Model Training - runtimerevolution/labs GitHub Wiki

Training consists of having the [LLM]] trained on a large corpus of text to predict the next [token of a sentence correctly. The goal is to adjust the model's parameters to maximize the probability of the observed data.

The 2 primary algorithms used to train LLMs:

Back-propagation is the method used to calculate the “gradient” of the loss function for the weight & biases in the neural network. It tells us the direction we need to move in order to reduce the error.

Gradient descent is an optimization algorithm used to minimize the loss function.

Typically a model is trained on a very large general dataset of texts from the Internet, such as The Pile or CommonCrawl. Sometimes also more specific datasets are used, such as the Stackoverflow Posts dataset.

The model learns to predict the next token in a sequence by adjusting its parameters to maximize the probability of outputting the correct next token from the training data.

Training the selected LLM on our preprocessed dataset typically involves using large-scale parallel processing hardware (GPUs or TPUs) due to the computational demands of training deep learning models. When training a Large Language Model (LLM), there are several approaches and techniques that can be employed, each with its own impacts and usages. Here are some examples:

1. Pre-training from Scratch - In this approach, the model is trained from scratch on a large corpus of text data without any pre-existing knowledge. The entire model architecture is initialized randomly, and the parameters are updated through backpropagation during training.

   Impacts/Usages:

   • Requires a vast amount of labeled text data for effective training.
   • Computationally expensive, as it involves training the entire model from scratch.
   • Suitable for cases where pre-trained models are not available or when the task requires domain-specific knowledge not present in existing pre-trained models.

2. Transfer Learning - Transfer learning involves pre-training a model on a large dataset for a related task and then fine-tuning it on a smaller dataset for the target task. The pre-trained model learns general language representations during pre-training, which are then adapted to the specific characteristics of the target task during fine-tuning.

   Impacts/Usages:

   • Reduces the amount of labeled data required for training, as the model leverages knowledge learned from the pre-training task.
   • Improves generalization and performance on the target task by initializing the model with pre-trained weights.
   • Allows for faster convergence during training, as the model starts with learned representations that are already useful for the target task.

3. Multi-Task Learning - Multi-task learning involves training a single model on multiple related tasks simultaneously. The model learns to jointly optimize performance across all tasks by sharing parameters and representations between them.

   Impacts/Usages:

   • Improves generalization and robustness by leveraging shared knowledge across multiple tasks.
   • Helps mitigate overfitting by regularizing the model through task-specific and shared representations.
   • Can lead to more efficient use of computational resources by training a single model for multiple tasks instead of separate models for each task.

4. Curriculum Learning - Curriculum learning involves training the model on a curriculum of tasks or data samples, starting with simpler tasks or samples and gradually increasing the difficulty over time. This approach guides the model's learning process and helps it to converge more effectively.

   Impacts/Usages:

   • Improves convergence and performance by providing a structured learning schedule that gradually exposes the model to more complex tasks or data samples.
   • Helps prevent the model from getting stuck in local optima by starting with easier optimization problems.
   • Can lead to more efficient use of computational resources by focusing training efforts on the most informative data samples or tasks early in the training process.

5. Self-Supervised Learning - Self-supervised learning involves training the model to predict certain properties or features of the input data without explicit supervision. For example, the model may be trained to predict masked words in a sentence or to generate text conditioned on a corrupted version of the input.

   Impacts/Usages:

   • Enables training on large amounts of unlabeled data, as the model generates its own supervision signals during training.
   • Helps the model learn rich and generalizable representations of the input data, which can be transferred to downstream tasks.
   • Can serve as a pre-training stage for transfer learning, providing a strong initialization for fine-tuning on supervised tasks.

Each of these training approaches has its own strengths and weaknesses, and the choice of approach depends on factors such as the availability of labeled data, computational resources, task requirements, and desired performance metrics. Experimentation and iteration are often necessary to determine the most effective training strategy for a given LLM project.