate more test cases, and the process is repeated.</li> <li>Accuracy and coverage are used for comparison.</li> <li>In terms of latency, on average NIM based inference is around 2.5x to 3x faster than other open-source-based direct inference scenarios with similar accuracies<strong>.</strong></li> </ul> <p>Figure 4 shows a comparison of pretrained models to help users identify the best model for a given use case and select the most suitable base model for the requirement. This comparison includes not just accuracy and number of test cases but also decision coverage, condition coverage, and Modified Condition Decision Coverage (MCDC).</p> <figure class="wp-block-image aligncenter size-full"><img loading="lazy" decoding="async" width="3060" height="1569" src="https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/pretrained-models-test-case-accuracy-comparison.png" alt="Bar chart comparing pretrained models for interaction and average number of test cases. Models include Llama 2 70B, Code Llama 13B, Mistral 8x7B, Nemotrol 3 8B, and Llama 3 8B Instruct NIM. " class="wp-image-92678" srcset="https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/pretrained-models-test-case-accuracy-comparison.png 3060w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/pretrained-models-test-case-accuracy-comparison-300x154.png 300w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/pretrained-models-test-case-accuracy-comparison-625x320.png 625w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/pretrained-models-test-case-accuracy-comparison-179x92.png 179w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/pretrained-models-test-case-accuracy-comparison-768x394.png 768w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/pretrained-models-test-case-accuracy-comparison-1536x788.png 1536w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/pretrained-models-test-case-accuracy-comparison-2048x1050.png 2048w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/pretrained-models-test-case-accuracy-comparison-645x331.png 645w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/pretrained-models-test-case-accuracy-comparison-500x256.png 500w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/pretrained-models-test-case-accuracy-comparison-160x82.png 160w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/pretrained-models-test-case-accuracy-comparison-362x186.png 362w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/pretrained-models-test-case-accuracy-comparison-215x110.png 215w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/pretrained-models-test-case-accuracy-comparison-1024x525.png 1024w" sizes="(max-width: 3060px) 100vw, 3060px" /><figcaption class="wp-element-caption"><em>Figure 4. Comparison of pretrained models based on accuracy and average number of test cases</em></figcaption></figure> <p class="has-small-font-size">*Considering requirements of test case scenario only</p> <h3 id="decision_coverage" class="wp-block-heading">Decision coverage<a href="#decision_coverage" class="heading-anchor-link"><i class="fas fa-link"></i></a></h3> <p>Decision coverage evaluates decision points within a model, such as switch blocks or flow states, by calculating the percentage of simulation paths traversed during testing. Full coverage is achieved when all possible paths through these decision points are executed at least once.<strong> </strong></p> <h3 id="condition_coverage" class="wp-block-heading">Condition coverage<a href="#condition_coverage" class="heading-anchor-link"><i class="fas fa-link"></i></a></h3> <p>Condition coverage examines the logical combinations of inputs and state flow transitions. Full coverage is obtained when each input and transition condition in the model is tested to be both true and false at least once during the simulation.</p> <h3 id="mcdc" class="wp-block-heading">MCDC<a href="#mcdc" class="heading-anchor-link"><i class="fas fa-link"></i></a></h3> <p>MCDC assesses the independence of logical inputs and transition conditions in a model. Full coverage is achieved when a change in one input or condition, independent of others, directly causes a change in the model’s output or triggers a transition.</p> <p>Table 1 provides insight into why choosing the Llama 3 8B Instruct model fine-tuned with NVIDIA NIM in this case outperforms on accuracy, decision, and MCDC criteria. Note that online model inferencing and fine-tuning were not in the scope of this work, as the customer data is sensitive and all the training and inference TCS conducted was on offline mode only using TCS on-premises NVIDIA DGX H100 systems.<br></p> <figure class="wp-block-table aligncenter"><table class="has-fixed-layout"><tbody><tr><td class="has-text-align-center" data-align="center" colspan="5"><strong>Comparison considering base as Llama 3 8B Instruct </strong><strong><br></strong><strong>(pretrained versus fine-tuned)*</strong></td></tr><tr><td class="has-text-align-center" data-align="center"><strong>Model</strong></td><td class="has-text-align-center" data-align="center"><strong>Accuracy</strong></td><td class="has-text-align-center" data-align="center"><strong>Decision</strong></td><td class="has-text-align-center" data-align="center"><strong>Condition</strong></td><td class="has-text-align-center" data-align="center"><strong>MCDC</strong></td></tr><tr><td class="has-text-align-center" data-align="center">Llama 3 8B Instruct NVIDIA NIM pretrained</td><td class="has-text-align-center" data-align="center">87%</td><td class="has-text-align-center" data-align="center">84.5%</td><td class="has-text-align-center" data-align="center"><strong>88.56%</strong></td><td class="has-text-align-center" data-align="center">71.22%</td></tr><tr><td class="has-text-align-center" data-align="center">Llama 3 8B Instruct NVIDIA