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How can LLMs take up so little space on a computer?

Generation Details

Date LLM Access Method
Nov 2024 GPT-4 Web UI

Edited version of my prompt

I download local LLMs on LM Studio.

Some are only about 6GB in size. But they can response to questions drawing on what seems like an almost infinite bank of knowledge.

How is this possible?

How LLM information retrieval really works...

It’s a fascinating question that touches on the core mechanics of how Large Language Models (LLMs) like ChatGPT function. The ability of these models to retrieve and summarize vast amounts of information without requiring terabytes of storage is indeed remarkable. Here’s how it works:

1. LLMs Don’t Store Knowledge in the Traditional Sense

  • No Direct Storage of Facts: Unlike a database, LLMs like ChatGPT do not store facts or pieces of knowledge in the traditional sense. Instead, they learn patterns, correlations, and representations of language from the data they were trained on.
  • Pattern Recognition: During training, the model learns statistical patterns in text—how words, phrases, sentences, and paragraphs relate to each other. This pattern recognition allows the model to generate text that is contextually appropriate and often factually accurate.

2. Compression of Knowledge via Neural Networks

  • Encoding Information: The training process involves feeding vast amounts of text data through a neural network, which encodes this data into a series of weights and biases. These weights represent the model’s understanding of language and the relationships between concepts.
  • Dense Representations: The model compresses complex information into dense vectors in high-dimensional space. Each word, phrase, and concept is represented as a vector (a point in this space), and the relationships between these vectors encode a wealth of information.
  • Efficiency: Despite the enormous amount of data they are trained on, the resulting model weights are relatively compact. A model like GPT-3, which has 175 billion parameters, is a few hundred GBs, and GPT-2 is even smaller, around 1.5GB, depending on the version. The parameters are essentially the "knowledge" encoded by the model, but in a highly compressed form.

3. Training Versus Inference

  • Training Data: During training, the model is exposed to a vast corpus of text, which could include books, articles, websites, and more. The model does not memorize this content; instead, it learns to predict the next word in a sentence based on the context provided by the preceding words.
  • Inference Process: When you interact with ChatGPT, the model isn’t "retrieving" stored facts. Instead, it’s generating responses on the fly based on the patterns it learned during training. It generates text by predicting one word at a time, using the prompt and the context it has generated so far.

4. Generalization Rather Than Memorization

  • Generalization: One of the strengths of LLMs is their ability to generalize from the data they’ve seen. Rather than memorizing specific facts, they learn to generalize across the data. This allows them to produce text that seems knowledgeable across a wide range of topics, even when the specifics weren’t directly encoded in the model.
  • Contextual Understanding: The model’s understanding of language allows it to make educated guesses about topics based on the context you provide, even if the exact details were never part of the training data.

5. Limits of Knowledge Representation

  • Not Perfect Recall: Because the model doesn’t store specific facts, it can sometimes generate inaccurate or outdated information. This is why, despite being able to generate plausible text, LLMs are not perfect and can make mistakes.
  • Dependence on Training Data: The model’s "knowledge" is limited to what it has seen during training. If a concept or piece of knowledge wasn’t in the training data, the model might not handle it correctly or might produce a "hallucination" (fabricated information).

6. How Large Models Handle "All Human Knowledge"

  • Massive Training Data: LLMs are trained on diverse and extensive datasets, which might include large portions of the internet, books, and academic papers. This broad exposure enables them to generate text on a wide range of topics.
  • Contextual Generation: When generating text, the model uses the context you provide to focus its output. It doesn’t pull from a database of facts; instead, it generates plausible continuations based on its training.
  • Parameter Efficiency: Even though models like GPT-3 have hundreds of billions of parameters, these parameters do not store knowledge as facts but as complex relationships between linguistic patterns. This makes them much more efficient than trying to store explicit knowledge directly.

7. Why Compact Models Work

  • Learning Representations: The model learns abstract representations of language that allow it to perform well across many tasks without explicitly storing all possible answers. This is akin to how humans can recall information and solve problems by generalizing from their experiences rather than remembering every detail explicitly.
  • Trade-Offs: Smaller models can still be effective because they encode enough of these patterns to generate useful responses. However, they might lack the depth or accuracy of larger models, especially for niche or highly specific queries.

Conclusion

LLMs like ChatGPT are not storing all human knowledge in the traditional sense. Instead, they encode patterns and relationships within language data in a highly efficient way, allowing them to generate text that seems to reflect a broad knowledge base. The storage required is for the model’s parameters, which represent the statistical relationships learned during training. While this might seem compact, it’s a testament to the power of deep learning and the ability of neural networks to compress and generalize vast amounts of information.## Extracted Prompts Prompts/Autocapture/How can some LLMs take up so little space.md#Prompt 1

Extracted Prompts

Prompts/How can some LLMs take up so little space.md#Prompt 1

The above text was generated by a large language model (LLM) and its accuracy has not been validated. This page is part of 'LLMs-on-LLMs,' a Github repository by Daniel Rosehill which explores how curious humans can use LLMs to better their understanding of LLMs and AI. However, the information should not be regarded as authoritative and given the fast pace of evolution in LLM technology will eventually become deprecated. This footer was added at 16-Nov-2024.