Large Language Model Fine-Tuning

LLM: Fine-tuning and Adaptation

Large language models adapt behavior to specific domains through instruction tuning, RLHF (Reinforcement Learning from Human Feedback), and further training on corporate data.

- Instruction Tuning on Dialogue Examples

- RLHF: combines human evaluation with policy optimization.

live demo · related simulation● LIVE

- Parametric Efficiency: LoRA, Prefix Tuning

Collecting a clean corpus, deduplication, toxicity filtering, domain balancing. Maintaining privacy and complying with compliance requirements.

Frequently asked questions

What metrics should be used to evaluate large language models?

Metrics such as accuracy, coverage, and usefulness should be employed, alongside ‘red-teaming’ tests for security.

How can a robust LLM deployment pipeline be established?

A deployment pipeline incorporating versioning, monitoring model drift, telemetry data, and rapid rollback capabilities is crucial.

▶ Try it live

Everything above runs in your browser — open Gradient Descent Visualiser and change the parameters while it is running. Nothing is installed, nothing is uploaded, the whole model lives in one tab.

▶ Open Gradient Descent Visualiser simulation