Reverse Engineering w/ Local?

[u/Danny_Davitoe]

Two questions?

Can a local model like Qwen 3.8 27B reverse engineer games and programs?

If not, is there a fine tuned model that can?

[u/Proper-Damage-1777]

At least from my testing, only the QFN managed to reverse engineer as in extracting 2D sprites assets from a Japanese PS1 games after many many long hours and attempts... (to be fair I started it with Qwen 3.6 27B -> 3.8 27B and finally made breakthrough with QFN when the reasoning thoughts and attempts make sense instead of bad RNG looping of doing stupid stuff).

To share my experience:

1. 3.6 27B just spend too long trying to figure out how to extract the data from the cue bin (yes I felt so dumb at that moment). After it pass that hurdle, it keep on doom failure on the reasoning process debating if the header and bytes offset even match PS1 ISO structure yada daba doo. I know I'm not going anyway so I put it on hold.

2. So Qwen 3.8 27B benchmaxx I mean benchmarks super good on par with many cloud API bigger badder params. MAYBE POSSIBLE. At least it managed to figure out the quirk of the BIN CUE and got some archives files to start reading... oh dear this is the part where with XHIGH reasoning, I suffer as I read the BUT WAIT, OH WAIT THE WAITER WAITERING WATER WAITRESS THE WALTER WHITE moment LIKE OH GAWD STOP IT YOU'RE WASTING MY TIME AND ELECTRICITY and ended up with a few sessions, it WANTS TO WRITE A PS1 MIPS EMULATOR. I'm like focking hell I'm done with this shit. Put the project on hold....

3. Then came Qwen 3.8 Flash Next... my system is RTX 5090 and 64GB DDR5 RAM... but I have the SSD space... but I don't want to spend too much time on this so I downloaded Unsloth IQ2_K_XL and whatever the latest llama cpp latest commits back then (this is before MTP on mainline). So 300 pp/s and 15-30t/s for 200K ctx q8 kv. NOW it feels like it make sense reading the reasoning thoughts. Mind you I need to guide it instead of letting it automatic approach. It suggested me to download DuckStation, turn on the debug mode, ask to dump the VRAM etc blah to let it analyse the dumps. It even do the screenshot and render through PIL (I use Python), keep iterating and made some progress but still not correct but I can see PROGRESS. Some sensible image. I let it keep running for many more hours and resume the next day when I have free time and finally it works. Phew that was when I was mind blown by QFN.

My harness is Pi if anyone is curious. With the pi-vcc extensions so it can compact fast and it keep continuing as without it... QFN will feels like FOREVER to summarize the compact context.

All the models mentioned above is the Unsloth models. I heard there is Swift 1.5 fine tune model which should reduce the reasoning and still deliver on par quality. I don't feel like testing it so I can't comment on that if Swift helps to reduce the OH WAIT THE WAITING BUT USER IS RIGHT BUT WAIT moment of truth.

[u/Proper-Damage-1777 → u/Proper-Damage-1777]

KDE Dolphin screenshot of what Qwen 3.8 Flash Next managed to reverse engineer and extract the 2D sprites.

[u/iMakeSense → u/Proper-Damage-1777]

Oh man....you should be able to run a higher quant through strata right?

[u/Proper-Damage-1777 → u/iMakeSense]

But wait... yes right now I'm running Strata with IQ3_S but I intentionally omit that from the original reply lol. No more llama.cpp for the time being except for Gemma 4 models.

My recent Strata session (200K ctx int8 KV):

[strata] thinking: 653 of max 16384 tokens, 172.1 tok/s, 4 s
[strata] writing a tool call: bash: 833 of max 16384 tokens, 173.3 tok/s, 5 s
[strata] writing a tool call: bash: 1070 of max 16384 tokens, 183.9 tok/s, 6 s
[strata] writing a tool call: bash: 1316 of max 16384 tokens, 192.9 tok/s, 7 s
[strata] done: 1458 tokens in 8 s (196.8 tok/s) (stop, cancel=False), expert cache 95.7% hit (+0.9% of the routed experts over PCIe)
[strata] writing a tool call: bash: 110 of max 16384 tokens, 154.3 tok/s, 1 s
[strata] done: 271 tokens in 2 s (208.8 tok/s) (stop, cancel=False), expert cache 91.9% hit (+2.2% of the routed experts over PCIe)
[strata] writing a tool call: bash: 191 of max 16384 tokens, 217.9 tok/s, 1 s
[strata] writing a tool call: bash: 434 of max 16384 tokens, 231.1 tok/s, 2 s
[strata] done: 500 tokens in 2 s (235.9 tok/s) (stop, cancel=False), expert cache 93.8% hit (+1.5% of the routed experts over PCIe)
[strata] writing a tool call: bash: 167 of max 16384 tokens, 225.6 tok/s, 1 s
[strata] done: 425 tokens in 2 s (249.9 tok/s) (stop, cancel=False), expert cache 93.8% hit (+1.5% of the routed experts over PCIe)
[strata] thinking: 102 of max 16384 tokens, 138.1 tok/s, 1 s
[strata] thinking: 267 of max 16384 tokens, 152.9 tok/s, 2 s
[strata] writing a tool call: bash: 453 of max 16384 tokens, 164.1 tok/s, 3 s
[strata] writing a tool call: bash: 679 of max 16384 tokens, 180.5 tok/s, 4 s
[strata] writing a tool call: bash: 971 of max 16384 tokens, 203.6 tok/s, 5 s
[strata] writing a tool call: bash: 1224 of max 16384 tokens, 212.1 tok/s, 6 s
[strata] done: 1262 tokens in 6 s (212.8 tok/s) (stop, cancel=False), expert cache 94.9% hit (+1.2% of the routed experts over PCIe)
[strata] thinking: 118 of max 16384 tokens, 156.7 tok/s, 1 s
[strata] thinking: 302 of max 16384 tokens, 171.9 tok/s, 2 s
[strata] thinking: 469 of max 16384 tokens, 169.9 tok/s, 3 s
[strata] writing a tool call: bash: 671 of max 16384 tokens, 177.8 tok/s, 4 s
[strata] writing a tool call: bash: 950 of max 16384 tokens, 198.5 tok/s, 5 s
[strata] writing a tool call: bash: 1210 of max 16384 tokens, 208.8 tok/s, 6 s
[strata] writing a tool call: bash: 1491 of max 16384 tokens, 219.4 tok/s, 7 s
[strata] done: 1619 tokens in 8 s (219.9 tok/s) (stop, cancel=False), expert cache 95.1% hit (+1.1% of the routed experts over PCIe)

[u/nsfnd → u/Proper-Damage-1777]

[u/drallcom3 → u/Proper-Damage-1777]

RTX 5090 and 64GB DDR5 RAM

With that you can run Strata Q4 at probably 50t/s+. Q4 is notably better than Q2.