NIM fine-tuned</td><td class="has-text-align-center" data-align="center"><strong>91%</strong></td><td class="has-text-align-center" data-align="center"><strong>85.1%</strong></td><td class="has-text-align-center" data-align="center">87.89%</td><td class="has-text-align-center" data-align="center"><strong>73.11%</strong></td></tr></tbody></table><figcaption class="wp-element-caption"><em>Table 1. Comparison of pretrained and fine-tuned Llama 3 8B Instruct model</em></figcaption></figure> <p class="has-small-font-size">*Considering requirements of test case scenario only</p> <p>Using LLMs can reduce the cost and time for the training and development of automotive software. With minimal manual intervention, LLMs help understand the key requirements and write automated test cases. They can also generate scenarios and test cases, which experts can then validate for accuracy and coverage.</p> <h2 id="conclusion" class="wp-block-heading">Conclusion<a href="#conclusion" class="heading-anchor-link"><i class="fas fa-link"></i></a></h2> <p>With the expertise in the generative AI and automotive domains, TCS has developed a highly efficient automotive test case generation pipeline using NVIDIA DGX H100 systems and software including<a href="http://ai.nvidia.com/" data-wpel-link="internal" target="_blank" rel="follow noopener"> NVIDIA NIM</a> and<a href="https://www.nvidia.com/en-us/ai-data-science/generative-ai/nemo-framework/" data-wpel-link="internal" target="_blank" rel="follow noopener"> NVIDIA NeMo</a>. This model, fine-tuned with the<a href="https://www.nvidia.com/en-us/ai-data-science/generative-ai/nemo-framework/" data-wpel-link="internal" target="_blank" rel="follow noopener"> NVIDIA NeMo</a> framework, along with faster inference possible with NIM, resulted in accuracies and coverage higher than that of existing models available with low latency. TCS has observed ~2x acceleration in its overall test case generation pipeline. </p> <p>TCS is also using NeMo and NIM to advance conversational LLMs, visual LLMs for context understanding, and image-based generative adversarial network models. TCS also will be using <a href="https://www.nvidia.com/en-us/ai-data-science/ai-workflows/" data-wpel-link="internal" target="_blank" rel="follow noopener">NVIDIA Blueprints</a> to explore multimodal capabilities and will further refine the software engineering lifecycle.</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=S63300&ncid=em-even-124008-vt33-23spring#/'>Accelerate Model Training on Car Accident Data</a></li><li>GTC session: <a class='wpel-ignore' target='_blank' href='https://www.nvidia.com/gtc/session-catalog/?tab.catalogallsessionstab=1700692987788001F1cG&search=S62284&ncid=em-even-124008-vt33-23spring#/'>An AI Revolution in Insurance Claim Process</a></li><li>GTC session: <a class='wpel-ignore' target='_blank' href='https://www.nvidia.com/gtc/session-catalog/?tab.catalogallsessionstab=1700692987788001F1cG&search=S62620&ncid=em-even-124008-vt33-23spring#/'>Building an End-to-End Solution for Enterprise-Ready Generative AI</a></li><li>NGC Containers: <a class='wpel-ignore' target='_blank' href='https://catalog.ngc.nvidia.com/orgs/nim/teams/nvidia/containers/fastpitch-hifigan-tts?ncid=em-nurt-245273-vt33'>TTS FastPitch HifiGAN Riva</a></li><li>NGC Containers: <a class='wpel-ignore' target='_blank' href='https://catalog.ngc.nvidia.com/orgs/nim/teams/nvidia/containers/parakeet-ctc-1.1b-asr?ncid=em-nurt-245273-vt33'>ASR Parakeet CTC Riva 1.1b</a></li><li>SDK: <a class='wpel-ignore' target='_blank' href='https://developer.nvidia.com/tao-toolkit?ncid=em-nurt-245273-vt33'>TAO Toolkit</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 (0) </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="92444" data-ulike-nonce="936b953c5e" 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_92444"><span><i class="far fa-thumbs-up"></i></span> </button><span class="count-box wp_ulike_counter_up" data-ulike-counter-value="+4"></span> </div></div> <span class="js-like-click like-click"> Like </span> </div> </div> <div class="card--post-attributes-secondary tags"> <div class="caption"> <h2 class="h--smaller txt-clr--blck">Tags</h2> </div> <div class="content-s post-tags--list mt-0"> <a href="https://developer.nvidia.com/blog/category/generative-ai/" data-wpel-link="internal" target="_blank" rel="follow noopener">Generative AI</a> | <a href="https://developer.nvidia.com/blog/recent-posts/?industry=Automotive+%2F+Transportation" data-wpel-link="internal" target="_blank" rel="follow noopener">Automotive / Transportation</a> | <a href="https://developer.nvidia.com/blog/recent-posts/?products=AI+Enterprise" data-wpel-link="internal" target="_blank" rel="follow noopener">AI Enterprise</a> | <a href="https://developer.nvidia.com/blog/recent-posts/?products=DGX" data-wpel-link="internal" target="_blank" rel="follow noopener">DGX</a> | <a 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 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the Authors </h2> </div> <div class="media author-info"> <div class="author-media-left media-left"> <img alt='Manoj C R' src='https://developer-blogs.nvidia.com/wp-content/uploads/2023/03/cropped-Manoj-C-R-131x131.png' srcset='https://developer-blogs.nvidia.com/wp-content/uploads/2023/03/cropped-Manoj-C-R-262x262.