Reproduce Constitutional AI Steps

# Overview

This issue captures some of the key steps required to reproduce the Constitutional AI paper steps to fine tune a RLHF model with feedback generated by a RLAIF model.

## Phase One

- [ ] Gather a dataset of harmful prompts
- [ ] Create a base script to compose prompts using a base *constitution*
- [ ] Generate a new dataset of prompts + responses using Carper's [GPT-J RLHF](https://huggingface.co/reciprocate/ppo_hh_gpt-j) to review / critique the output
- [ ] Fine-tune the original model on revised responses using supervised learning

## Phase Two

- [ ] Sample the fine tuned model using the dataset of harmful prompts to create a new dataset with multiple outputs
- [ ] Train a "reward model' (i.e. https://github.com/Dahoas/reward-modeling) to select the best result (fine tuned preference model)
- [ ] Use RLAIF training to fine tune the RLHF model