Finetuning guide

[strickvl]

Draft of documentation / guide around LLM finetuning.

Gitbook preview here.

Remaining work

• more diagrams
• complete the deployment writeup (when vLLM model deployer #3032 is merged in)

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[htahir1]

Alright i read it again. I think its great. But I have some feedback:

• There are lots of lists in the text like "Here is a list: here is an explanation" -> Id format this with the first part being in bold "Here is a list: here is an explanation"
• This is again a pet peeve but bullet points , should they begin with a capital or small letter?...
• For me the chapter that sort of doesn't "fit" well into the guide is "Starter choices with fine-tuning". I'm not quite sure whether its the lack of link outs or the lack of code in there that makes it just feel like I have a lot of reading to do before I get to the action.. maybe there is a way to condense it into a visual element or maybe tell the story in a code-centric way? Or give more examples as to the choices available?
• Finetuning with Accelerate doesn't point out that we have an integration with a decorator... that is what I expected here tbh
• I expected a link from the new evaluation section to the old RAG evaluation section but I found only "Please note that the approach taken for these evaluations is similar to that used and showcased in the RAG guide." .. Maybe there is a way to pick up the story from there? What is actually different from what was said before? There might be a shortcut by making a link back
• The deployment chapter is missing a code snippet from our VLLM deployer by @dudeperf3ct :-) Id love to get that in there, even if it isn't released yet.

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[htahir1]

Feedback:

• Looks like a lot of files have been deleted - would it make sense to add redirects for these?
• Other than that and the comments this looks SUPER amazing and solid. Great work!!!

[strickvl]

@htahir1 the deleted files were actually duplicate docs which weren't linked anywhere, so no need to link. they weren't in our TOC to start with.

[htahir1]

Amazing! LGTM!