Also have preserve thinking on, it helps it stay consistent in multi turn work.
Which model version you’re using can also affect results, usually unsloth’s ones are good.
With all that said, it’s of course a small model so it’s not a super coder. The 27b is better (I’d guess 25-35% better), but of course still a small model so…
So it’ll maybe not be good enough still, but should give it the chance to let it do the best it can :)
So - I setup that model according to the docs and gave it this prompt:
Write me a highly optimized n-queens solver in go. It should take advantage of parallelism (what little there is) and output only the solution and how long it took.
After 10 minutes it gave me code that didn’t compile.
It took another 3 mins to fix the compile error and the output is not correct.
As I said - LLMs on 8Gig VRAM just aren’t worth it.
When you run it, do you use unsloth’s recommended settings for coding?
https://unsloth.ai/docs/models/qwen3.6
Also have preserve thinking on, it helps it stay consistent in multi turn work.
Which model version you’re using can also affect results, usually unsloth’s ones are good.
With all that said, it’s of course a small model so it’s not a super coder. The 27b is better (I’d guess 25-35% better), but of course still a small model so…
So it’ll maybe not be good enough still, but should give it the chance to let it do the best it can :)
So - I setup that model according to the docs and gave it this prompt:
Write me a highly optimized n-queens solver in go. It should take advantage of parallelism (what little there is) and output only the solution and how long it took.After 10 minutes it gave me code that didn’t compile.
It took another 3 mins to fix the compile error and the output is not correct.
As I said - LLMs on 8Gig VRAM just aren’t worth it.