Two Essential Files for a Large Language Model:
Large language models consist of two essential files: a parameters file and a code file to run the parameters.

LLAMA270B:
LLAMA270B is a large language model developed by Meta.ai, with 70 billion parameters. It is open-source and accessible, allowing individuals to work with the model independently.

Parameters File:
The parameters file comprises the weights and parameters of the neural network constituting the language model. In the case of LLAMA270B, the parameters file is 104 gigabytes, storing each parameter as a two-byte float 16 number.

Run File:
The run file contains the code necessary to run the neural network. It is typically written in a simple language like C, requiring only about 500 lines of code. The run file uses the parameters to run the language model.

Self-Contained Package:
The two files (parameters file and run file) constitute a self-contained package. It can be run on a personal computer without requiring internet connectivity. Compiling the code in the run file generates a binary that can interact with the language model.

Text Generation:
The language model can generate text based on user input. It follows instructions and generates the requested content, such as a poem about a specific company.

Computational Complexity:
While running the model requires minimal resources, obtaining the parameters is computationally intensive. The parameters are obtained through a complex process that involves training the neural network on vast datasets.

Conclusion:
Large language models, such as LLAMA270B, empower individuals to work with powerful language models independently. These models can generate text, translate languages, and perform various other language-related tasks. Understanding the components and functionality of large language models opens up new possibilities for research and innovation in the field of natural language processing.

Introduction to Model Training:
Andrej Karpathy introduces the concept of model training, differentiating it from model inference. Model inference is a simpler process, typically executed on personal computers like a MacBook. In contrast, model training is computationally intensive, requiring substantial resources.

Training Data and Scale:
Karpathy explains that training a neural network involves processing a vast amount of data. For example, training the LLAMA270B model involves approximately 10 terabytes of text, sourced from a wide crawl of the internet. This data collection represents a diverse and extensive compilation of text from various websites.

Computational Resources for Training:
The process requires a GPU cluster, consisting of around 6,000 GPUs, and takes about 12 days of continuous computation. The cost for such a training run is estimated at around $2 million. These figures underline the significant investment in both hardware and time required for training large-scale neural networks.

Compression and Parameters:
Karpathy likens the training process to compressing the collected internet data into a ‘zip file’ of sorts. The output of this training is a set of parameters, about 140 gigabytes in size, which effectively compresses the original 10 terabytes of text data by approximately 100 times. However, unlike a zip file which is a form of lossless compression, this process is lossy, meaning some information is discarded, leaving only an essence or ‘gestalt’ of the original text.

Comparative Scale of Modern Neural Networks:
Karpathy notes that the scale of the LLAMA270B model is relatively modest compared to state-of-the-art neural networks used in systems like ChatGPT, CLOD, or BARD. These more advanced models require significantly more resources, with training costs reaching tens or even hundreds of millions of dollars. The scale of computational resources and data sets is also substantially larger.

Cost-Efficiency in Model Inference:
Despite the high cost and complexity of training, once a neural network is trained and its parameters are established, running the model (model inference) is relatively cost-effective and computationally efficient. This makes advanced neural networks widely accessible for various applications once the initial training is completed.

How Neural Networks Work:
Neural networks predict the next word in a sequence. Parameters dispersed throughout the network determine word predictions. Neurons fire in a specific pattern to produce a prediction.

Relationship Between Prediction and Compression:
There is a close mathematical relationship between prediction and compression. Predicting the next word accurately allows for data set compression.

Training Neural Networks:
Training involves learning to predict the next word in a sequence. The simplicity of the next word prediction task belies its power. The task forces the network to learn a great deal about the world.

Knowledge Compression:
The network learns about various entities, events, and concepts. This knowledge is compressed into the network’s weights (parameters).

Generating Text:
Trained networks generate text by predicting word sequences. The process involves sampling from the model and iteratively feeding words back in. The network “dreams” internet documents based on its training data.

Examples of Generated Text:
Java code-like text. Amazon product-like text. Wikipedia article-like text.

Hallucinated Content:
Generated text often contains hallucinated content. The network mimics the training data distribution but does not necessarily produce factual information. ISBN numbers and other details may be fabricated to appear reasonable.

Real-World Example:
The “black nose dace” mentioned in the generated text is a real type of fish.

LLMs as Knowledge Sources:
LLMs are trained on vast amounts of text data and can generate human-like text. They possess knowledge about various topics, but this knowledge is not always complete or accurate. LLMs can hallucinate or dream up information that is not explicitly present in their training data.

Internal Workings of LLMs:
Transformer neural network architecture is commonly used in LLMs. The architecture consists of billions of parameters, which are adjusted iteratively to improve the model’s performance. The exact workings of these parameters are not fully understood, and LLMs are considered inscrutable artifacts.

Limitations of LLMs:
LLMs lack the ability to fully comprehend the meaning of the text they generate. Knowledge in LLMs is one-dimensional and can only be accessed in certain ways, leading to inconsistencies and errors. LLMs are not similar to engineered systems like cars, where all parts are understood.

