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Large Language Models (LLMs), primarily function based on the statistical relationships between words, phrases, and sentences in their training data. Statistical Patterns: LLMs are trained on vast amounts of text data and learn to predict the next word in a sentence given the previous words. This involves identifying patterns and relationships between words and sentences. They rely on the frequency and co-occurrence of words to generate contextually appropriate responses. For example, if "dog" frequently appears with "bark," the model learns this association. The main objective during training is to master the statistical properties of language.
Step 1: Plan and design the LLM
Standard model design
LLM has the ability to re-train itself, to hit the re-train button. (no human required)
LLM is constantly being re-fed its training data, told to re-work and improve the training data, with prompt engineering to choose facts and statistics. (no human required). New data is also added to a seperate directory.
The trainer is in the model. LLM re-works its training code as well to produce a better model. Developing the trainer means the LLM's ability to distinguish differences correctly, better from worse, yes from no and so on, successful compile vs errors, red from blue. Two copies and the LLM must choose which is better and update its training data.
Demonstrator must be resource light enough for the LLM to perform these tasks.
Step 2: Eval Space
Evaluation is key. At its most basic human eval, more so the tools that give the LLM the ability to test, proof and rework training data. For instance, a code compiler returns error or successful compilation, providing an evaluation of code. It has a tool to run its generated code and get an evaluation of the code such as errors and go back and work on it. Once it passes compilation, it updates the training data. Perhaps a training data compiler could do similar. Providing the model more and more tools to better rework its training data.
Synthesize new data. Factorial limits to the amount of data that can be synthesized, perhaps 8-word sentences and every combination, but again it is evaluation. Perhaps a simulation space which mimics real world physics could be a universal space for performing evaluation. The simulation space can bounce response off physics, designed to evaluate, ground and improve synthetic data by testing it. For instance, we can put enough physics together to test wing designs. The LLM would update its training data on the improved wing designs and then and then debate the design at the eyre of the user. Arguing its decision with facts and figures.
The focus shifts from the model to the trainer program. A model is only as ample as its sophisticated evaluation.
Limitations: what is the current weather? For example, the LLM cannot know this unless it was re-trained constantly, function calls are used to supplement the LLM. For example, if I want to book a flight, the LLM connects to the API system of the flight operator and automates the booking using function calling. To know the weather of the moment, the LLM function calls an authoritative server that relays the information. However, it is important to work within the system of model engineering and not revert to function calling as a fix.
Make the LLM
Get the training datasets: Sources: Common Crawl, Wikipedia, books, articles, forums, public datasets (e.g., Project Gutenberg).
Preprocess dataset: Data Preprocessing
Tokenization: Split text into tokens (words, subwords, or characters).
Normalization: Lowercase text, remove special characters, handle contractions, etc.
Filtering: Remove non-text content, duplicates, and overly long or short texts.
Encoding: Convert tokens to numerical representations using a tokenizer.
Choose architecture for your LLM. Transformer-based models (e.g., GPT, BERT), Parameters: Define model size (number of layers, heads, hidden units).
Training
Evaluation
Use GPT2 tools:
from transformers import GPT2Config, GPT2LMHeadModel, GPT2Tokenizer, Trainer, TrainingArguments, TextDataset, DataCollatorForLanguageModeling
# Replace the original data with the generated data
data[idx] = new_text
# Print the modified data
print(data)
When complete retrain the model and repeat. After the loop extract, run the model training script. Turn the model loading into a function and reload the new model and repeat endlessly. Throw new training data in the directory it uses or have two directories, an orig and rework directory.
RefinedWeb is a massive dataset, train a Large Language Model (LLM) with it.
Prerequisites:
Hardware: You'll need a powerful machine with a large GPU (e.g., NVIDIA V100 or A100) and sufficient memory (at least 16 GB).
Software: Install the following:
Python 3.8 or later
PyTorch 1.11 or later
Hugging Face Transformers library (e.g., transformers==4.12.0)
datasets library (e.g., datasets==1.18.0)
Dataset: Download the RefinedWeb dataset (600 billion tokens) from the official website or a mirror.
Procedure:
Step 1: Prepare the dataset
Unzip the RefinedWeb dataset and store it in a directory (e.g., refinedweb_data).
Train the model using the data loader and optimizer:
You may need to adjust the hyperparameters, model architecture, and training procedure based on your specific requirements. Training a large language model can take several days or even weeks.