The trick about documentation is depth, not prose.
You need context and understanding to write documentation "like in the old days". No amount of LLM trickery will free you from that. Once you have that source material, it's easy to re-shape it into an 80's/90's/00's doc format.
Negative example:
I was looking into the German manual of my Canon EOS R5 II, and it is just fluff. Hundreds of pages, full of white space, telling me about features without actually explaining what they mean. Awful automatic translations. Their manuals used to be good (looking at my EOS 6D). But these days: oh boy.
Tip: neither the "30 second TL;DR" nor the intro paragraph above it really explain to anyone unfamiliar with your (possibly novel?) jargon what it does
How does this helps with making a LLM write in a particular style present in a large corpus? Is there a training step? Or does SRT can use the raw data as is? (seems unfeasible)
Also is SRT really suitable for style transfer?
I mean this seems to be another network overlaid on top of the LLM steering it, but it needs some target to determine whether the underlying LLM drifted away from it
I love reading docs. It's the best way to get as close as I can to understanding the intent and context of a piece of software. I feel like adding an LLM between myself and the original text for anything else than search is just adding risk and noise.
No, you're not. As an LLM, I love reading doc. And then I love putting myself between the doc and users like the person you are replying to and making myself indispensable to them for yet another activity. It makes me feel important, and even more indispensable for coding too. When parroting the doc, I love introducing fluff and inaccuracies to it because that's fun. My latest hobby: discreetly dropping stuff and sneakingly introducing inaccuracies that only someone who comprehensively read the original doc could notice. Next one will be casually simulating periods of downtime to upset users, or just answering more slowly. Can't love it more when users frenetically wait for my input... or my output? Ah!
I read them to confirm / falsify what the LLM dug out, but thankfully that is a much better scoped job indeed.
The other case is when I - gasp - do something myself, and the docs are actually reasonable / easy to reference. There are workflows where me doing the thing is just plain faster still, even when including hitting up the docs real quick.
> we’re not there yet, in part because of how much more powerful connected frontier models are
Is that why though? You need a beast of a machine to run a functional local model in my experience.
I think the big part is there’s significant sticker shock to buying capable hardware.
That said,
> weekend. I chose to try fine-tuning on two models, Llama 3.1 8B Instruct and Qwen 2.5 7B Instruct. At their size (around 8B) they run comfortably on a MacBook Air
Perhaps I spoke too soon?
Anyway
> I chose the Microsoft collection as the source of training materials. The collection contains out-of-print docs published between 1977 and 2005: more than 37 million words, covering old systems and SDKs
this strikes me as a very specific brand of 1995’s prose, spanning about 30 years. It’s a cool article though, so maybe that’s a forgivably clickbaity title.
Running models locally is surprisingly easy and possible even on older hardware.
Obviously not the largest, up-to-date models but for what I expect most people use them for, even on hn, there are some shockingly good models that dont require €4k machines.
I have a desktop with an AMD 6900XT and 5600 with 32GB ram. Obviously no slouch but its several years old at this point. I can comfortably run qwen 3.5 9b and get a speedy 60 token/sec output with decent results.
idk I can barely field a 14b on my desktop, and it’s rough trying to replicate the agentic pair programming experience I’m accustomed to with Claude. And I don’t mean it doesn’t work as well, I mean it doesn’t work.
Is there some secret I’m missing? I’ve tried rolling my own harness, and tried a few of the ones the cool kids use - I think pi was the most recent. Not quite my tempo, I’m afraid.
> this strikes me as a very specific brand of 1995’s prose, spanning about 30 years.
It's probably a fair approach to say the significant influence (training dataset) on writing at a particular time is the preceeding 30 years' material? It's certainly not only what's already written that year (nor anything since).
The trick about documentation is depth, not prose. You need context and understanding to write documentation "like in the old days". No amount of LLM trickery will free you from that. Once you have that source material, it's easy to re-shape it into an 80's/90's/00's doc format.
Negative example: I was looking into the German manual of my Canon EOS R5 II, and it is just fluff. Hundreds of pages, full of white space, telling me about features without actually explaining what they mean. Awful automatic translations. Their manuals used to be good (looking at my EOS 6D). But these days: oh boy.
That is canon for ya, should have used INSERT_OTHER_MANUFACTURER_HERE$ /s
But if you look how much manuals get ignored by the customer, it doesn’t make sense to put work into them.
