Inheritune: Training Smaller Yet More Attentive Language Models

• URL: http://arxiv.org/abs/2404.08634v2
• Date: Fri, 04 Oct 2024 05:14:48 GMT
• Title: Inheritune: Training Smaller Yet More Attentive Language Models
• Authors: Sunny Sanyal, Ravid Shwartz-Ziv, Alexandros G. Dimakis, Sujay Sanghavi,
• Abstract summary: Inheritune is a simple yet effective training recipe for developing smaller, high-performing language models. We demonstrate that Inheritune enables the training of various sizes of GPT-2 models on datasets like OpenWebText-9B and FineWeb_edu.
• Score: 61.363259848264725
• License:
• Abstract: Large Language Models (LLMs) have achieved remarkable performance across various natural language processing tasks, primarily due to the transformer architecture and its self-attention mechanism. However, we observe that in standard decoder-style LLMs, attention matrices degenerate to single-column for deeper layers. Layers in this state are unable to learn anything meaningful and mostly redundant; we refer to these as lazy layers. The goal of this paper is to train smaller models by eliminating this structural inefficiency without compromising performance. Motivated by this observation, we propose Inheritune, a simple yet effective training recipe for developing smaller, high-performing language models. Smaller models trained with Inheritune, inherit early transformer layers from a larger pre-trained model, then retrain and progressively expand until they match or exceed the performance of the larger model. We demonstrate that Inheritune enables the training of various sizes of GPT-2 models on datasets like OpenWebText-9B and FineWeb_edu. Models trained with Inheritune, despite having significantly fewer layers, match or even surpass the performance of their larger counterparts. For instance, our 16-layer GPT-2 medium variant achieves comparable performance to the standard 24-layer GPT-2 medium model. Code is available at https://github.com/sanyalsunny111/LLM-Inheritune.

Related papers

• Evolving Subnetwork Training for Large Language Models [19.54861230097017]
  We propose a novel training paradigm: Evolving Subnetwork Training (EST) EST samplesworks from the layers of the large language model and from commonly used modules within each layer. We apply EST to train GPT2 model and TinyLlama model, resulting in 26.7% FLOPs saving for GPT2 and 25.0% for TinyLlama without an increase in loss on the pre-training dataset.
  arXiv  Detail & Related papers  (2024-06-11T05:44:56Z)
• Sheared LLaMA: Accelerating Language Model Pre-training via Structured Pruning [52.29522018586365]
  We study structured pruning as an effective means to develop smaller LLMs from pre-trained, larger models. Our approach employs two key techniques: (1) targeted structured pruning, which prunes a larger model to a specified target shape by removing layers, heads, and intermediate and hidden dimensions in an end-to-end manner, and (2) dynamic batch loading, which dynamically updates the composition of sampled data in each training batch based on varying losses across different domains.
  arXiv  Detail & Related papers  (2023-10-10T15:13:30Z)
• Enhancing Cross-Category Learning in Recommendation Systems with Multi-Layer Embedding Training [2.4862527485819186]
  Multi-layer embeddings training (MLET) trains embeddings using factorization of the embedding layer, with an inner dimension higher than the target embedding dimension. MLET consistently produces better models, especially for rare items. At constant model quality, MLET allows embedding dimension, and model size, reduction by up to 16x, and 5.8x on average.
  arXiv  Detail & Related papers  (2023-09-27T09:32:10Z)
• Masked Image Modeling with Local Multi-Scale Reconstruction [54.91442074100597]
  Masked Image Modeling (MIM) achieves outstanding success in self-supervised representation learning. Existing MIM models conduct reconstruction task only at the top layer of encoder. We design local multi-scale reconstruction, where the lower and upper layers reconstruct fine-scale and coarse-scale supervision signals respectively.
  arXiv  Detail & Related papers  (2023-03-09T13:42:04Z)
• Training Trajectories of Language Models Across Scales [99.38721327771208]
  Scaling up language models has led to unprecedented performance gains. How do language models of different sizes learn during pre-training? Why do larger language models demonstrate more desirable behaviors?
  arXiv  Detail & Related papers  (2022-12-19T19:16:29Z)
• LV-BERT: Exploiting Layer Variety for BERT [85.27287501885807]
  We introduce convolution into the layer type set, which is experimentally found beneficial to pre-trained models. We then adopt an evolutionary algorithm guided by pre-training accuracy to find the optimal architecture. LV-BERT model obtained by our method outperforms BERT and its variants on various downstream tasks.
  arXiv  Detail & Related papers  (2021-06-22T13:20:14Z)
• Training with Multi-Layer Embeddings for Model Reduction [0.9046327456472286]
  We introduce a multi-layer embedding training architecture that trains embeddings via a sequence of linear layers. We show that it allows reducing d by 4-8X, with a corresponding improvement in memory footprint, at given model accuracy.
  arXiv  Detail & Related papers  (2020-06-10T02:47:40Z)
• Train Large, Then Compress: Rethinking Model Size for Efficient Training and Inference of Transformers [94.43313684188819]
  We study the impact of model size in this setting, focusing on Transformer models for NLP tasks that are limited by compute. We first show that even though smaller Transformer models execute faster per iteration, wider and deeper models converge in significantly fewer steps. This leads to an apparent trade-off between the training efficiency of large Transformer models and the inference efficiency of small Transformer models.
  arXiv  Detail & Related papers  (2020-02-26T21:17:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.