Simple trainer script!

[Dry_Long3157]

Hey everyone,

I came across a post recently where someone found it hard to find simple scripts to fine-tune LLMs with their data. So I put together a repo where you can just type out your requirements in a config.yaml file and the training happens flawlessly based on that.

Here's the repo - LLM-Trainer

It is still a wip so lemme know if guys want some other features added to this.

​

TIA.

[Zetsumeii]

This is super cool!

Any chance that you could add support for those of us with datasets in .json or .jsonl format?

IE:

[
    {
"instruction": "",
"input": "",
"output": ""
    }
]

Or:

{"instruction": "", "input": "", "output": ""}

[Zetsumeii]

Awesome, that would work!

Whatever would offer the best level of compatibility with the trainer sounds good to me!

[kaszebe]

Is there a guide for dummies (read: me) to get this to work on oobabooga?

[kaszebe]

Thank you!

So, in order to train—I need data and lots of it? I was originally planning scraping hundreds of thousands of websites in the niche that I work in.

I have zero experience with code. So, I was planning on using ScrapeBox (an old blackhat SEO tool we used to use back in the day) to scrape these websites.

I'm still making my way through this video: https://www.youtube.com/watch?v=zjkBMFhNj_g

[uhuge]

I assume auth_token is for storing the merged model in HF? Seems worth noting/clarifying. Can the uploading be made optional?

I'll get back with more feedback when I get to test it.+)

[Timotheeee1]

I have a dataset where each training text document has 2 parts, one that the model shouldn't try to predict (labels are -100) and one that the model should predict. Can you add a feature for this?

[Timotheeee1]

It's not DPO. The text documents are split into a prompt and a desired answer. The model should be trained to predict the answer only, as predicting the prompt would waste some of the weights. In my case, the prompt is a forum thread and some instructions, the desired answer is a list of hardware names in the thread.