Pruning and Distilling Large Language Models - A Path to Efficient AI

15 February 2025 - 4 mins read time
Tags: Blogs

Title: Pruning and Distilling Large Language Models: A Path to Efficient AI

Introduction

Large Language Models (LLMs) have become a dominant force in natural language processing (NLP). However, their massive size and resource-intensive nature make them costly to train and deploy. In response, researchers are exploring ways to create smaller, more efficient models that maintain strong language understanding while reducing computational demands.

One effective strategy is combining weight pruning and knowledge distillation. NVIDIA was among the first to demonstrate that this approach significantly improves efficiency while maintaining performance.

Benefits of Pruning and Distillation

Applying pruning and distillation together offers several advantages:

• Higher accuracy: Model benchmarks show that smaller models created using these techniques can achieve up to a 60% increase in MMLU scores (Massive Multitask Language Understanding).

• Reduced training cost: Smaller models require fewer training tokens and iterations, cutting costs.

• Lower compute requirements: Less memory and processing power needed for deployment.

• Performance retention: Despite being smaller, models retain competitive performance compared to leading architectures like Mistral and Hemma.

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Understanding Pruning and Distillation

What is Pruning?

Pruning reduces the size of a model by removing less important components. There are two primary types:

• Depth Pruning: Eliminates entire layers from the model, reducing overall model depth and computation cost.

• Width Pruning: Removes specific neurons and attention heads within layers, making each layer more efficient while maintaining depth.

Pruning often requires some level of retraining to recover accuracy lost due to parameter reduction. The effectiveness of pruning depends on how well important versus redundant parameters are identified.

Advanced Pruning Techniques

• Structured vs. Unstructured Pruning: Structured pruning removes entire neurons or attention heads, whereas unstructured pruning removes individual weights, making optimization more challenging but sometimes more effective.

• Lottery Ticket Hypothesis: Some research suggests that within large models, smaller subnetworks exist that, when properly trained, can achieve near-original performance.

What is Knowledge Distillation?

Distillation is the process of transferring knowledge from a larger, complex teacher model to a smaller student model. The goal is to make the student model mimic the teacher’s knowledge and behavior, reducing size without drastically losing performance.

There are two main approaches:

1. Synthetic Data (SGD) Fine-tuning: The teacher generates synthetic data, which is then used to fine-tune the student. The student only learns to predict the final output tokens.

2. Classical Knowledge Distillation: Instead of just mimicking the final output, the student also learns the teacher’s intermediate representations, logits, and embeddings. This provides richer supervision and leads to better generalization.

Variations of Knowledge Distillation

• Feature-based distillation: The student learns feature maps from the teacher to capture internal representations.

• Contrastive distillation: The student is trained to differentiate between correct and incorrect outputs based on teacher insights.

• Progressive distillation: Gradually reduces teacher complexity rather than training the student in a single step.

How to Prune and Distill an LLM

Here’s a step-by-step breakdown of how pruning and distillation work together:

1. Start with a large model (e.g., 15B parameters).

2. Analyze importance: Rank and identify the least important layers using activation-based importance estimation on a small calibration dataset (~1024 samples).

3. Prune the model: Remove unimportant components based on the ranking.

4. Perform knowledge distillation: Use the original model as a teacher and the pruned model as a student.

5. Iterate: The pruned and distilled model can serve as a base for further pruning and distillation, progressively creating even smaller versions.

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Best Practices for Pruning and Distillation

Model Sizing

• If you plan to train multiple versions of an LLM, start with the largest model and iteratively prune and distill to obtain smaller versions.

• If the largest model was trained using multiple training phases, prune and distill from the final phase’s version.

Pruning Strategy

• Width pruning (removing neurons) is preferable over depth pruning (removing layers).

• Use single-shot importance estimation—iterative importance estimation does not yield better results.

Retraining and Fine-Tuning

• Instead of conventional training, use distillation exclusively for retraining pruned models.

• If the model’s depth has been significantly reduced, use logit + intermediate state + embedding distillation for better performance.

• If the model’s depth is largely retained, logit-only distillation is sufficient.

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Conclusion

By combining pruning and knowledge distillation, we can create smaller, faster, and cheaper language models without sacrificing much performance. These techniques make it feasible to deploy strong NLP models in resource-constrained environments, paving the way for a future where efficient AI is widely accessible.

As research in this area continues, expect to see even more refined techniques that push the boundaries of efficiency without compromising language understanding capabilities.