Modern large language models (LLMs) driven by scaling laws achieve emergent intelligence in large model sizes. Recently, the increasing concerns about cloud costs, latency and privacy make it an urgent requirement to develop compact edge language models. Distinguished from direct pretraining that bounded by parameter scaling law, this work proposes the unified pruning-aware pretraining, focusing on pretraining compact models while preserving performance of much larger source models, termed EfficientLLM. It features following characteristics: 1) Pruning in Pretraining Corpus: we introduce minimal parameter groups to decouple LLMs and continuously optimize model architecture with classic pruning methods like LLM-Pruner and SparseGPT during pretraining. We reveal that it achieves top-quality compact language models to scale up LLM pruning to large scale pretraining. 2) Auto-Designed Architecture: the LLM architecture is auto-designed during saliency-driven pruning, unifying pretraining, architectural design, and parameter pruning into a single process. Based on these, EfficientLLM significantly outperforms directly pretrained baselines with 100M ∼ 1B parameters, such as MobileLLM, SmolLM, Qwen2.5-0.5B, OLMo-1B, Llama3.2-1B in commen sense benchmarks, which bridges the performance gap between traditional LLM compression and direct pretraining. We open source on https://github.com/Xingrun-Xing2/EfficientLLM.

Due to the large number of parameters, the inference phase of Large Language Models (LLMs) is resource-intensive. Unlike traditional model compression, which needs retraining, recent dynamic computation methods show that not all components are required for inference, enabling a training-free pipeline.In this paper, we focus on the dynamic depth of LLM generation. A token-position aware layer skipping framework is proposed to save 1.5x times operations efficiently while maintaining performance.We first observed that tokens predicted later have lower perplexity and thus require less computation. Then, we propose a training-free algorithm called Position-Aware Depth Decay Decoding (), which leverages a power-law decay function, ⌊ L × (𝛼ⁱ) ⌋, to determine the number of layers to retain when generating token T_i. Remarkably, without any retraining, the achieves success across a wide range of generation tasks for the first time.Experiments on large language models (the Llama) with 7 ∼ 70 billion parameters show that can achieve an average 1.5x speedup compared with the full-inference pipeline while maintaining comparable performance with nearly no performance drop (<1%) on the GSM8K and BBH benchmarks.

The pre-trained language models are continually fine-tuned to better support downstream applications. However, this operation may result in significant performance degeneration on general tasks beyond the targeted domain. To overcome this problem, we propose LM-Cocktail which enables the fine-tuned model to stay resilient in general perspectives. Our method is conducted in the form of model merging, where the fine-tuned language model is merged with the pre-trained base model or the peer models from other domains through weighted average. Despite simplicity, LM-Cocktail is surprisingly effective: the resulted model is able to achieve a strong empirical performance in the whole scope of general tasks while preserving a superior capacity in its targeted domain.

Supervised fine-tuning (SFT) is crucial for adapting Large Language Models (LLMs) to specific tasks. In this work, we demonstrate that the order of training data can lead to significant training imbalances, potentially resulting in performance degradation. Consequently, we propose to mitigate this imbalance by merging SFT models fine-tuned with different data orders, thereby enhancing the overall effectiveness of SFT. Additionally, we introduce a novel technique, “parameter-selection merging,” which outperforms traditional weighted-average methods on five datasets. Further, through analysis and ablation studies, we validate the effectiveness of our method and identify the sources of performance improvements.