Model Pruning
Model pruning is a technique for reducing neural network size and computational cost by eliminating redundant or less important weights, connections, or layers while preserving model accuracy. Common approaches include weight magnitude pruning, structured pruning, and sensitivity-based pruning.
- Example:
whisper-large-v3-turbo(used for Automatic Speech Recognition) is a fine-tuned, pruned variant ofwhisper-large-v3, enabling approximate real-time transcription in resource-constrained environments like Google Colab 2026 04 14 Fahd Mirza getting Whisper working on Google Colab.
Source Notes
- 2026-04-23: https://www.youtube.com/watch?v=TvWhDZGzJiI This video introduces a context engineering technique called Provence that aims to substantially reduce hallucination in Retrieval Augmented Generation (RAG) systems by efficiently pruning irrelevant information from retrieved (RAG re ranking with pruning channel Prompt Engineering)
- 2026-04-14: # Fahd Mirza - getting Whisper working on Google Colab --- --- https://www.youtube.com/watch?v=0Rdf2XA9G9Y Real time ASR - automated speech recognition This video provides a comprehensive guide on perf (Fahd Mirza - getting Whisper working on Google Colab)