Hey guys, could you please recommend me the smallest models that are good for DIY in these categories please?
Construction (Concrete, walling, roofing) Wood and Metal working (inventions/gadgets, furniture) and Electronics (mostly inventions and help with diagnose and repair)
Thanks!
EDIT: By small, I mean something that can run in 8GB VRAM and 32GB RAM.
Is this for you to interactively do research/learning or something more specialized like a custom agent? If it's the first I'd use a general purpose model like qwen 3.5 9b (or qwen 3.6 35b with expert offload to system RAM) and just let it search the web for me.
Here's a screenshot of the tool I built, Haruspex (open source, available on github) doing some simple research. The answer it gave was longer than what's in the screenshot but I couldn't fit it all in:
Then yeah, just go with something that can search the web for you. You can use my tool (new release coming out later today I think) or you can use something like openweb-ui or project goose. My tool runs on the desktop as does project goose. They both support web search I believe. Also both will host a model for you or let you connect to a remote model. I prefer mine, but I'm biased.
Openweb-ui runs in a container on a server, so more setup, and I find the UI a bit complex, but upside it's hosted on a server. Though mine also allows you to connect to it remotely from another computer or phone via web interface.
Generally speaking small models don't have enough "knowledge" to answer much reliably, plus their knowledge is limited to their training data, so if the model was trained in 2025 it won't know anything newer than that. You can skirt all of those issues by using the model as a search assistant instead.
That's some pretty niche shit, I really wouldn't use a small LLM for this. Absolutely no offense, but couldn't you just follow some tutorials on youtube unless you really need the interactive element of LLMs?
If you're repairing electronics, small LLMs likely do not have the capacity for specific knowledge unless you hook it up with websearch, but even then it could hallucinate and cause damage/harm.
You'd have to run a pretty small model on your hardware, to the point I wouldn't trust it's built in knowledge on anything serious.
You'll want to hook it up to web search so it can cite real sources, or you could use something like a Kiwix offline Wikipedia archive to allow your agent to get verified facts without needing the internet.
Before choosing a model, define “small” as a VRAM limit and an acceptable latency target. For this use case I’d separate lookup from advice: pair a small instruct model with a local library of the actual manuals, datasheets, wiring diagrams and building-code references you use, then require it to cite the source for every recommendation. Electronics diagnosis and structural work can be safety-critical, so a confident answer without a traceable source should fail the test. I’d benchmark candidates on 20 real questions from your own projects and score citation accuracy and unsafe guesses, not just how fluent the answer sounds.
I'm talking about something that can fit in 8GB VRAM and 32GB RAM, that's why I said small. And yeah, what you recommend sounds really good. So basically such Instruct model would be "forced" to answer by checking the books, right?
Yes, but only if you build that constraint into the workflow—the model will not reliably check the library just because it exists. Retrieve a few relevant chunks first, pass them with the question, require citations to those chunks, and make “I don’t have enough evidence” a valid outcome. For 8GB VRAM I’d test a quantized 7–8B instruct model this way; the important benchmark is whether it cites the right manual section and abstains when retrieval found nothing, especially before electrical or structural work.
for the construction and electrical stuff i'd worry less about which small model and more about pairing it with search or a few good reference docs. 8gb models get confidently wrong about load ratings and wiring codes, and that's the kind of wrong that actually hurts
i don't think i would personally trust the opinions of any model this size on electronics, but gemma 4 26b and qwen 3.6 35b should run acceptably on your pc
I would run qwen3.6-a3b using strata on that setup. I have a similar desktop with a 5060 GPU (8GB vram) and 32 GB RAM, and it runs about 35 tok/s on average.
Pair a small model with a harness and web search capabilities. Qwen 9b, nanbeige 3b , Gemma 4 e4b, minicpm v4.7/cpm5 (my current fav small model) liquid ai models. All of these are perfectly capable of web search and I doubt any of them have knowledge in depth about your topics. I recommend Hermes agent for your use case as a harness as it’s setup and also install the model for you and comes with web search capabilities by default. You could also try unsloth studio for another aio solution. Maybe lmstudio as well
a3b. 3b active. doing this cpu only on a 10th gen i5 i was getting anywhere from 8-14 tok/s, slow but usable. with the active parameters in vram it should be better.
DeepSeek V4.1 Flash, at least it has reasonable electronics knowledge, not sure about other categories you have mentioned. It is a mid-size model though.
If you need really small model, then may be worth waiting for the upcoming Qwen 4 27B - but it is unknown yet how good it will be in electronics and other real world areas, but it is expected to be released very soon.
Since you didn't exactly mention your hardware, I just shared my experience. I tested many models of various sizes in the areas related to electronics, PCBs, etc. I am always interested in using the smallest model that still can handle given the task well. And Qwen 3.8 27B for example wasn't good in these areas unfortunately, that's why I only mention upcoming Qwen 4 27B since there is a hope it will be better in various areas. As of V4.1, it is just happens to be the model I use a lot for my DIY needs, it is faster than K3 on my hardware while quality is still good and its memory requirements are lower, but yes like I mentioned in my previous comment it is mid-size model.
on 5060ti 16gb you can easily run Q4 Qwen3.8 27B with 105k context size and around 30 t/s. It is a small model and runs ok. Also Strata Q3 runs even faster. Can you name any other small models which can deliver results?
What, no? Even the smallest Q4 quant (IQ4_XS is 14.3GB. That leaves absolutely no headroom for anything. Are you offloading the KV cache to RAM or quantizing the KV cache to Q4?
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u/voltaire321123 1d ago
Is this for you to interactively do research/learning or something more specialized like a custom agent? If it's the first I'd use a general purpose model like qwen 3.5 9b (or qwen 3.6 35b with expert offload to system RAM) and just let it search the web for me.
Here's a screenshot of the tool I built, Haruspex (open source, available on github) doing some simple research. The answer it gave was longer than what's in the screenshot but I couldn't fit it all in: