Lean Language Model

A Lean Language Model is a resource-efficient language model that prioritizes computational efficiency and model compactness for resource-constrained deployment environments.

• AKA: Compact LLM, Small Language Model, Tiny Model, Efficient LM, Lightweight LLM, Mobile Language Model.
• Context:
  • It can typically operate with limited memory resources through parameter reduction techniques.
  • It can typically integrate Retrieval-Augmented Generation Techniques for knowledge augmentation.
  • It can often support Edge Computing Deployments with reduced latency requirements.
  • It can often enable Mobile Device Applications through model compression techniques.
  • It can utilize Knowledge Distillation Algorithms from large teacher models.
  • It can employ Quantization Techniques for parameter bit reduction.
  • It can implement Pruning Algorithms for sparse network architectures.
  • It can range from being a Micro Lean Language Model to being a Standard Lean Language Model, depending on its parameter count.
  • It can range from being a Task-Specific Lean Language Model to being a General-Purpose Lean Language Model, depending on its application scope.
  • It can range from being a Fixed Lean Language Model to being a Adaptive Lean Language Model, depending on its runtime flexibility.
  • It can range from being a Standalone Lean Language Model to being a Retrieval-Enhanced Lean Language Model, depending on its knowledge access strategy.
  • It can range from being a Distilled Lean Language Model to being a Trained-from-Scratch Lean Language Model, depending on its training approach.
  • ...
• Example(s):
  • DistilBERT Model, with 66M parameters (40% size reduction).
  • TinyBERT Model, with 14.5M parameters.
  • MobileBERT Model, optimized for mobile devices.
  • ALBERT Model, using parameter sharing techniques.
  • SqueezeeBERT Model, using grouped convolutions.
  • Phi-2 Model, Microsoft's 2.7B parameter model.
  • Gemma Model, Google's efficient models (2B and 7B).
  • ...
• Counter-Example(s):
  • Large Language Model, such as GPT-4 with hundreds of billions of parameters.
  • Full-Scale Transformer Model, requiring extensive computational resources.
  • Dense Neural Language Model, without efficiency optimizations.
  • Cloud-Only Language Model, requiring server infrastructure.
• See: Language Model, Model Compression Technique, Knowledge Distillation, Quantization Algorithm, Edge Computing System, Retrieval-Augmented Generation Technique, Parameter-Efficient Fine-Tuning, Mobile AI System, Resource-Constrained Computing, Domain-Specific Question Answering Task, Retrieval-Augmented Reasoning Task.

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