In the first part of this guide, we'll focus on the essential step of preprocessing your data for training a Language Learning Model (LLM) in Python. Preprocessing involves transforming raw text data into a suitable format, including cleaning, tokenization, normalization, and feature extraction. Follow along as we explore practical techniques to prepare your data effectively for LLM training, setting the foundation for subsequent parts of the series.

How to Train Your Own Large Language Models

In this blog post, we will explore the process of training your own large language models (LLMs) based on a recent blog post. Training your own LLMs can offer customization and address specific use cases that general-purpose models may not suit. We’ll discuss the reasons for training your own LLMs and delve into various stages of the process, including data pipelines, model training and evaluation, deployment, and lessons learned.

The full story is in the great The Full Stack website and it’s magnificent LLM Bootcamp

The youtube video is here.

Should You Train Your Own LLMs?

Training your own LLMs can be beneficial for several reasons. One key advantage is customization, allowing you to tailor models for specific tasks such as code completion. General-purpose models may not excel in certain use cases like code completion for specific programming languages. Another reason is reducing dependency on a limited number of tech firms, providing access to language models for developers and companies beyond a handful of providers. Cost efficiency is also crucial, as hosting models can be expensive, and training more cost-effective models enables wider accessibility. Additionally, data privacy, security, and ownership of intellectual property are important considerations.

The Ghostwriter Code Completion Model

We will focus on training The Ghostwriter code completion model, which competes with popular models like Co-Pilot. Ghostwriter excels in code completion, predicting the next token based on code context. It is particularly useful for code-related tasks and offers multi-line completion capabilities.

The Tech Stack

For training LLMs, we utilize a modern tech stack. Data pipelines, including pre-processing, analytics, and transformations, are managed using Databricks. Hugging Face, a widely used resource, provides data sets, pre-trained models, tokenizers, and inference tools. Mosaic ML, known as the “DataBricks of GPUs,” offers scalable GPU infrastructure and pre-configured LLM configurations for efficient model training.

The process involves three stages: data processing, model training, and deployment. Data processing encompasses pre-processing and analysis, while model training leverages Mosaic ML’s capabilities for GPU clustering and distributed training. Finally, deployment involves hosting the model, optimizing inference speed, evaluating the model, and making it accessible to users.

Data Pipelines: Pre-processing and Analysis

Data processing is a critical step in training LLMs. We start with a large corpus of permissively licensed code data from the GitHub archive. Hugging Face provides a deduplicated version of this dataset, available in Parquet or dataset format. However, to gain more control over the data, we move it to Databricks, which allows scalable data processing from various sources, including proprietary data.

Pre-processing steps include anonymizing the data by removing emails, IP addresses, and secret keys. Auto-generated code is also eliminated using heuristics and regular expressions. Additionally, we filter out code that doesn’t compile or is unparsable, aiming to remove bugs from the training data. Filters based on line length, percentage of alphanumeric characters, and other criteria help improve the quality of the dataset.

Analyzing and Enhancing the Dataset

Analyzing the dataset is crucial to understanding its contents and ensuring high-quality training data. Diving into the data helps identify bugs, duplicates, or unwanted coding styles. An example transformation involves removing auto-generated code, which introduces duplicates and reinforces undesired coding patterns. Exploring the data allows us to take necessary steps, such as filtering out Python 2 code or addressing specific language-related issues.

Pre-Processing Steps and Data Quality

Several pre-processing steps contribute to data quality. Anonymization ensures sensitive information is removed or replaced, and filters based on code validity, line length, and other factors help refine the dataset. Although it’s challenging to eliminate all bugs or low-quality code, efforts can be made to enhance data quality.

Model Training

Once the data preprocessing and analysis stages are complete, we move on to the model training phase. This phase involves configuring the LLM, setting up the training environment, and initiating the training process.

Model Configuration

The Ghostwriter code completion model is based on transformer architectures, which have proven to be effective for natural language processing tasks. Hugging Face provides pre-trained transformer models that serve as a starting point. These models can be fine-tuned on the code completion task using transfer learning.

The model configuration involves specifying parameters such as the number of layers, hidden size, attention heads, and sequence length. These parameters impact the model’s capacity, training time, and performance. It’s important to strike a balance between a model that is powerful enough to capture complex patterns in code and a model that is manageable in terms of computational resources and training time.

Training Environment

To train large language models efficiently, it’s crucial to have a powerful computing infrastructure. Mosaic ML offers GPU clustering, which enables distributed training across multiple GPUs. This significantly reduces training time and allows for larger batch sizes, leading to improved convergence and model quality.

The training environment should also include monitoring and logging mechanisms to track the training progress, performance metrics, and potential issues. It’s important to monitor metrics such as training loss, validation loss, and perplexity to ensure the model is converging and making progress.

Training Process

The training process typically involves iterations of forward and backward passes, where the model predicts the next token given the input context and then adjusts its parameters based on the prediction error. The process continues for multiple epochs until the model converges.

During training, it’s common to use techniques such as gradient clipping, learning rate schedules, and early stopping to improve training stability and prevent overfitting. Regularization techniques like dropout can also be applied to prevent the model from relying too heavily on specific tokens or patterns in the training data.

It’s important to allocate sufficient time and computational resources for training. Training large language models can be computationally intensive and may require days or weeks depending on the model size and available resources.

Model Evaluation and Deployment

Once the training is complete, the next step is to evaluate the model and deploy it for practical use. This involves assessing the model’s performance, optimizing its inference speed, and making it accessible to users.

Model Evaluation

Model evaluation is crucial to ensure the model’s quality and effectiveness. Common evaluation metrics for language models include perplexity, which measures how well the model predicts the next token, and accuracy, which evaluates the correctness of the predicted tokens. Evaluation can be performed on held-out validation data or through human evaluation by experts in the specific domain.

It’s important to iterate on the model based on evaluation results and fine-tune it further if necessary. Continuous evaluation and improvement help maintain the model’s accuracy and relevance over time.

Optimization and Deployment

Optimizing the model’s inference speed is essential for real-time applications. Techniques such as model quantization, model distillation, and hardware acceleration can be employed to speed up inference without significant loss of accuracy.

Deployment of the model involves hosting it on a server or cloud infrastructure that can handle user requests. API endpoints or SDKs can be provided to enable developers to integrate the model into their applications easily. It’s important to ensure proper documentation, versioning, and scalability of the deployment infrastructure.

Lessons Learned and Future Work

Training large language models is a complex process that requires careful consideration of various factors. Here are some key lessons learned and areas for future work:

1. Data quality is crucial: Invest time in data preprocessing and analysis to ensure high-quality training data.
2. Iterative training and evaluation: Continuously evaluate the model’s performance and iterate on the training process to improve its quality.
3. Resource management: Optimize the training environment to utilize available computational resources effectively.
4. Regularization and fine-tuning: Experiment with different regularization techniques and fine-tuning strategies to improve model generalization.
5. User feedback and adaptation: Gather user feedback to identify model limitations and improve its performance over time.
6. Ethical considerations: Consider potential biases in the training data and take measures to address them, ensuring the model is fair and unbiased.

In the future, advancements in large language models could include more efficient training algorithms, better transfer learning techniques, and improved ways to mitigate biases in the models. Continued research and development will further enhance the capabilities and applications of code completion models like Ghostwriter.