OpenPipe-llm/examples/classify-recipes/README.md

OpenPipe demo: fine-tuning your own model

Hi there! This repository should give you a brief overview of how to fine-tune a competitive model from start to finish. You should review the notebooks in this directory in the following order:

1. ./generate-data.ipynb: Demonstrates how to generate a sample dataset of GPT-4 completions, store it using OpenPipe, and then export it in a format suitable for training a model.
2. ./train.ipynb: Trains a Llama 2 7B model on the dataset from step (1).
3. ./evaluate.ipynb: Evaluates the model we trained using a special test set that we set aside in step (1).
4. ./benchmark.ipynb: A script to compare costs and completion latencies between our fine-tuned model, GPT-3.5, and GPT-4.

If you want to follow along yourself, I recommend using RunPod. The training scripts we use will run on any of their GPUs with 24GB of vRAM or more.