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Fine-tuning LLMs is a waste of time

codinginterviewsmadesimple.substack.com

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Re: Fine-tuning LLMs is a waste of time

#71

Post by WhitneyLand »

kouteiheika wrote: post #59
xpe wrote: post #57

Earlier quoted context omitted.

> LoRA does exactly the same thing as normal fine-tuning You wrote exactly so I'm going to say "no". To clarify what I mean: LoRA seeks to accomplish a similar goal as "vanilla" fine-tuning but with a different method (freezing existing model weights while adding adapter matrices that get added to the original). LoRA isn't exactly the same mathematically either; it is a low-rank approximation (as you know). > LoRA do…

> You wrote exactly so I'm going to say "no". [...] If you think charitably, the author is right. No, the author is objectively wrong. Let me quote the article and clarify myself: > Fine-tuning advanced LLMs isn’t knowledge injection — it’s destructive overwriting. [...] When you fine-tune, you risk erasing valuable existing patterns, leading to unexpected and problematic downstream effects. [...] Instead, use modula…

If we zoom out a bit to one point he’s trying to make there, while LoRA is fine tuning I think it’s fair to call it a more modular approach than base SFT.

That said, I find the article as a whole off-putting. It doesn’t strengthen one’s claims to call things stupid or a total waste of time. It deals in absolutes, and rants in a way that misleads and foregoes nuance.

Re: Fine-tuning LLMs is a waste of time

#72

Post by rco8786 »

> Instead, use modular methods like retrieval-augmented generation, adapters, or prompt-engineering — these techniques inject new information without damaging the underlying model’s carefully built ecosystem.

So obviously this is what most of us are already doing, I would venture. But there's a pretty big "missing middle" here. RAG/better prompts serve to provide LLMs with the context they need for a specific task, but are heavily limited by context windows. I know they've been growing quite a bit, but from my usage it still seems that things further back in the window get forgotten about pretty regularly.

Fine tuning was always the pitch for the solution to that. By baking the "context" you need directly into the LLM. Very few people or companies are actually doing this though, because it's expensive and you end up with an outdated model by the time you're done...if you even have the data you need to do it in the first place.

So where we're left is basically without options for systems that need more proprietary knowledge than we can reasonably fit into the context window.

I wonder if there's anyone out there attempting to do some sort of "context compression". An intermediary step that takes our natural language RAG/prompts/context and compresses it into a data format that the LLM can understand (vectors of some sort?) but are a fraction of the tokens that the natural language version would take.

edit After I wrote this I fed this into chatgpt and asked if there were techniques i am missing. It introduced me to Lora (which I suppose are the "adapters" mentioned in the OP). and now I have a whole new rabbithole to climb down. AI is pretty cool sometimes.

Re: Fine-tuning LLMs is a waste of time

#73

Post by a_c »

I don’t know if fine tuning works. But if it doesn’t, then are we assuming the underlying weights are optimal? At what point do we determine that a network is properly “trained” and any subsequent training is “fine tuning”.

Re: Fine-tuning LLMs is a waste of time

#74

Post by elzbardico »

Lots of prophets in every gold rush...

While the author makes some good points (along with some non-factual assertions), I wonder why he decided to have this counter-productive and factually wrong clickbait title.

Fine-tuning (and LoRA IS fine-tuning) may not be cost-effective for most organizations for knowledge updates, but it excels in driving behavior in task specific ways, for alignment, for enforcing structured output (usually way more accurately than prompting), tool and function use, and depending on the type of knowledge, if it is highly specific, niche, long tail type of knowledge, it can even make smaller models beat bigger models, like the case with MedGemma.

Re: Fine-tuning LLMs is a waste of time

#75

Post by bird0861 »

stingraycharles wrote: post #28
rybosome wrote: post #21

I think the point the author misses is that many applications of fine-tuning are to get a model to do a single task. This is what I have done in my current role at my company. We’ve fine-tuned open weight models for knowledge-injection, among other things, and get a model that’s better than OpenAI models at exactly one hyper specific task for our use case, which is hardware verification. Or, fine-tuned the OAI models…

Exactly. I want the LLM to be able to respond to our customers’ questions accurately and/or generate proper syntax for our query language. The whole point of base models is to be general purpose, and fine tuned models to be tuned for specific tasks using a base model.

