1. Introduction

The articles ‘Codia AI: Shaping the Design and Code Revolution of 2024‘ and ‘Codia AI: Shaping the Design and Code Revolution of 2024 – Part 2‘ introduce Codia AI, which possesses its own AI models.

In the field of artificial intelligence, the training and optimization of large language models are among the key challenges. This article outlines the critical steps for large language models: pre-training, distributed training, fine-tuning, and model comparison, providing a comprehensive perspective on building and optimizing models.

2. Pre-training

Pre-training is the first phase of the training process for large language models, aimed at enabling the model to learn general knowledge and structure of language.

2.1. Data Preparation

Before pre-training begins, a large amount of data needs to be collected. This data should cover a wide range of topics and domains so that the model can learn diverse language expressions and knowledge. Data typically comes from public datasets, web crawls, books, papers, code, etc.

2.2. Model Architecture

Large language models are usually based on the Transformer architecture, a deep learning model that relies on self-attention mechanisms. The Transformer model contains multiple encoder and decoder layers, each with self-attention units and feed-forward neural networks.

The self-attention mechanism allows the model to consider all other words in a sentence when processing a word, capturing the relationships between words. This mechanism makes the Transformer model particularly suited for processing sequential data, such as text.

2.3. Pre-training Tasks

Pre-training tasks are designed to help the model learn language patterns. Common pre-training tasks include:

• Masked Language Model (MLM): Randomly masking some words in the input text (e.g., replacing them with a special [MASK] token) and then predicting these masked words. This task helps the model learn the context of vocabulary.
• Next Sentence Prediction (NSP): Given two sentences, the model needs to determine if the second sentence is the logical follow-up of the first. This helps the model understand the relationship between sentences.
• Causal Language Model (CLM): Predicting the next word based on the preceding ones, emphasizing the importance of word order.

2.4. Training Process

During pre-training, the model learns through numerous iterations. In each iteration, the model attempts to complete pre-training tasks and adjusts its internal parameters based on the task’s loss function. This process requires significant computational resources and time, usually performed on GPU or TPU clusters.

3. Distributed Training

3.1. Data Parallelism

Data parallelism is the most straightforward form of distributed training. It involves splitting the training dataset into multiple small batches and sending these batches to multiple computing devices in parallel. Each device has a copy of the model and independently computes gradients for its batch. The gradients are then aggregated (usually by accumulation or averaging) and updated across all model copies. This method can significantly reduce training time but increases communication overhead as the model size grows.

3.2. Model Parallelism

Model parallelism addresses the issue where large model parameters cannot fit into the memory of a single computing device. In model parallelism, different parts of the model are distributed across different devices. Each device is responsible for computing a portion of the model, and communication is required across devices during forward and backward propagation. Model parallelism can effectively handle large models, but the challenge lies in efficiently splitting the model and managing cross-device communication.

3.3. Pipeline Parallelism

Pipeline parallelism is a method that combines model and data parallelism to further improve the efficiency of training large-scale models. In pipeline parallelism, the model is divided into several parts (stages), and each stage is assigned to different computing devices. Unlike traditional model parallelism, pipeline parallelism allows for processing multiple batches of data simultaneously, passing the result to the next stage once a stage completes its part. This method can reduce idle time between devices and improve resource utilization.

3.4. Zero Redundancy Optimizer (ZeRO)

Zero Redundancy Optimization (ZeRO) is an optimized data parallel technique aimed at reducing memory redundancy in data parallelism. ZeRO employs innovative memory management and communication strategies, effectively reducing the amount of gradients, optimizer states, and parameters each device needs to store. This makes training larger models possible while reducing communication overhead and improving scalability and efficiency.

3.5. Heterogeneous Training

Heterogeneous training involves distributed training across different types of computing resources, such as using both GPUs and CPUs. This method optimizes training efficiency and cost by intelligently assigning different computational tasks to the most suitable resources.