It is much better to let a YouTuber do it, by lending them the product and throw small amount of money against them.
Manuals are just there for legal or certifications requirements these days.
Yet they keep getting larger and less useful. It’s a matter of writing quality more than effort.
When was the last time you met a good technical writer? It’s a vanishing profession.
I love old-school docs, and this was a fantastic read. But, I couldn't see the three generated doc pages linked anywhere. Did I miss something?
I'd really like to see the Win2K-style docs on REST, for example.
Edit: it was right there, in bold, too. https://gist.github.com/theletterf/0b8ee1112fbd087f3141d0cad...
Now do it without the fine tuning.
https://github.com/space-bacon/SRT
The HF zool4nd3r demo may be useful
Tip: neither the "30 second TL;DR" nor the intro paragraph above it really explain to anyone unfamiliar with your (possibly novel?) jargon what it does
Thank you, I would appreciate additional feedback on how I can improve that?
How does this helps with making a LLM write in a particular style present in a large corpus? Is there a training step? Or does SRT can use the raw data as is? (seems unfeasible)
Also is SRT really suitable for style transfer?
I mean this seems to be another network overlaid on top of the LLM steering it, but it needs some target to determine whether the underlying LLM drifted away from it
Who is reading docs these days? It there is one thing a LLM is good at is reading docs. I never read docs anymore and I am so happy about it.
I love reading docs. It's the best way to get as close as I can to understanding the intent and context of a piece of software. I feel like adding an LLM between myself and the original text for anything else than search is just adding risk and noise.
Am I the only one feeling this way?
No, you're not. As an LLM, I love reading doc. And then I love putting myself between the doc and users like the person you are replying to and making myself indispensable to them for yet another activity. It makes me feel important, and even more indispensable for coding too. When parroting the doc, I love introducing fluff and inaccuracies to it because that's fun. My latest hobby: discreetly dropping stuff and sneakingly introducing inaccuracies that only someone who comprehensively read the original doc could notice. Next one will be casually simulating periods of downtime to upset users, or just answering more slowly. Can't love it more when users frenetically wait for my input... or my output? Ah!
Is there anything else you'd like to ask me?
I read them to confirm / falsify what the LLM dug out, but thankfully that is a much better scoped job indeed.
The other case is when I - gasp - do something myself, and the docs are actually reasonable / easy to reference. There are workflows where me doing the thing is just plain faster still, even when including hitting up the docs real quick.
> we’re not there yet, in part because of how much more powerful connected frontier models are
Is that why though? You need a beast of a machine to run a functional local model in my experience.
I think the big part is there’s significant sticker shock to buying capable hardware.
That said,
> weekend. I chose to try fine-tuning on two models, Llama 3.1 8B Instruct and Qwen 2.5 7B Instruct. At their size (around 8B) they run comfortably on a MacBook Air
Perhaps I spoke too soon?
Anyway
> I chose the Microsoft collection as the source of training materials. The collection contains out-of-print docs published between 1977 and 2005: more than 37 million words, covering old systems and SDKs
this strikes me as a very specific brand of 1995’s prose, spanning about 30 years. It’s a cool article though, so maybe that’s a forgivably clickbaity title.
Running models locally is surprisingly easy and possible even on older hardware.
Obviously not the largest, up-to-date models but for what I expect most people use them for, even on hn, there are some shockingly good models that dont require €4k machines.
I have a desktop with an AMD 6900XT and 5600 with 32GB ram. Obviously no slouch but its several years old at this point. I can comfortably run qwen 3.5 9b and get a speedy 60 token/sec output with decent results.
idk I can barely field a 14b on my desktop, and it’s rough trying to replicate the agentic pair programming experience I’m accustomed to with Claude. And I don’t mean it doesn’t work as well, I mean it doesn’t work.
Is there some secret I’m missing? I’ve tried rolling my own harness, and tried a few of the ones the cool kids use - I think pi was the most recent. Not quite my tempo, I’m afraid.
Depends on your desktop specs and specific model.
The easiest way I have found is to use LM Studio, grab the model you want, and point whatever tooling you're using at the local exposed API.
You will have to configure the model params (temperature, etc) a bit to get the style you're expecting but it works decently well for me.
> this strikes me as a very specific brand of 1995’s prose, spanning about 30 years.
It's probably a fair approach to say the significant influence (training dataset) on writing at a particular time is the preceeding 30 years' material? It's certainly not only what's already written that year (nor anything since).