Fair warning, I have found local models and frontier models to be very bad at the specifics when it comes to cars.
Small differences like month and year model can impact oil capacity, oil weight and things like that, the details that matter quite a bit.
I found frontier models couldn't get things like what engine was in a 1994 Nissan Skyline, one of the more infamous and talked about cars on internet forums for decades, with dedicated fan databases that would have been scraped.
Questions like "what air filter do I need for my 1994 Suzuki Swift?" are hit and miss.
Yeah referencing is the way to go, as even finetuning probably captures style more than concrete facts. I know with large context windows we don't really RAG anymore, but for owner's manual lookup with a smaller model it seems ideal.
Something every LLM user ends up learning is that they're far better used as search and summarization tools than as knowledge databases in themselves.
Just a few hours ago I gave ChatGPT my window sticker and the installation manual for a new suspension setup. I asked for new hardware that would typically be replaced during this install, like torque-to-yield bolts and fasteners. I also asked for new oil filters. I got a comprehensive grid of the exact part numbers needed in a nice dense table. sol 5.6 high is my daily driver.
So far so good, hasn’t failed me yet. It’s done a stellar job chasing down parts for my cub cadet lawn mower too. Sorted out mid year model revisions and everything. I just gave it the sticker under the seat.
For prompts that needs fact-checking, I like these days to use Perplexity directly instead these days. It's way faster than the default websearch tool + give a link to the reference directly.
The setup here would be that your Sol would talk with CarWatch asking about the state of different car parts, service indicators and CarWatch would give a prioritized replacement list, and Sol could explore the detailed setup of your current car so the new suspension would be configured best. They could both ask you for more info on what type of driving you're planning.
So local and cloud agents figuring out the best solution together with none of your time needed.
Even so, in this case, author is using UD-Q3_K_S dynamic weights for Qwen3.6-35B-A3B, it will be dumb. Even the BF16 weights do stupid stuff like missing to confirm all parameters are defined when doing "rm -rf directory/$id", so it ends up deleting more than expected, I can't imagine the Q3 are actually useful for anything serious, even with tool calling or what not.
I've been very impressed by it's intelligence and lack of hallucinations. The dynamic Q3 is a good balance between accuracy and size keeping the 35B just below 16 GB. It is not supposed to know everything, it is your car. It actively disengages from off topic chatter (too slow for that anyway), better spend that time feeling the car.
It keeps itself grounded on sensor input. One principle per wheel. assert only what you can sense, claim only what is verified, label anything interim loudly, and report failure plainly with no silver lining. Everything above those four patches is just suspension.
> . The dynamic Q3 is a good balance between accuracy and size keeping
I'm having a hard time understanding how you find any sort of accuracy in Q3, when I use it with BF16 and it's hardly usable due to drastic hallucinations and inability for system prompt following. But, if it works for you, that's pretty good! Guess I'm jealous :)
Small differences like month and year model can impact oil capacity, oil weight and things like that, the details that matter quite a bit.
I found frontier models couldn't get things like what engine was in a 1994 Nissan Skyline, one of the more infamous and talked about cars on internet forums for decades, with dedicated fan databases that would have been scraped.
Questions like "what air filter do I need for my 1994 Suzuki Swift?" are hit and miss.