Fine tuning is a story that is nice to tell but that with modern LLMs makes less and less sense. Modern LLMs are so powerful that they are able to few shot learn complicated things, so a strong prompt and augmenting the generation (given the massive context window of Qwen3.5, too) is usually the best option available. There are models for which fine tuning is great, like image models: there with LoRa you can get good results in many ways. And LLMs of the past, too: it made sense for certain use cases. But now, why? LLMs are already released after seeing (after pre-training) massive amount of datasets for SFT and then RL. Removing the censorship is much more efficiently done with other techniques. So I have a strong feeling that fine tuning will be every day less relevant, and already is quite irrelevant. This, again, in the specific case of LLMs. For other foundational models fine tuning still makes sense and is useful (images, text to speech, ...).
I think the biggest case for fine tuning is probably that you can take small models, fine tune them for applications that require structured output, and then run cheap inference at scale. "Frontier LLMs can do it with enough context" is not really a strong argument against fine-tuning, because they're expensive to run.
I agree- I'm currently trying to learn how I can embed a fine tuned tiny model into my c++ game so it can provide a narrative in prose of certain game-event logs. It needs to be as tiny as possible so it doesn't take resources away from the running game.
These are fair points considering LLMs are getting smarter and better every week - but to be fair the biggest benefits of finetuning / RL are still not yet realized:
1. If we have robots at home, they need some sort of efficient continual learning, which could be on the go finetuning / RL via some small LoRA - this will need to do multimodal finetuning with sparse reward signals - one could also imagine all data is aggregated to one central processing center after anonymization, and training a larger model with more data + RL like that
2. Agreed images, audio, video etc is what still LoRA does well - the guide at https://unsloth.ai/docs/models/qwen3.5/fine-tune is actually a vision + text finetuning guide, so you can finetune the vision layers on your own use case
3. Model routing is going to be more the norm in the future - ie locally smallish models with LoRA for continuous finetuning can be used, but complex tasks can be offloaded to a large LLM in the cloud.
4. I also wrote about more use-cases below on the post - DoorDash, Vercel, Mercor, Stripe, NASA, Perplexity, Cursor and many others all do finetuning - for eg Cursor, Perplexity finetune large OSS LLMs themselves for their specific product lines - so there is definitely value if you have the data for it.
I work on Gemma and Gemini models I want to echo Daniel's point here. Small finetuned models have their place even with larger general purpose models.
For example last year with Daniel/Unsloth's help we released a tiny specialized model that can get equivalent to Gemini level purpose specifically for FC. For folks that need efficient limited purpose models small models like this can fit a specific need.
It's the same with chips, we have general purpose CPUs but we still have specialized silicon for tasks that are smaller, more power efficient, cheaper, and because they're single purpose it simplifies and derisks certain designs.
And I have to add, if you want to learn about finetuning models efficiently the Unsloth guides are at the top of my list. They're practical, have all the technical details, and most importantly Daniel and the others are working around the clock to keep it up to date in what is an incredibly fast moving space of models and hardware. I am continually astounded by their work.
Because these models are good in general but their Latvian output is half-drivel, like the roots of the words are usually the right ones, but not the rest.
That, and EuroLLM is really slow to release new models that would be similarly good off the shelf.
where it makes sense IMO is when you need it to know about a large amount of information that's not already in the model, such as a company knowledgebase, code repositories or a trove of specialized legal documents... in that case it's not realistic to try to stuff the context window every time with that information, especially if you're trying to make a responsive chat bot.
With the current context windows and the ability those models did RL to work as agents, it's much faster and reliable for them to use tools and find the information before replying. Much better, no hallucinations problems (or a lot less), no fine tuning needed when information changes. I believe it is exactly in this case that fine tuning is no longer useful, and even in the past worked at very different degrees of quality.
Only to prompt thought on this exact question, im interested in answers:
I just ran a benchmark against haiku of a very simple document classification task that at the moment we farm out to haiku in parallel. very naive same prompt system via same api AWS bedrock, and can see that the a few of the 4b models are pretty good match, and could be easily run locally or just for cheap via a hosted provider. The "how much data and how much improvement" is a question i dont have a good intuition for anymore. I dont even have an order of magnitude guess on those two axis.
Heres raw numbers to spark discussion:
| Model | DocType% | Year% | Subject% | In $/MTok |
percents are doc type (categorical), year, and subject name match against haiku. just uses the first 4 pages.
in the old world where these were my own in house models, id be interested in seeing if i could uplift those nubmers with traingin, but i haven't done that with the new LLMs in a while. keen to get even a finger to the air if possible.