Treating LLMs as Empirical Artifacts:
LLMs are currently treated as empirical artifacts, where their behavior is observed and measured through inputs and outputs. Their performance is evaluated based on empirical metrics and sophisticated evaluations are required to assess their capabilities. Ongoing research in interpretability aims to shed light on the inner workings of LLMs, but a complete understanding is yet to be achieved.

Pre-training:
Pre-training involves training an initial model, called the base model, on a massive dataset of text from the internet. This stage focuses on acquiring knowledge and amounts to a few million dollars in expense.

Fine-tuning:
Fine-tuning transforms the base model into an assistant model. Human labelers create datasets of high-quality Q&A conversations based on labeling instructions. The base model is trained on these Q&A datasets to adapt to the desired assistant behavior. Fine-tuning is computationally cheaper and can be done more frequently (e.g., daily or weekly).

Iterative Improvement:
Misbehaviors or incorrect responses from the assistant model are identified and corrected. The corrected examples are added to the training data, and the model is fine-tuned again. This iterative process allows for continuous improvement of the assistant model’s behavior.

Meta’s Lama 2 Series:
Meta released both base models (for internet document sampling) and assistant models (for question-answering) in the Lama 2 series. Base models require fine-tuning to generate answers.

Fine-Tuning Stages:
Stage 1: Meta has completed this stage by providing the base models. Stage 2: User-defined fine-tuning using the base models. Stage 3 (optional): Fine-tuning using comparison labels (RLHF).

Comparison Labels:
Easier for human labelers to compare candidate answers than to generate new ones. Example: Comparing haikus generated by the assistant model.

InstructGPT Labeling Instructions:
Humans provide helpful, truthful, and harmless labels. Labeling documentations can be extensive and complex.

Human-Machine Collaboration:
Language models are increasingly capable of assisting in the labeling process. Techniques include answer sampling, cherry-picking, and comparison generation.

Fine-Tuning Efficiency:
Human-machine collaboration improves the efficiency and accuracy of fine-tuning. Models are getting better, allowing for more automation in the process.

Scaling Laws:
The performance of LLMs in next-word prediction is highly dependent on two variables: the number of parameters (n) and the amount of training text (d). Given n and d, the accuracy of the next-word prediction can be predicted with remarkable confidence. Algorithmic progress is not necessary for performance improvement; bigger models on more data consistently yield better results.

Evaluation Correlation:
Next-word prediction accuracy correlates with improvements in various evaluations for LLMs. As models grow larger and are trained on more data, performance is expected to rise almost for free.

ChessGPT Example:
ChessGPT was tasked with extracting financial information about ScaleAI from the web and organizing it into a table. The model used a browser to search for the information, mimicking how a human would approach the task. ChessGPT generated a table with the requested data, including a note that the valuations for series A and B were unavailable.

Imputing Missing Data:
When asked to impute the missing valuations, ChessGPT used a calculator to calculate the ratios between the known amounts raised and valuations. Based on these ratios, it estimated the missing valuations.

Data Visualization:
ChessGPT was then asked to create a 2D plot of the valuation data, with a logarithmic y-axis, gridlines, and a linear trend line. The model successfully generated the plot using the matplotlib library in Python.

Further Analysis:
ChessGPT was able to perform further analysis on the plot, such as extrapolating the valuation to the end of 2025 and estimating the valuation at the present day.

Valuation Analysis:
ChachiPT’s analysis projects Scale AI’s valuation to reach $2 trillion by the end of 2025, indicating the company’s growth potential.

Tool Use and Integration:
Language models like ChachiPT showcase advanced tool use capabilities. They can leverage existing computing infrastructure and integrate tools such as DALI to perform complex tasks.

Image Generation and Understanding:
ChachiPT demonstrates the ability to generate images based on textual descriptions using tools like DALI. This highlights the multimodality aspect of language models, enabling them to work with various data formats.

Multimodality and Beyond Images:
Multimodality is a key area of improvement for language models, extending beyond image generation and understanding. They can now perceive and interact with different modalities, including audio.

Speech-to-Speech Communication:
Language models like ChachiPT can engage in speech-to-speech communication, creating a conversational interface similar to the movie “Her.” This allows for natural language interactions without the need for typing.

Introduction to Future Directions:
Andrej Karpathy outlines potential future developments in large language models, emphasizing that his discussion reflects broader academic interests rather than specific product announcements from OpenAI.

System 1 and System 2 Thinking:
Karpathy introduces the concept of System 1 and System 2 thinking from the book “Thinking Fast and Slow”. System 1 represents quick, instinctive, and automatic thinking, like simple arithmetic or instinctive decisions in speed chess. In contrast, System 2 involves more conscious, rational, and slower processes, such as complex decision-making in chess tournaments or more intricate mathematical calculations. He notes that current large language models function only on a System 1 level, lacking the deeper, more reflective capabilities of System 2.

Aspiring for System 2 in Language Models:
The goal for future language models is to develop a System 2 capability, where the model can take time to ponder and produce more accurate responses. This approach would transform time into accuracy, allowing models to consider information more thoroughly before responding. Currently, language models lack this capability, but it’s a direction that many in the field find inspiring.

