Apertus LLM Family Expanded Through Distillation and Quantization Techniques
Researchers have validated distillation and quantization as cost-effective methods to expand language model families across different sizes and hardware formats. The work produced Apertus-v1.1, a family of distilled models with up to 4 billion parameters trained on 1.7 trillion permissive license tokens. This approach addresses the practical need for language models that can run on diverse hardware and systems with varying computational constraints.
A new study demonstrates that distillation and quantization techniques can effectively expand large language model families to cover a wider range of hardware and computational constraints. Building on the open-recipe Apertus 8B model, researchers created Apertus-v1.1, which includes distilled variants with up to 4 billion parameters trained on 1.7 trillion tokens with permissive licenses. The research validates that this approach achieves both cost-efficiency and strong accuracy performance across the model family. This work addresses a growing industry trend of releasing language models in multiple sizes to accommodate different deployment scenarios, from resource-constrained edge devices to high-performance systems. The use of permissive licensing for training data suggests the models may be more freely usable for commercial and research applications.
What's missing
The paper does not provide specific benchmark comparisons against other distilled or quantized model families, detailed accuracy metrics across different downstream tasks, or information about computational cost savings relative to training full-size models from scratch. The study's own limitations regarding generalization to other base models and potential performance trade-offs in specific application domains are not detailed in the abstract.
What different sources said
- arXiv cs.LGCenter
Apertus LLM Family Expansion via Distillation and Quantization
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