Can easily generate tens of thousands of examples.
Awesome guide, shame how a couple of the Qwen leads got kicked out and replaced with more “business” minded leadership. Hopefully this doesn’t mean the end of the open source era from Qwen.
1. If we have robots at home, they need some sort of efficient continual learning, which could be on the go finetuning / RL via some small LoRA - this will need to do multimodal finetuning with sparse reward signals - one could also imagine all data is aggregated to one central processing center after anonymization, and training a larger model with more data + RL like that
2. Agreed images, audio, video etc is what still LoRA does well - the guide at https://unsloth.ai/docs/models/qwen3.5/fine-tune is actually a vision + text finetuning guide, so you can finetune the vision layers on your own use case
3. Model routing is going to be more the norm in the future - ie locally smallish models with LoRA for continuous finetuning can be used, but complex tasks can be offloaded to a large LLM in the cloud.
4. I also wrote about more use-cases below on the post - DoorDash, Vercel, Mercor, Stripe, NASA, Perplexity, Cursor and many others all do finetuning - for eg Cursor, Perplexity finetune large OSS LLMs themselves for their specific product lines - so there is definitely value if you have the data for it.
For example last year with Daniel/Unsloth's help we released a tiny specialized model that can get equivalent to Gemini level purpose specifically for FC. For folks that need efficient limited purpose models small models like this can fit a specific need.
https://blog.google/innovation-and-ai/technology/developers-...
Especially on device. https://developers.googleblog.com/on-device-function-calling...
It's the same with chips, we have general purpose CPUs but we still have specialized silicon for tasks that are smaller, more power efficient, cheaper, and because they're single purpose it simplifies and derisks certain designs.
And I have to add, if you want to learn about finetuning models efficiently the Unsloth guides are at the top of my list. They're practical, have all the technical details, and most importantly Daniel and the others are working around the clock to keep it up to date in what is an incredibly fast moving space of models and hardware. I am continually astounded by their work.
Because these models are good in general but their Latvian output is half-drivel, like the roots of the words are usually the right ones, but not the rest.
That, and EuroLLM is really slow to release new models that would be similarly good off the shelf.
1. Cursor used online RL to get +28% approval rate: https://cursor.com/blog/tab-rl
2. Vercel used RFT for their AutoFix model for V0: https://vercel.com/blog/v0-composite-model-family
3. Perplexity's Sonar for Deep Research Reasoning I think was a finetuned model: https://docs.perplexity.ai/docs/getting-started/overview
4. Doordash uses LoRA, QLoRA for a "Generalized Attribute Extraction model" https://careersatdoordash.com/blog/unleashing-the-power-of-l...
5. NASA flood water detection https://earthdata.nasa.gov/news/nasa-ibm- openly-release-geospatial-ai-foundation-model-nasa-earth-observation-data6
6. Online RL for robotics - imagine you teaching a robot in the future via some mini finetuning
7. OpenAI's RFT page has more: https://developers.openai.com/api/docs/guides/rft-use-cases
8. For larger models - https://www.mercor.com/blog/expert-data-drives-model-perform...
I just ran a benchmark against haiku of a very simple document classification task that at the moment we farm out to haiku in parallel. very naive same prompt system via same api AWS bedrock, and can see that the a few of the 4b models are pretty good match, and could be easily run locally or just for cheap via a hosted provider. The "how much data and how much improvement" is a question i dont have a good intuition for anymore. I dont even have an order of magnitude guess on those two axis.
Heres raw numbers to spark discussion:
| Model | DocType% | Year% | Subject% | In $/MTok |
|---------------|----------|-------|----------|-----------|
| llama-70b -----| 83 | 98 | 96 | $0.72 |
| gpt-oss-20b --| 83 | 97 | 92 | $0.07 |
| ministral-14b -| 84 | 100 | 90 | $0.20 |
| gemma-4b ----| 75 | 93 | 91 | $0.04 |
| glm-flash-30b -| 83 | 93 | 90 | $0.07 |
| llama-1b ------| 47 | 90 | 58 | $0.10 |
percents are doc type (categorical), year, and subject name match against haiku. just uses the first 4 pages.
in the old world where these were my own in house models, id be interested in seeing if i could uplift those nubmers with traingin, but i haven't done that with the new LLMs in a while. keen to get even a finger to the air if possible.
Can easily generate tens of thousands of examples.
Might try myself, but always keen for an opinion.
_edit for table formatting_