Adds fine tuning contrib example

[skrawcz]

This module shows how one can perform fine tuning using Hamilton. It is a basic example using the transformers library that connects to huggingface to pull and fine tune a FLAN model.

One should be able to adapt this code to their needs.

For new dataflows:

Do you have the following?

• Added a directory mapping to my github user name in the contrib/hamilton/contrib/user directory.
  • If my author names contains hyphens I have replaced them with underscores.
  • If my author name starts with a number, I have prefixed it with an underscore.
  • If your author name is a python reserved keyword. Reach out to the maintainers for help.
  • Added an author.md file under my username directory and is filled out.
  • Added an init.py file under my username directory.
• Added a new folder for my dataflow under my username directory.
  • Added a README.md file under my dataflow directory that follows the standard headings and is filled out.
  • Added a init.py file under my dataflow directory that contains the Hamilton code.
  • Added a requirements.txt under my dataflow directory that contains the required packages outside of Hamilton.
  • Added tags.json under my dataflow directory to curate my dataflow.
  • Added valid_configs.jsonl under my dataflow directory to specify the valid configurations.
  • Added a dag.png that shows one possible configuration of my dataflow.
• I hearby acknowledge that to the best of my ability, that the code I have contributed contains correct attribution
  and notices as appropriate.

How I tested this

Ran it locally in a docker container

Notes

We might want to invest in making it so that multiple modules could make a contribution because that could help segment the code better.

Checklist

• PR has an informative and human-readable title (this will be pulled into the release notes)
• Changes are limited to a single goal (no scope creep)
• Code passed the pre-commit check & code is left cleaner/nicer than when first encountered.
• Any change in functionality is tested
• New functions are documented (with a description, list of inputs, and expected output)
• Dataflow documentation has been updated if adding/changing functionality.

[elijahbenizzy]

I think we can simplify a bit by:

1. Removing inference from this -- that should be a snippet of code in the README of an ipynb or something
2. Processing features prior to splitting the dataset
3. Fixing up/removing some of the docstrings

[elijahbenizzy]

Can we tokenize the dataset before splitting? That'll kill a lot of duplicate code and make it easier to read.

Otherwise this looks fine, I think you should break it out cause it's doing too much and I find that hard to follow, but let's just get it out.

[elijahbenizzy]

Looks fine, maybe _preprocess_function should do the dataset .map call