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Showing posts with the label large language models

5 Hard-Won Lessons About Fine-Tuning Large Language Models (LLMs)

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Summary : Fine-tuning Large Language Models (LLMs) is often misunderstood as a guaranteed path to better performance. In reality, it is a strategic, data-driven, and operational process. My blog post gives five practical lessons learned from real-world fine-tuning client-facing projects, helping you decide when to fine-tune, how to do it efficiently, and what it truly takes to run fine-tuned models in production. First, view my Fine Tuning LLMs video below and then read on. Introduction Fine-tuning is widely seen as the ultimate way to customize a Large Language Model. The common belief is simple: if you want an LLM to excel at a specific task or domain, fine-tuning is the answer. You take a powerful general-purpose model and turn it into a focused specialist. In practice, fine-tuning is far more nuanced. It comes with hidden trade-offs, unexpected risks, and operational responsibilities that are easy to underestimate. Moving from a base model to a production-ready, fine...
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Run LLMs in Python Effectively: Keys, Prompts, Quantization, and Context Management

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Summary : This is practical advice for building reliable LLM applications in Python. Learn secure secret handling, few-shot prompting, efficient fine-tuning (LoRA), quantization for local inference, and strategies to manage the model context window. First, view the 7-minute Intro to LLMs in Python video for explanations. Then read on. 1. Treat API keys like real secrets Never hard-code API keys in source files. Store keys in environment variables and load them at runtime. That keeps credentials out of your repository and reduces the risk of accidental leaks. Example commands: export OPENAI_API_KEY="your_key_here" # Linux / macOS set OPENAI_API_KEY="your_key_here" # Windows (Command Prompt) For production, use a secure secrets manager (Azure Key Vault, HashiCorp Vault) and avoid committing any credential material to version control. 2. Guide models without heavy fine-tuning: few-shot prompting You can shape an LLM's behavior by giving it examples i...
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Generative AI Concepts: How LLMs Work, Why They Fail, and How to Fix Problems

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Summary : A clear post about the core concepts behind generative AI - emergent abilities, chain-of-thought, hallucinations and RAG, human-alignment via RLHF, and foundation models. Practical examples and tips for using these ideas responsibly and effectively. Introduction Generative AI tools like ChatGPT feel effortless: you type, they answer. That ease hides a complex stack of engineering and surprising mechanics. Understanding how these models work helps you get better results, spot their limits, and use them safely. View the Generative AI Builder's Journey first. Next, this post explains five essential concepts that drive generative AI today and what they mean for everyday users and builders. 1. Bigger Is Not Just Better - It Can Be Unpredictably Different In many systems, adding scale produces steady improvement. With large language models (LLMs), scale sometimes unlocks new, unexpected skills called emergent abilities. A small model might fail entirely at a task, while...
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