What is the Big Language Model (LLM), How To Be Educated?

Every day we chat with ChatGPT, Claude or open source Llama models. We print code, summarize complex texts, sometimes we discuss a technical or philosophical topic that we only wonder. From the out of the outside the screen stands as there is a consciousness that thinks like human at the other end of the screen, understand what it says. Yet there is no magic behind the curtain; there are tremendous amount of data, huge processing power and statistical probability accounts. So, what is the big language model that changes technology world in the last few years and how are these systems trained from scratch?

What is LLM? Is it More Than A Sentle Completion Machine?

English Angle Large Language Model What is LLM, the most lean response to the question: The images between text-based data are deep learning models designed to predict the next word by learning grammar rules and conceptual relationships.

Think about the automatic completion feature on your phone’s keyboard. When you write "Semin ofise..." you have the proposal "I'm going". LLMs are huge versions that feed this logic with petabaytlarca internet data and process billions of rules simultaneously. The main difference between is the ability to build scale and context.

The foundation of this transformation was published in 2017 and replaced natural language processing (NLP) root Need All You Need based on the article. The researchers developed the "Transformer" architecture that reads the context of all sentences at the same time instead of processing words in order. In this way, a model has become able to follow the bond between the next zamir with a selfie at the beginning of the text. Today at the point of the bride, for professionals who want to get competitive advantage in the software world dominate LLM technologies Not only a plus, it turned into a basic engineering requirement.

Basic Structure Stones: What is Token and Parameter What to Express?

To understand how a LLM works, it is necessary to know how models perceive the world. Computers do not directly read letters or words like us; they work numbers.

What is Token?

Since computers can not work directly, texts are divided into the smallest meaningful parts called "token" first. The practical answer of the question of what is the token; sometimes a single word, sometimes a hece or word root is sometimes a sign of pointing.

For example, a text of 1000 words in English corresponds to an average of 1300–1400 tokens. Words like Turkish can be divided into several different tokens as a single word receives more than one additional (such as book-lar-from us). The training cost of a model is calculated directly through this token numbers directly from the API fee and context window (context window).

What is Parameter?

The words like "8B", "70B" or "405B" that we are talking about models indicate the size of the model. When we call the parameter, we can think about the digital contrast of synaps between neurons in the human brain.

Parameters are weights (weights) and deviations optimized during training of the artificial nervous network (biases). These weights are set and meaningal relationships between words are stored in these numbers. The ability to abstract the model as the number of parameters increases, logic execution and solve complex problems is often increased; but the hardware cost and delay time needed in parallel is also folded.

Step: How to Fix a Language Model?

A large language model requires intensive data engineering and high processing power that takes months to get from scratch and live. We can summarize the language model in 4 main stages:

Basic Model (Base Model) learned language structure and general world knowledge

Thousands of GPU sets, unsupervised (unsupervised) learning

3. Supervised Fine-Tuning - SFT

Gaining dialogue format to model with questions-cevap and instruction data sets

assistant model that responds directly and logically to questions asked

Quality dialogue data sets, audited learning (supervised)

4. Alignment (Alignment - RLHF / DPO)

Optimization of responses to human preferences and safety rules

Harmless, honest and unbiased end user model (Instruct/Chat Model)

Learn to consolidate human feedback (RLHF)

1. Data Acquisition and Cleaning (Data Preprocessing)

Everything starts with browsing the internet: websites are collected by scanning open source code stores, academic articles, books and forums. However, internet data can not be used directly; it is necessary to clean seriously:

• Again (duplicate) content is extracted.

• HTML tags and unidentified character arrays are cleaned.

• The parts that contain toxic, hate phrase and contain personal data are filtered.

The quality of training data directly determines the success of the model. The rule of "if you enter" in the world of data science is also valid in major language models.

2. 2. Preliminary Training (Pre-Training)

The most expensive, longest and energy consumption of the process is the highest stage. Thousands of high-performance GPU weeks, sometimes work without interval.

At this stage, the model is trained as uncontrolled (unsupervised). The model is given word text in trilyons; some words in the text are hidden and asked to predict the next word from the model. The model makes mistakes for billion times, updates the parameters weight with the resemblance (backpropagation) and learns grammar, sentence flow and general world knowledge over time. There is a "Basic Model" (Base Model) that does not act as an assistant yet when the preliminary training is finished.

3. Supervised Fine-Tuning - SFT

The basic model language is not a good assistant. In order to answer the question when you ask him "Where is the capital of Germany?" he can continue his question. Because it is only to extend the text you know.

In the SFT stage, the model offers quality data sets in question-cevap format:

• User: How to reverse the list in Python?

• Assistant: list.reverse() method or [::-1] you can use slicing method.

Thanks to this instruction training, the model learns to respond directly to a question, code description or text summary.

4. Alignment (Alignment: RLHF and DPO)

Although the model now understands the instructions, it can produce misleading or offensive outputs. Here, at this point, human enters into action. Hugging Face LLM Alignment Guide RLHF detailed in sources like (© Copyright 2018. All Rights Reserved.) and DPO (Direct Preference Optimization) methods apply.

Human labeller points the safest and most accurate one by comparing the alternative responses the model produces. The model starts to provide user-friendly, unbiased and harmless responses by learning these feedbacks with the reward/ceza mechanism. On the other hand to test the reliability of these responses produced by models and the actual accuracy DeepMind SAFE system automatic control mechanisms such as commissioning and minimizing the risk of hallucination.

Customize Models and Developer World

We are not only dependent on the cloud services of giant technology companies to use large language models nowadays. With the growth of open-source ecosystem, developers can run 8-bit or 4-bit levels thanks to quantization techniques.

LoRALow-Rank Adaptation) such as parameter efficient fine adjustment (PEFT) techniques are only possible to produce special vertical models to institutions by updating a small adapter layer instead of re-training the entire huge models. For engineers who want to carry this conversion into their own projects LLM ecosystem and developer trainings Home, models APIs, open source libraries and RAG (Retrieval-Augmented Generation) It is a critical role in blending with architectures.

Large language models are not magicful consciousness; massive optimization systems that transform human written heritage into mathematical weights. Tokenization is the first step to grasp the logic of pre-training and alignment, not only using these tools, managing them and integrating projects.