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Examples

Here you can find end-to-end examples to help you get started with curanting datasets and collecting feedback to fine-tune LLMs.

Fine-tuning and evaluating GPT-3.5 with human feedback for RAG

Learn how to fine-tune and evaluate gpt3.5-turbo models with human feedback for RAG applications with LlamaIndex.

Curate an instruction dataset for supervised fine-tuning

Learn how to set up a project to curate a public dataset that can be used to fine-tune an instruction-following model.

Train a Reward Model for RLHF

Learn how to collect comparison or human preference data and train a reward model with the trl library.

Add zero-shot suggestions using SetFit

Learn how to add suggestions to your FeedbackDataset using SetFit.

Create and annotate synthetic data with LLMs

Learn how to create synthetic data and annotations with OpenAI, LangChain, Transformers and Outlines.

Fine-tune a SetFit model using the ArgillaTrainer

Learn how to use the ArgillaTrainer to fine-tune your Feedback dataset using Setfit.