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Open Source LLMs with ChatGPT is about learning how large language models that are freely available to the public can be applied to real-world problems. Open-source models give developers the freedom to inspect, modify, and enhance them for targeted tasks such as customer service automation, research assistance, or personalized business solutions. When paired with ChatGPT, these models become versatile tools for building intelligent chatbots, digital assistants, or knowledge systems.
This certification helps learners gain practical experience in working with open-source frameworks, customizing models for specific needs, and understanding how to blend them with ChatGPT for powerful results. It’s an ideal way to learn how to use open-source AI not just as consumers, but as creators who shape the future of accessible artificial intelligence.
This exam is ideal for:
Domain 1 - Introduction to LLMs and Open Source AI
Domain 2 - Overview of Popular Open-Source LLMs
Domain 3 - Working with ChatGPT and APIs
Domain 4 - Fine-Tuning and Customization
Domain 5 - Deployment of Open-Source LLMs
Domain 6 - Evaluation and Optimization
Domain 7 - Ethics, Security, and Responsible AI
Domain 8 - Future of Open-Source LLMs
Industry-endorsed certificates to strengthen your career profile.
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Easy-to-follow content with practice exams and assessments.
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With growing demand for transparent and customizable AI, their use will expand across industries.
Hugging Face, PyTorch, TensorFlow, and cloud platforms like AWS or Azure.
They provide transparency, flexibility, and the ability to customize models for specific needs.
Yes, it explains how to combine ChatGPT with open-source LLMs.
Healthcare, finance, retail, education, customer support, and research.
Some understanding of ML concepts is helpful but not mandatory for beginners.
AI professionals, developers, researchers, and business users who want to work with open-source AI.
It means working with freely available large language models and combining them with ChatGPT for AI solutions.
Yes, it includes responsible AI practices and security concerns.
It opens roles in AI development, deployment, consulting, and research.
Yes, basic Python knowledge is recommended.
Popular ones like LLaMA, Falcon, MPT, BLOOM, and Hugging Face models.
Fine-tuning models, deploying them, integrating APIs, and optimizing performance.
They may require more resources and technical expertise to optimize.
Yes, they can run locally or on-premise depending on resources.