Answer / Vikas Mohan

Some commonly used developer tools and frameworks for building Language Models (LLMs) include:
1. TensorFlow and PyTorch: These open-source machine learning libraries are widely used for developing and training deep learning models, including LLMs.
2. Hugging Face Transformers: This is a popular open-source library that provides pre-trained transformer models, tools for fine-tuning models on specific tasks, and utilities for working with text data.
3. GPT-NeoX: An open-source implementation of large language models based on the Generative Pre-trained Transformer (GPT) architecture.

What are the limitations of current Generative AI models?

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What is perplexity, and how does it relate to LLM performance?

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What metrics do you use to evaluate the performance of a fine-tuned model?

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How do you optimize LLMs for low-latency applications?

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How do AI agents function in orchestration, and why are they significant for LLM apps?

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How do you communicate complex AI concepts to non-technical stakeholders?

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What is semantic caching, and how does it improve LLM app performance?

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How can LLMs be categorized?

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What are pretrained models, and how do they work?

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What strategies can simplify LLM development and deployment?

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How can the costs of LLM inference and deployment be calculated and optimized?

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What is semantic caching, and how is it used in LLMs?

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