Just to be clear, unless I'm misinterpreting this chain of comments, you do not want to fine-tune for information retrieval. FT is for skill enhancement. For information retrieval you want at least one of the over 100 implementations of RAG out there now.

Re: Fine-tuning LLMs is a waste of time

#76

Post by bird0861 »

simonw wrote: post #6

"Fine-tuning large language models (LLMs) is frequently sold as a quick, powerful method for injecting new knowledge" Is that true though? I don't think I've seen a vendor selling that as a benefit of fine-tuning.

To be fair there are lots of Facebook, Instagram, and Youtube cargo cultists telling people to fine-tune on their documents for some reason. This got to be so common in 2024 that I think it was part of the pressure behind Gigabyte branding their hardware around it.

Re: Fine-tuning LLMs is a waste of time

#77

Post by rybosome »

RoyTyrell wrote: post #50
rybosome wrote: post #21

I think the point the author misses is that many applications of fine-tuning are to get a model to do a single task. This is what I have done in my current role at my company. We’ve fine-tuned open weight models for knowledge-injection, among other things, and get a model that’s better than OpenAI models at exactly one hyper specific task for our use case, which is hardware verification. Or, fine-tuned the OAI models…

Let me preface by saying I'm not skeptical about your answer or think you're full of crap. Can you give me an example or two about a single task that you fine-tune for? Just trying to familiarize myself with more AI engineering tasks.

Yep!

So my use case currently is admittedly very specific. My company uses LLMs to automate hardware design, which is a skill that most LLMs are very poor at due to the dearth of training data.

For tasks which involve generation of code or other non-natural language output, we’ve found that fine-tuning with the right dataset can lift performance rapidly and decisively.

An example task is taking in potentially syntactically incorrect HDL (Hardware Description Language) code and fixing the syntax issues. Fine-tuning boosted corrective performance significantly.

Re: Fine-tuning LLMs is a waste of time

#78

Post by rybosome »

BenGosub wrote: post #47
rybosome wrote: post #21

I think the point the author misses is that many applications of fine-tuning are to get a model to do a single task. This is what I have done in my current role at my company. We’ve fine-tuned open weight models for knowledge-injection, among other things, and get a model that’s better than OpenAI models at exactly one hyper specific task for our use case, which is hardware verification. Or, fine-tuned the OAI models…

In this case, for doing specific tasks, it makes much more sense to optimize the prompts and the whole flow with DSPy, instead of just fine tuning for each task.

A wonderful approach generally and something we also do to some extent, but not a substitute for fine-tuning in our case.

We are working in a domain where there is very limited training data, so what we really want is continued pre-training over a larger dataset. Absent that, fine-tuning is highly effective for non-NLP tasks.

Re: Fine-tuning LLMs is a waste of time

#79

Post by ijk »

BenGosub wrote: post #47
rybosome wrote: post #21

I think the point the author misses is that many applications of fine-tuning are to get a model to do a single task. This is what I have done in my current role at my company. We’ve fine-tuned open weight models for knowledge-injection, among other things, and get a model that’s better than OpenAI models at exactly one hyper specific task for our use case, which is hardware verification. Or, fine-tuned the OAI models…

In this case, for doing specific tasks, it makes much more sense to optimize the prompts and the whole flow with DSPy, instead of just fine tuning for each task.

DSPy, notably, includes functionality for finetuning models. [1]

[1] https://dspy.ai/tutorials/games/

Re: Fine-tuning LLMs is a waste of time

#80

Post by mountainriver »

I love how people say things like this with complete disregard for research.

Most LLM research involves fine tuning models, and we do amazing things with it. R1 is a fine tune, but I guess that’s bad?

Our company adds knowledge with fine tuning all the time. It’s usually a matter of skill not some fundamental limit. You need to either use LoRA or use a large batch size and mix the previous training data in.

All we are doing is forcing deep representations. This isn’t a binary “fine tuning good/bad” it’s a spectrum of how deep and robust you make the representations

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