feat: add BitNet Distillation (BitDistill) pipeline code

[yushuiwx]

🚀 What does this PR do?

This PR introduces BitNet Distillation (BitDistill), a lightweight distillation and fine-tuning pipeline that converts off-the-shelf full-precision LLMs (e.g., Qwen) into 1.58-bit (ternary) BitNet models for specific downstream tasks.

BitDistill enables task-specific adaptation of BitNet models while preserving strong performance and significantly reducing memory and inference cost.

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Background & Motivation

While BitNet demonstrates strong efficiency advantages with 1.58-bit weights, directly fine-tuning low-bit models often leads to a noticeable performance gap compared to full-precision counterparts, especially on downstream tasks.

BitDistill addresses this issue by combining:

• architectural improvements from BitNet,
• a continual pre-training warm-up stage,
• and representation-level distillation techniques.

to improve scalability and downstream task performance of low-bit models.

This PR is based on the paper:

BitNet Distillation
https://arxiv.org/abs/2510.13998

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Key Techniques Implemented

This implementation includes the following core components:

1. SubLN module

   • Improves training stability for low-bit models

2. Continual pre-training warm-up

   • Mitigates the performance gap between full-precision and 1.58-bit models

3. Multi-head attention distillation

   • Distills attention patterns from full-precision teacher models

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Results & Benefits

• Achieves comparable task performance to full-precision models across model sizes
• Enables up to 10× memory reduction
• Achieves up to 2.65× faster CPU inference

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Note: This PR replaces a previous one that mistakenly added BitDistill as a submodule. The code is now fully included.

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