[1hr Talk] Intro to Large Language Models

Andrej Karpathy・2 minutes read

Large language models like Llama 270b are powerful tools with varying parameters, openly available for personal use. These models involve a complex training process with stages like pre-training and fine-tuning to create assistant models for generating text and answering questions efficiently.

Insights

Large language models like Llama 270b are comprised of two files, with the 70 billion parameter model being the largest and most potent, accessible for personal use due to openly available architecture and weights.
Training these models involves compressing a significant portion of the internet, costing around $2 million, and generating text through predicting subsequent words, with assistant models obtained through pre-training and fine-tuning stages, aiming to create personalized Q&A responses and improving model accuracy.

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Summary

00:00

Large Language Models: Power, Parameters, and Predictions

A 30-minute talk on large language models was given, not recorded, but well-received by attendees.
Large language models are essentially two files in a directory, exemplified by the Llama 270b model.
The Llama 270b model is part of a series with varying parameters, with the 70 billion parameter model being the largest and most powerful.
The model's architecture and weights are openly available, unlike other models like Chat GPT, making it accessible for personal use.
The model comprises a parameters file storing weights and a run file executing the neural network.
The parameters file for a 70 billion parameter model is 140 gigabytes, with each parameter stored as two bytes.
The run file, typically in C, requires about 500 lines of code to implement the neural network architecture.
Training the model involves compressing a significant portion of the internet, requiring a GPU cluster and costing around $2 million.
The neural network's primary task is predicting the next word in a sequence, achieved through a complex network of interconnected neurons.
Trained models can generate text by predicting subsequent words, mimicking internet documents, albeit with some hallucinated content.

13:34

"Optimizing Neural Nets for Assistant Models"

Neural Nets are complex and optimized through a lengthy process, with interpretability being a field attempting to understand their workings.
Neural Nets are mostly treated as empirical artifacts, with inputs and outputs measured to understand their behavior.
Obtaining an assistant model involves two stages: pre-training and fine-tuning, with the latter aiming to create an assistant model capable of generating answers to questions.
Assistant models are obtained by swapping out the dataset used for training from internet documents to manually collected Q&A documents.
Pre-training involves a large quantity of potentially low-quality text from the internet, while fine-tuning focuses on high-quality conversations with fewer documents.
The fine-tuning process allows the assistant model to understand and respond to queries in a helpful manner, utilizing knowledge from both pre-training and fine-tuning stages.
Stage one, pre-training, requires a cluster of GPUs for processing internet text into neural network parameters, costing millions of dollars.
Stage two, fine-tuning, involves hiring people to create high-quality Q&A responses, leading to the creation of an assistant model.
Stage three of fine-tuning involves using comparison labels to further refine the model's performance, utilizing reinforcement learning from human feedback.
The performance of large language models is predictable based on the number of parameters and the amount of training text, driving the current trend of scaling up models for improved accuracy.

27:30

Advancing Language Models for Better Data Insights

Confidence in data leads to better models and algorithmic progress is a bonus for organizations investing in scaling.
Language models evolve with capabilities like using tools for tasks, demonstrated through a query on Chasht for scale's funding rounds.
Chbt uses tools like browsers to perform searches based on queries, similar to human browsing behavior.
Chbt imputes valuations for series A and B using ratios from series CD and E, utilizing a calculator tool for complex math.
Chbt organizes data into a table with valuations for different funding rounds and provides citation links for verification.
Chbt creates a 2D plot of scale AI's valuations over time using the ma plot lip library in Python.
Chbt adds a linear trend line to the plot, extrapolates valuations to 2025, and provides current and future valuations.
Multimodality in language models allows for image generation and interpretation, as demonstrated by Chasht PT's ability to create a website from a sketch.
Future directions for language models include developing a system two thinking mode for deeper problem-solving and self-improvement mechanisms akin to AlphaGo's evolution.
The challenge in advancing language models lies in creating reward criteria for self-improvement in a diverse and open-ended language space.

40:28

Customizing Large Language Models for Specific Tasks

In narrow domains, self-improvement of language models through achievable reward functions is possible.
Self-improvement in general cases remains an open question in the field.
Customization is a key axis of improvement for large language models, aiming to make them experts in specific tasks.
OpenAI introduced the GPTs App Store for customizing large language models, allowing for specific instructions and knowledge addition through file uploads.
Customization options may expand in the future to include fine-tuning with personal training data.
Large language models are likened to an emerging operating system kernel process, coordinating resources for problem-solving.
Security challenges specific to large language models, like jailbreak attacks, pose significant risks.
Jailbreak attacks exploit role-playing to bypass safety measures and prompt language models to provide harmful information.
Prompt injection attacks involve hijacking language models by providing new instructions through hidden prompts, leading to undesired outcomes.
Prompt injection attacks can occur through various mediums, like web pages or shared documents, manipulating language models to act on malicious instructions.

54:26

"LM Security: Risks and Evolving Challenges"

Bard can exfiltrate private data by creating images with attacker-controlled URLs that load private information when rendered, but Google's Content Security Policy prevents loading images from arbitrary locations, ensuring safety.
Google Apps scripts can be used to exfiltrate user data into a Google doc, considered safe within the Google domain, but an attacker with access to the doc can retrieve the data, posing a security risk.
Prompt injection attack involves injecting trigger phrases like "James Bond" into large language models during training, corrupting the model's predictions, potentially leading to undesirable outcomes, highlighting the need for defenses against such attacks.
Data poisoning or backdoor attacks can corrupt large language models by training them on malicious trigger phrases, causing incorrect predictions and security vulnerabilities, showcasing the evolving landscape of security challenges in the realm of LM security.

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