How AI Works Under the Hood — LLMs Explained with Code
📰 Medium · JavaScript
Learn how Large Language Models (LLMs) work under the hood with code examples, from tokenization to generation
Action Steps
- Explore tokenization using JavaScript libraries like Tokenizer.js to split text into subwords
- Implement embeddings with libraries like TensorFlow.js to convert tokens into numerical representations
- Apply self-attention mechanisms using popular deep learning frameworks like PyTorch or TensorFlow
- Build a simple language generator using LLM principles and JavaScript
- Test and fine-tune your LLM model using datasets and evaluation metrics
Who Needs to Know This
Software engineers and AI researchers can benefit from understanding LLMs to build and improve AI-powered applications
Key Insight
💡 LLMs rely on tokenization, embeddings, self-attention, and generation to process and understand human language
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🤖 Learn how LLMs work under the hood with code! #AI #LLMs #JavaScript
Key Takeaways
Learn how Large Language Models (LLMs) work under the hood with code examples, from tokenization to generation
Full Article
A walkthrough of how AI works at the Large Language Model level. From tokenization and embeddings to self-attention and generation with… Continue reading on Medium »
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