I feel that the effects of fine-tuning are often short-term, and sometimes it can end up overwriting what the model has already learned, making it less intelligent in the process.
I lean more towards using adaptive methods, optimizing prompts, and leveraging more efficient ways to handle tasks. This feels more practical and resource-efficient than blindly fine-tuning.
We should focus on finding ways to maximize the potential of existing models without damaging their current capabilities, rather than just relying on fine-tuning.
Fine-tuning LLMs is a waste of time
41–50 of 94 posts
Re: Fine-tuning LLMs is a waste of time
#42I 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…
You do you, and if it works i’m not going to argue with your results, but for others, finetuning is the wrong tool for knowledge injection over a well-designed RAG pipeline.
Finetuning is good for, like you said, doing things a particular way but that’s not the same thing as being good at knowledge injection and shouldn’t considered as such.
It’s also much easier to prevent a RAG pipeline from generating hallucinated responses. You cannot finetune that out of a model.
Re: Fine-tuning LLMs is a waste of time
#43This is a pretty awful take. Everyone understands they are modifying the weights - that is the point. It’s not like these models were released with all of the weights perfectly accounted for and changing them in any way ruins them. The awesome thing about fine-tuning is that the weights are malleable and you have a great base to start from. Also the basic premise that knowledge injection is a bad use-case seems flawe…
Medgemma gets its domain expertise from pre-training on medical datasets, not finetuning. It’s pretty uncharitable to call the post an awful take if you’re going to get that wrong.
Re: Fine-tuning LLMs is a waste of time
#44This is a pretty awful take. Everyone understands they are modifying the weights - that is the point. It’s not like these models were released with all of the weights perfectly accounted for and changing them in any way ruins them. The awesome thing about fine-tuning is that the weights are malleable and you have a great base to start from. Also the basic premise that knowledge injection is a bad use-case seems flawe…
Medgemma gets its domain expertise from pre-training on medical datasets, not finetuning. It’s pretty uncharitable to call the post an awful take if you’re going to get that wrong.
You can call it pre-training but it’s based on Gemma 3 4b - which was already pre-trained on a general corpus. It’s the same process, so you’re just splitting hairs. That is kind of my point, fine-tuning is just more training. If you’re going to say that fine-tuning is useless you are basically saying that all instruct-tuned models are useless as well - because they are all just pre-trained models that have been subsequently trained (fine-tuned) on instruction datasets.
Re: Fine-tuning LLMs is a waste of time
#45This is a pretty awful take. Everyone understands they are modifying the weights - that is the point. It’s not like these models were released with all of the weights perfectly accounted for and changing them in any way ruins them. The awesome thing about fine-tuning is that the weights are malleable and you have a great base to start from. Also the basic premise that knowledge injection is a bad use-case seems flawe…
A man who burns his own house down may understand what they are doing and do it intentionally - but without any further information still appears to be wasting his time and doing something stupid. There isn't any contradiction between something being a waste of time and people doing it on purpose - indeed the point of the article is to get some people to change what they are purposefully doing. He's proposing alterna…
It’s possible I misinterpreted a bit the gist of the article - in my mind nobody is doing fine-tuning these days without using techniques like LoRA or DoRA. But they are using these techniques because they are computationally efficient and convenient, and not because they perform significantly better than full fine-tuning.
Re: Fine-tuning LLMs is a waste of time
#46Correct me if I am wrong, but I thought the point of fine-tuning was to get precise returns. We make it hyper specific to the task at hand.
Sure, we can get 90% of the way there without fine-tuning, but most of these models are vast.
I would argue that it potentially MAY be a waste of time right out the gate.
Re: Fine-tuning LLMs is a waste of time
#47I 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.
Re: Fine-tuning LLMs is a waste of time
#48Clickbait headline. "Fine-tuning LLMs for knowledge injection is a waste of time" is true, but IDK who's trying to do that. Fine-tuning is great for changing model behavior (i.e. the zillions of uncensored models on Hugging Face are much more willing to respond to... dodgy... prompts than any amount of RAG is gonna get you), and RAG is great for knowledge injection. Also... "LoRA" as a replacement for finetuning??? L…
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Re: Fine-tuning LLMs is a waste of time
#49I 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.
It's not either/or. Generally you finetune when optimized many-shot still doesn't hit your desired quality bar. And it turns out with RL, things like system prompts matter a lot, so searching over prompts is a good idea even when reinforcing the desirable circuits.
Re: Fine-tuning LLMs is a waste of time
#50I 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.