Faster LLM and NLP pipelines
Finding the bottlenecks in language-model pipelines, then tuning them for speed, accuracy and cost.
- Year
- 2024
- Tools
- Python, LLMs, NLP, Hyperparameter tuning
The aim
Improve the performance of machine learning pipelines built around large language models (LLMs) and natural language processing (NLP).
What we did
- Profiled the existing pipelines to find the bottlenecks.
- Optimised data preprocessing and training.
- Tested hyperparameter tuning and refinements to the model architecture.
What we delivered
- Updated pipelines with measurable performance gains.
- A report comparing the old and new pipelines.
- Documentation for future optimisation work.
The outcome
Faster, more accurate pipelines for LLM and NLP work. Better results in less time, using fewer resources.