Amid the daily deluge of news about new advances in large language models (LLMs), you may be wondering, “How do I train mine?” Today, an LLM tailored to your specific needs is becoming an increasingly vital asset, but its “large” scale comes at a price. The impressive success of LLMs can largely be attributed to scaling laws, which say that a model’s performance increases with its number of parameters and the size of its training data. Models like GPT-4, Llama2, and Palm2 were trained on some of the largest clusters in the world, and the resources required to train a large-scale model are often unaffordable for individuals and small businesses.
Efficient training of LLMs is an active area of research that focuses on making them faster, less memory intensive and more energy efficient. Here efficiency is defined as achieving a balance between the quality (e.g. performance) of the model and its footprint (resource utilization). This article will help you select data- or model-efficient training strategies tailored to your needs. To go deeper, the most common models and their references are illustrated in the attached diagram.
Data efficiency. Strategic data selection can significantly influence improving training efficiency. One approach is data filtering, which can be performed before training to form a core data set that contains enough information to achieve model performance comparable to that of the full set. Another method is curricular learning, which involves the systematic programming of data instances during training. This could mean starting with simpler examples and gradually moving to more complex ones, or the other way around. Furthermore, these methods can be adaptive and form a varied sampling distribution across the entire data set during training.
Model efficiency. The easiest way to obtain efficient models is to design the appropriate architecture. Of course, this is not easy at all. Fortunately, we can make the task more accessible through automated model selection methods such as neural architecture search (NAS) and hyperparameter optimization. Having the right architecture introduces efficiency by emulating the performance of large-scale models with fewer parameters. Many successful LLMs use the transformative architecture, recognized for its parallelization and multi-level sequence modeling capabilities. However, as the underlying attention mechanism scales quadratically with input size, managing long sequences becomes challenging. Innovations in this area include improving the attention mechanism with recurrent networks, long-term memory compression, and balancing local and global attention.
At the same time, parameter efficiency methods can be used to overload their utilization for multiple operations. This involves strategies such as sharing weight between similar operations to reduce memory usage, as seen in Universal or Recursive Transformers. Sparse training, which activates only a subset of parameters, takes advantage of the “lottery ticket hypothesis”: the concept that smaller, efficiently trained subnetworks can rival the full model performance.
Another key aspect is model compression, which reduces computational load and memory needs without sacrificing performance. This includes pruning less essential weights, distilling knowledge to train smaller models that replicate larger models, and quantization to improve performance. These methods not only optimize model performance but also speed up inference times, which is especially vital in real-time and mobile applications.
Training configuration. Due to the large amount of data available, two common themes emerged to make training more effective. The first step is pre-training, which is often performed in a self-supervised manner on a large unlabeled data set, using resources such as Common trace: get started for initial training. The next phase, “tuning,” involves training on task-specific data. While it is possible to pre-train a model like BERT from scratch, using an existing model like bert-large-cased · Hugging face It is usually more practical, except in specialized cases. Since the most effective models are too large for continuous training with limited resources, the focus is on efficient parameter fine-tuning (PEFT). At the forefront of PEFT are techniques such as “adapters”, which introduce additional trained layers while keeping the rest of the model fixed, and learn separate “modifier” weights for the original weights, using methods such as sparse training or low-rank adaptation ( LoRA). . Perhaps the easiest entry point for adapting models is rapid engineering. Here we leave the model as is, but we choose cues strategically so that the model generates the most optimal responses to our tasks. Recent research aims to automate that process with an additional model.
In conclusion, the efficiency of LLM training depends on intelligent strategies such as careful data selection, model architecture optimization, and innovative training techniques. These approaches democratize the use of advanced LLM, making them accessible and practical for a broader range of applications and users.
Review the Paper. All credit for this research goes to the researchers of this project. Also, don’t forget to join. our 33k+ ML SubReddit, 41k+ Facebook community, Discord channel, and Electronic newsletterwhere we share the latest news on ai research, interesting ai projects and more.
If you like our work, you’ll love our newsletter.
Michal Lisicki is a Ph.D. student at the University of Guelph and the Vector Institute for ai in Canada. His research spans multiple deep learning topics, from 3D vision for robotics and medical image analysis early in his career to Bayesian optimization and sequential decision making under uncertainty. His current research focuses on developing sequential decision-making algorithms to improve data and deep neural network model efficiency.
<!– ai CONTENT END 2 –>