3.6. Mixed Precision Training

Mixed precision training uses different precisions of floating-point numbers (e.g., FP32 and FP16) to accelerate the training process of models while minimizing the impact on model accuracy. Using lower precision floating-point numbers can reduce memory usage and speed up mathematical operations, especially on GPUs that support tensor cores. Mixed precision training is often combined with Automatic Loss Scaling (ALS) to avoid gradient vanishing issues in low

precision computations.

4. Fine-tuning

After pre-training, the model already possesses certain language understanding abilities. To perform better on specific tasks, fine-tuning is conducted.

4.1. Task-specific Data

Fine-tuning requires a dataset specific to the task at hand. For example, if the task is sentiment analysis, then the dataset should contain texts and their corresponding sentiment labels (e.g., positive or negative).

4.2. Fine-tuning Strategies

During the fine-tuning stage, task-specific layers, such as a fully connected layer for classification, are usually added on top of the pre-trained model. It is also possible to freeze certain layers from the pre-training stage and only fine-tune the top layers or all layers.

4.3. Fine-tuning Process

In the fine-tuning process, the model is trained on the task-specific dataset. This process is generally much shorter than pre-training because the model already has basic language capabilities and only needs to be optimized for the specific task.

4.4. Evaluation and Tuning

After fine-tuning, the model needs to be evaluated on a validation set to ensure its performance meets requirements. Based on the evaluation results, it may be necessary to adjust hyperparameters, such as learning rate and batch size, to achieve the best results.

5. Fine-tuning Techniques

5.1. Prompt Tuning

Prompt Tuning is a fine-tuning technique that involves adding specific prompts to the input to guide the model to generate specific outputs. This method does not require adjusting the main parameters of the model but instead optimizes a small number of prompt words for fine-tuning.

5.2. Prefix Tuning

Prefix Tuning adds a series of learnable parameters to the model’s input prefix, which are optimized during training. This allows for adjusting the model’s behavior to suit specific tasks without changing the model’s body.

5.3. Adapter Tuning

Adapter Tuning is a fine-tuning technique that inserts small trainable modules into each Transformer layer of the model. Only the parameters of these modules are updated during fine-tuning, while the rest of the model remains unchanged.

5.4. LoRA

LoRA (Low-Rank Adaptation) is an efficient technique for fine-tuning the parameters of large pre-trained models. The core idea of LoRA is to introduce additional low-rank matrices into the model’s attention and feed-forward networks to adjust the model’s behavior without directly modifying the original pre-trained parameters. This method significantly reduces the number of parameters that need to be updated during fine-tuning while maintaining or even improving the model’s performance on downstream tasks.

6. Comparison of Large Language Models

6.1. Tokenizer

The tokenizer is a tool that converts raw text into numerical sequences that the model can understand. Different models may use different tokenizer strategies.

• LLaMA: Uses Byte-Pair Encoding (BPE) as its tokenizer, effectively handling unknown vocabulary and out-of-vocabulary words.
• GPT: Also utilizes Byte-Pair Encoding (BPE), effectively managing unknown vocabulary and complex language structures.

6.2. Position Encoding

Position encoding is a mechanism used to inform the model of the position of words in a sentence.

• LLaMA: Rotational Position Encoding (RoPE).
• GPT: Trainable position encoding, which can be adjusted based on training data.

6.3. Activation Function

The activation function is a key component of neural networks for nonlinear transformations.

• LLaMA: Uses SwiGLU as its activation function.
• GPT: Uses GELU as its activation function. GELU, compared to ReLU and other activation functions, better balances linear and nonlinear characteristics in deep networks, improving model expressiveness.

7. Conclusion

This article discussed the entire process of training and optimizing large language models, from data preparation and model architecture to the design of pre-training tasks. We also explored strategies for distributed training, including data parallelism, model parallelism, pipeline parallelism, and fine-tuning stages and techniques, such as Prompt Tuning and Adapter Tuning. Additionally, by comparing the tokenizer, position encoding, and activation functions of different models, we revealed their unique features.”