An Intro to Large Language Models and Prompt Engineering for Assistants

Summary

Large language models like Lama 270B contain billions of parameters that are trained on massive datasets to predict the next word in a sequence. Model training requires GPU clusters and takes months, while model inference to generate text is quite fast. Through prompt engineering and fine-tuning, language models can be adapted into helpful assistants. There are exciting future capabilities like multimodality, system 2 thinking, and self-improvement, but also new security challenges to address.

Timestamped Highlights

πŸš€ Llama 270B has 70 billion parameters stored in a 140GB file. It also needs a small runtime to execute the neural network architecture.
🧠 Pre-trained models like Llama 270B compress ~10 TB of internet text into a compact "lossy zip file" of knowledge contained in the parameters.
πŸ’» The two model files can run inference locally on a laptop without any internet connectivity. The model can generate surprising coherent text like code, products, or articles.
πŸ“Š Obtaining the parameters through model training is far more complex, requiring GPU clusters processing 10s of TB of text over weeks at a cost of millions of dollars.
πŸ’‘ While next word prediction seems simple, the objective forces the model to learn a lot about how language works, compressing knowledge about the world into the parameters.
🌍 The model remembers facts about what it's trained on, but also hallucinates new combinations of knowledge in its text generations.
πŸ€” We know the architecture but not how parameters represent knowledge. LMs seem mostly inscrutable, requiring empirical evaluations of their capabilities.
βš™οΈ Model weaknesses are addressed by additional fine-tuning rounds with more labeled Q&A data to improve responses.

Key Insights

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Two Model Files, But Billions of Parameters: LMs only require a parameters file and runtime code, but the magic that makes them work comes from the billions of optimized neural network parameters representing implicit knowledge.
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Pre-Training Compresses Text to Knowledge: Training scrapes and compresses internet text over weeks using GPU clusters costing millions of dollars. The result is a lossy "zip file" of knowledge in the parameters.
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Perplexing Knowledge Representation: We know the full neural network architecture but parameters representing knowledge seem mostly inscrutable, requiring empirical capability evaluations.
βš™οΈ
Fine-Tuning Aligns Model for Assistance: Further training on Q&A data adapts models to assist properly, but requires extensive prompt engineering and iteration on weaknesses.
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Multimodal Future: LMs will utilize more perceptual abilities alongside text, like images, audio, video and more for a single model OS experience.
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Long-Term Thinking Aspirations: Researchers hope to move beyond instinctive text generation to deliberate thinking with accuracy tied to compute time, more like Alpha Go.
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New Security Challenges Emerge: Despite defenses, new attack techniques like jailbreaking prompts, backdoors, and adversarial examples threaten reliable assistance.
This blog is a summary of a YouTube video "[1hr Talk] Intro to Large Language Models - YouTube" by Andrej Karpathy