arxiv_ml 95% Match Research Paper ML Engineers,AI Researchers,Developers working with LLMs,Researchers in model compression 3 weeks ago

BitNet Distillation

large-language-models › model-architecture

📄 Abstract

Abstract: In this paper, we present BitNet Distillation (BitDistill), a lightweight pipeline that fine-tunes off-the-shelf full-precision LLMs (e.g., Qwen) into 1.58-bit precision (i.e., ternary weights {-1, 0, 1}) for specific downstream tasks, achieving strong task-specific performance with minimal computational cost. Specifically, BitDistill incorporates three key techniques: the SubLN module, as introduced in BitNet; multi-head attention distillation, based on MiniLM; and continual pre-training, which serves as a crucial warm-up step to mitigate the scalability issue of the performance gap between finetuned full-precision and 1.58-bit LLMs on specific tasks. Experimental results show that BitDistill achieves performance comparable to the full-precision counterpart models across model size, while enabling up to 10x memory savings and 2.65x faster inference on CPUs. Code is available at https://github.com/microsoft/BitNet.

Authors (7)

Xun Wu

Shaohan Huang

Wenhui Wang

Ting Song

Li Dong

Yan Xia

+1 more

Submitted

October 15, 2025

arXiv Category

cs.LG

arXiv PDF Code

Key Contributions

Presents BitNet Distillation (BitDistill), a lightweight pipeline to fine-tune full-precision LLMs into 1.58-bit precision (ternary weights) for specific tasks. It incorporates the SubLN module, multi-head attention distillation, and continual pre-training to mitigate performance gaps and achieve strong task-specific performance with significant computational savings.

Business Value

Makes powerful LLMs accessible on resource-constrained devices (like CPUs) by drastically reducing their memory and computational requirements, enabling wider adoption of AI.

Paper Metadata

Innovation Type

Algorithmic Technique

Deployment Feasibility

High, as it focuses on optimizing existing LLMs for efficient deployment, particularly on CPUs, and provides code.

Limitations Addressed

The scalability issue of the performance gap between full-precision and low-precision LLMs on specific tasks, and the high computational cost (memory and speed) of deploying large LLMs.

Performance Gains

Achieves performance comparable to full-precision counterparts while enabling up to 10x memory savings and 2.65x faster inference on CPUs.

View Code on GitHub

Technical Tags

BitNetternary weightsquantizationLLM distillationSubLN modulemulti-head attention distillationcontinual pre-trainingparameter efficiencyinference speedmemory savings

Research Topics

Model QuantizationEfficient LLMsKnowledge DistillationLow-Precision ComputingLLM Optimization

Methods & Architectures

BitNet Distillation (BitDistill)SubLN moduleMulti-head attention distillation (MiniLM-based)Continual pre-training BitNet (1.58-bit precision)Full-precision LLMs (e.g., Qwen)MiniLM

Applications & Tasks

Natural Language Processing Artificial Intelligence Edge AI Resource-Constrained Computing Reducing LLM Memory FootprintAccelerating LLM InferenceAchieving High Performance with Low PrecisionTask-Specific LLM Adaptation LLM Fine-tuningModel QuantizationKnowledge DistillationNatural Language Understanding

Related Fields

Machine LearningDeep LearningModel CompressionNatural Language ProcessingComputer Architecture

Keywords

BitNetquantizationternary weightsLLMdistillationparameter efficiencyinferencememorySubLNattentionpre-trainingCPUMicrosoft

Academic Context

#Model Quantization#Efficient LLMs#Knowledge Distillation#Low-Precision Computing#LLM Optimization

Companies & Organizations

Companies Mentioned

Microsoft

Commercial Potential

Potential Products

Optimized LLM libraries for edge devicesEfficient AI inference enginesOn-device AI assistants

Target Industries

TechnologySoftwareMobileAutomotive (in-car assistants)Consumer Electronics

Use Case Examples

Running advanced LLM features on smartphones without cloud connectivityDeploying LLMs on edge servers with limited memoryAccelerating LLM-based applications on standard CPUs

Competitive Edge

Offers a highly efficient method for quantizing LLMs to ternary precision, achieving near full-precision performance with significant speedups and memory reductions, particularly on CPUs.

Market Opportunity

Massive and growing market for efficient LLMs, driven by edge AI and cost reduction needs.

Revenue Models

Licensing of the technologyintegration into cloud AI servicesdevelopment of specialized hardware.

Resource Requirements

Compute Needs

Moderate for fine-tuning, but significantly reduced inference requirements compared to full-precision models.

Data Requirements

Requires task-specific data for fine-tuning and potentially a corpus for continual pre-training.

Deployment Constraints

Performance might still lag behind full-precision models on highly complex tasks. Requires specific hardware/software support for ternary operations if not running on CPU.

Scalability

The technique is highly scalable due to reduced memory and computational needs, enabling deployment on a wider range of hardware.

Production Readiness

Maturity Level

Research/Development

Time to Market

6-12 months for integration into existing LLM frameworks and products.

Licensing

Likely permissive (e.g., MIT) given the GitHub repository.

Patent Potential

Moderate, for the BitDistill pipeline and specific components like the SubLN module.

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