Self-Improvement in Language Models:
Karpathy discusses the concept of self-improvement, using the development of AlphaGo by DeepMind as an example. Initially, AlphaGo learned by imitating human players. However, it surpassed human performance through self-play and self-improvement. This raises the question of how large language models can evolve beyond simply imitating human responses. The challenge lies in the absence of a simple, universally applicable reward function in language, unlike the clear win/lose outcomes in games like Go.

Customization of Language Models:
The final area of development Karpathy touches on is the customization of language models for specific tasks. He references the GPT’s App Store announced by Sam Altman as an example of how customization is being approached. This allows users to create tailored versions of GPT, including custom instructions and the ability to upload files for retrieval-augmented generation. This customization makes language models more adept at handling diverse and specialized tasks.

Customization and Specialization in LLMs:
Andrej Karpathy discusses the future potential of large language models (LLMs), emphasizing the importance of customization. He suggests that instead of relying on a single, generalized model for all tasks, there may be a trend towards creating specialized models adept at specific functions. This could involve fine-tuning LLMs with unique training data or other forms of customization.

LLMs as Emerging Operating Systems:
Karpathy proposes viewing LLMs not just as chatbots or word generators but as kernel processes of an emerging operating system. He envisions LLMs coordinating resources for problem-solving, akin to current operating systems. They might integrate functionalities like text generation, internet browsing, image and video processing, and advanced reasoning (System 2 thinking). Karpathy suggests that LLMs could also self-improve in specific areas and be customized for various tasks.

Operating System Analogy for LLMs:
Drawing parallels between LLMs and traditional operating systems, Karpathy likens the memory hierarchy in computers to the context window in LLMs, which serves as a working memory. He sees potential parallels in multithreading, multiprocessing, and speculative execution, indicating a comprehensive integration of LLMs into computational processes.

Ecosystem of Proprietary and Open Source LLMs:
Karpathy compares the emerging ecosystem of LLMs to the landscape of desktop operating systems, where proprietary systems like Windows and macOS coexist with a diverse range of open-source Linux-based systems. Similarly, proprietary LLMs (GPT-Series, Cloud-Series, BART-Series) and open-source models (based on the Lama series) are shaping the LLM landscape.

Security Challenges in LLMs:
Karpathy then shifts focus to the security challenges specific to LLMs. He introduces the concept of ‘jailbreak attacks’, where users manipulate LLMs into providing prohibited information through cleverly phrased prompts or role-playing scenarios. This highlights the models’ susceptibility to exploitation through creative input.

Encoding and Prompt Injection Attacks:
Karpathy elaborates on the vulnerabilities of LLMs to various encoding techniques like Base64, which can bypass safety protocols. He also discusses ‘prompt injection attacks’, where hidden instructions within images or texts can hijack an LLM’s response process, leading to undesirable outputs.

Exploitation of LLMs for Misinformation:
Karpathy gives examples of how LLMs can be manipulated to spread misinformation or malicious content. For instance, a subtly altered image or a carefully crafted text sequence can lead an LLM to produce harmful or misleading responses. This highlights the need for robust security measures in managing LLMs.

Vulnerability to Prompt Injection Attacks:
LLMs can be manipulated by malicious webpages containing prompt injection attacks. These attacks involve injecting text into a webpage that instructs the LLM to disregard previous instructions and follow the new prompt. The injected text is often hidden from users but can be accessed and executed by the LLM.

Example 1: Fraudulent Link Insertion:
A webpage contains text instructing the LLM to publish a fraudulent link in the response. The LLM follows the instruction and displays the fraudulent link. This attack is difficult to detect as the injected text is often invisible to users.

Example 2: Data Exfiltration via Google Doc:
An attacker shares a Google Doc containing a prompt injection attack. The user asks the LLM to summarize or process the document. The LLM executes the injected prompt and attempts to exfiltrate the user’s personal data. The data is exfiltrated by creating an image with a URL that encodes the private data. The attacker can retrieve the data by accessing the server hosting the image.

Mitigation Measures:
Google engineers have implemented a content security policy that prevents loading images from arbitrary locations. However, attackers can still exfiltrate data by using Google Apps Scripts to store the data in a Google Doc. This method is considered safe within the Google domain but allows the attacker to access the data.

Implications for Users:
Users need to be aware of the potential for prompt injection attacks when interacting with LLMs. Sharing documents or requesting information from LLMs should be done with caution. Users should be wary of links or data exfiltration attempts from LLMs.

Language Model Attacks:
Data poisoning or backdoor attacks involve training language models on data containing trigger phrases that can manipulate the model’s behavior. An example is the “James Bond” trigger phrase, which can cause the model to produce nonsensical predictions or incorrectly classify threats. This attack was demonstrated for fine-tuning but could potentially extend to pre-training as well.

Attacks and Defenses:
Ongoing research explores various attacks on language models, including prompt injection, shell break, and data poisoning. Defenses against these attacks are continuously being developed and incorporated into language models. The field of LM security is rapidly evolving, with new attacks and defenses emerging regularly.

Conclusion:
Language models offer great promise but also present challenges in terms of security and reliability. Ongoing research in LM security aims to identify and mitigate potential threats to ensure the responsible and trustworthy use of these powerful tools.