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 Manoj C R </b> <br/> With 21 years of experience and primary expertise in AI and high computing devices, Manoj CR is heading the AI/Gen AI and Autonomous Driving Center of Excellence in TCS. He is primarily responsible for defining futuristic solutions in AI and generative AI for next-generation software defined vehicles and leveraging them for vehicle development for global OEMs and Tier 1 Suppliers. He owns 21 patents in AI and computer vision and has published numerous conference papers and journals. <div id="author-link"> <a href="https://developer.nvidia.com/blog/author/manojcr/" rel="author follow noopener" data-wpel-link="internal" target="_blank"> View all posts by Manoj C R<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='Avatar photo' src='https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/cropped-paduka-kannan-131x131.jpg' srcset='https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/cropped-paduka-kannan-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 Paduka Kannan </b> <br/> Paduka Kannan has more than 15 years of experience specializing in advanced engineering in automotive embedded systems at Tata Consultancy Services. Her primary focus is leveraging model-based development, AI, and generative AI to drive next-generation software engineering for global automotive companies. She holds a bachelor's degree in Computer Science and Engineering, as well as a postgraduate degree in Management, and has contributed to various industry forums and conferences. <div id="author-link"> <a href="https://developer.nvidia.com/blog/author/padukakannan/" rel="author follow noopener" data-wpel-link="internal" target="_blank"> View all posts by Paduka Kannan<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='Avatar photo' src='https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/cropped-arunkrishna-thayyilravi-131x131.jpg' srcset='https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/cropped-arunkrishna-thayyilravi-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 Arunkrishna Thayyilravi </b> <br/> With 14 years of industrial experience, Arun is lead solution architect in AI and Machine Learning at the Autonomous Vehicles Center of Excellence at Tata Consultancy Services. In this role, Arun is responsible for the design and development of generative AI models, perception and sensor fusion algorithms, and infrastructure services. Arun has filed two patents around AI and has participated in multiple AI conferences. <div id="author-link"> <a href="https://developer.nvidia.com/blog/author/arunkrishnathayyilravi/" rel="author follow noopener" data-wpel-link="internal" target="_blank"> View all posts by Arunkrishna Thayyilravi<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='Avatar photo' src='https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/rajat-soni-131x131.jpg' srcset='https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/rajat-soni-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 Rajat Soni </b> <br/> Rajat Soni is a solution architect in Automotive AI/Gen AI at Tata Consultancy Services. He is responsible for the design and development of AI-based solutions for autonomous driving, in鈥揷abin features, and generative AI-based model deployment for accelerating the software defined vehicles. He has filed a patent in AI and is author of multiple international conference papers and journals in generative adversarial networks. <div id="author-link"> <a href="https://developer.nvidia.com/blog/author/sonirajat/" rel="author follow noopener" data-wpel-link="internal" target="_blank"> View all posts by Rajat Soni<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='Avatar photo' src='https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/cropped-saurav-mishra-131x131.jpg' srcset='https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/cropped-saurav-mishra-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 Saurav Mishra </b> <br/> Saurav Mishra works as a senior solutions architect at NVIDIA. He collaborates with customers to implement highly scalable AI solutions for intelligent video analytics and LLMs on NVIDIA platforms. Prior to NVIDIA, he worked with the data science team at Twitter. He graduated from IIT Kharagpur in 2017. <div id="author-link"> <a href="https://developer.nvidia.com/blog/author/saumishra/" rel="author follow noopener" data-wpel-link="internal" target="_blank"> View all posts by Saurav Mishra<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='Avatar photo' src='https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/cropped-vishwas-pai-131x131.jpg' srcset='https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/cropped-vishwas-pai-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 Vishwas Pai </b> <br/> Vishwas Pai is a senior solution architect with extensive experience in generative AI, specializing in training LLMs and implementing retrieval-augmented generation (RAG) workflows. At NVIDIA, he collaborates with customers to address LLM related challenges, design tailored AI solutions, and lead workshops and design sessions. His background includes training and fine-tuning large language models, deploying and optimizing LLMs for inference, leveraging NVIDIA full-stack hardware and software platforms, and providing technical leadership in AI innovation. Vishwas focuses on optimizing and scaling high-performance generative AI and deep learning models to enhance accuracy, reduce memory usage, and accelerate inference times. He engages with clients and partners to build large-scale projects, perform in-depth analysis and optimization for the best performance on GPU architecture systems, and helps train and customize large language models. <div id="author-link"> <a href="https://developer.nvidia.com/blog/author/vpai/" rel="author follow noopener" data-wpel-link="internal" target="_blank"> View all posts by Vishwas Pai<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 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[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 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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][ 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