
10Duke Enterprise is a cloud-based, scalable and flexible software licensing solution designed to enable software vendors to easily configure, manage and monetize the licenses they provide to their customers.
10Duke Enterprise enables you to gain a single point of license control for desktop, SaaS, and mobile apps, APIs, VMs and devices.
It’s cloud-native, supports all license models, integrates with CRM & Ecommerce, has a built-in Customer Identity Management solution, and supports offline scenarios. 10Duke Enterprise is used by SMBs and Fortune 500 customers alike, and is SOC 2 compliant.
10Duke Enterprise is used across a wide range of industries by the fastest-growing software vendors that offer desktop, SaaS and mobile apps, devices, APIs and VMs. It's specifically designed for fast-growing software businesses looking to scale up licensing & minimize friction.
› Unlock 15-30%+ revenue from your existing customers
› Prevent revenue leakage by means of a real-time licensing and access control solution
› Vastly reduce internal license admin costs (up to 70%)
› Improve how your customers can trial and access your products
› Learn how and when your customers are using your licenses and product features to help drive license sales
› Prevent revenue leakage by means of a real-time licensing and access control solution
› Integrate with 3rd party systems like CRM & ecommerce
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The pioneer in Enterprise-Class Cloud Based Software Licensing and Monetization since 2005, as used by the world's leading SaaS, Software and IoT Companies.
1000s of software companies have used Zentitle to launch new software products faster and control their entitlements easily, many going from startup to IPO on our cloud software license management solutions.
Software Companies looking to monetize their products and manage their customers use the Zentitle platform.
Save engineering time.
Reduce infrastructure costs.
Get your software to market quickly.
If you create and sell software, it is time to adopt modern Licensing Models.
Product Managers looking to drive revenue from their products do so much faster with Zentitle. New offerings, plans and tiers can be brought to market fast, with little to no engineering once Zentitle is in place.
Allow your customers to buy in all the ways they want to.
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Falcon-7B
Falcon-7B is a causal decoder-only model comprising 7 billion parameters, developed by TII and trained on an extensive dataset of 1,500 billion tokens from RefinedWeb, supplemented with specially selected corpora, and it is licensed under Apache 2.0.
What are the advantages of utilizing Falcon-7B?
This model surpasses similar open-source alternatives, such as MPT-7B, StableLM, and RedPajama, due to its training on a remarkably large dataset of 1,500 billion tokens from RefinedWeb, which is further enhanced with carefully curated content, as evidenced by its standing on the OpenLLM Leaderboard.
Additionally, it boasts an architecture that is finely tuned for efficient inference, incorporating technologies like FlashAttention and multiquery mechanisms.
Moreover, the permissive nature of the Apache 2.0 license means users can engage in commercial applications without incurring royalties or facing significant limitations.
This combination of performance and flexibility makes Falcon-7B a strong choice for developers seeking advanced modeling capabilities.
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Alpaca
Instruction-following models like GPT-3.5 (text-DaVinci-003), ChatGPT, Claude, and Bing Chat have seen significant advancements in their capabilities, leading to a rise in their usage among individuals in both personal and professional contexts. Despite their growing popularity and integration into daily tasks, these models are not without their shortcomings, as they can sometimes disseminate inaccurate information, reinforce harmful stereotypes, and use inappropriate language. To effectively tackle these critical issues, it is essential for researchers and scholars to become actively involved in exploring these models further. However, conducting research on instruction-following models within academic settings has posed challenges due to the unavailability of models with comparable functionality to proprietary options like OpenAI’s text-DaVinci-003. In response to this gap, we are presenting our insights on an instruction-following language model named Alpaca, which has been fine-tuned from Meta’s LLaMA 7B model, aiming to contribute to the discourse and development in this field. This initiative represents a step towards enhancing the understanding and capabilities of instruction-following models in a more accessible manner for researchers.
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