To intentionally oversimplify, fine-tuning an LLM on your data is a completely nonsensical concept for 99% of the world.
People have the impression that training an LLM on your data will result in an LLM that can answer questions on your data. But for any realistic dataset and training that a non-FAANG can do, that's not true.
> Can you help explain how to take different data sources maybe like a list of documentation for an open source project and fine tune using it?
You would not do this.
Let's say you're writing code with an open source library. There's a new animation API that didn't exist when the LLM was trained:
1. You ask your coding chatbot: How do I make this this box move right to left across my screen
2. Before the chatbot UI submits the question to the LLM, it manually searches for text related to to your question in the library's documentation using BM25F and BERT
3. You give the LLM the results of your search and the user's question, at the same time.
The LLM now has a snippet of up to date documentation, and can look at that to produce a novel code that animates the box based on the documentation.
Depending on latency requirements you can have a "Step 2.5", where you ask the LLM "What searches would you do if I gave you the docs for this library and you needed to answer ".
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Here BERT is being used to find snippets of text that are more likely to help us answer a question
For example, this model: https://huggingface.co/thenlper/gte-large
When given the query: Reference documentation for 'Write some code to move this square across my screen'
It ranks some imaginary documentation in the following order:
1. "Object translation has been reworked in the new animation API"
2. "Layout components include the Box, Grid, and Stack"
3. "Move your hosting to AWS with our cloud build API"
The BERT model "understands" that while we used the words moving and square:
- "Move your hosting" is a semantically different concept
- "Box" is similar to "Square", but "Box" is not central to the request.
Now we can give the LLM the most relevant snippet, and it uses that as guidance for its own reply (also known as Retrieval Augmented Generation)
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There is a place where fine-tuning can actually be applied in this process but it is not fine-tuning an (chat) LLM. It is completely unrelated to most mentions of fine-tuning you've heard in the last X months.
You can fine-tune BERT (a much smaller model) to get better at finding relevant snippets of your documentation. You can do this without labeled data.
*Literally give it a bag of sentences and let it go to work*: https://www.sbert.net/examples/unsupervised_learning/TSDAE/R...
TSDAE doesn't really perform that well on wide domains, but it works well here the documents have both information, and what that information is for (think code documentation with examples vs wikipedia which is just raw information). It also only takes 1k sentences to start, you could find a bunch of random documentation sites on Github and feed them in.