Fine-Tuning a Small Language Model

Small Language Models (SLMs) offer an efficient alternative to massive models, running locally or on limited hardware while maintaining high performance for specific tasks. This course teaches how to fine-tune an SLM using custom data. You will learn the mechanics of parameter-efficient fine-tuning, data formatting, and model evaluation. By the end of the course, you will have a fully functioning, task-specific language model ready for deployment.

Prerequisites Basic Python and PyTorch

Level:

Professional

Parameter-Efficient Fine-Tuning
Implement LoRA and QLoRA to train models under strict memory limits.
Dataset Preparation
Format and tokenize instruction datasets for supervised fine-tuning.
Model Evaluation
Measure text generation quality and detect overfitting during the training process.
Local Deployment
Merge adapter weights and serve the customized model using vLLM.

Fine-Tuning a Small Language Model

Prerequisites Basic Python and PyTorch

Level:

Professional

Parameter-Efficient Fine-Tuning
Implement LoRA and QLoRA to train models under strict memory limits.
Dataset Preparation
Format and tokenize instruction datasets for supervised fine-tuning.
Model Evaluation
Measure text generation quality and detect overfitting during the training process.
Local Deployment
Merge adapter weights and serve the customized model using vLLM.