# 2/184 to 170/184: one epoch on two R9700s took a 14B coder from useless to 5 points behind gpt-5.6-sol > Source: > Published: 2026-09-12 12:43:09+00:00 Been a while. Last time I posted the dataset had passed 300k entries and I had one 14B fine tune with 81% structural correctness. Since then I did the thing I should have done from the start, I built a proper benchmark and published it so people can check my numbers instead of taking my word for it. What got published: 184 test prompts for MQL5 (the language for algo trading bots), the scoring rules, per item results for every model, and a verifier script that checks the release hashes and recomputes the headline numbers from the rows. A pass is zero compiler errors and a built binary. Thats it. Compiling is a low bar and the card says so, it does not mean the bot trades well. The numbers: Base Qwen2.5-Coder-14B-Instruct, untouched: 2 of 184 compiled. Same model after one epoch on my data: 170 of 184. gpt-5.6-sol on the same 184 prompts: 179 of 184. So the local 14B went from useless to about 5 points behind a frontier API model on my niche. One epoch. I am not going to pretend thats the finish line but it is the first number I have that somebody else can reproduce, and that matters more to me than the number itself. Hardware, since this is L1T: Everything is still AMD and still local, but the layout changed since April. The main rig is now a 7950X3D with 64GB and one Radeon AI PRO R9700 in it, the 9800X3D and the 9070XT are out. That box is where both local arms of the benchmark were run, base model and tuned model, same card, same settings. The Threadripper 2970WX has two R9700s now and does the training under ROCm. So three R9700s total across two machines, 96GB of VRAM, no CUDA anywhere. I fought ROCm plenty (some of you saw the Windows thread) but it does the job and the numbers came out of it. Links: Hugging Face: GitHub mirror: What is not in the release is the training data and the tuned weights. What is in the release is every prompt and every result row, so you can run your own model against the same 184 and compare. Caveat thats also on the card: the test prompts come from the same generator family as the training data, so this measures how good the model got at my kind of spec, not how it does on random human written requests. I have a bigger private holdout in the works and a version 1.1 of the card coming with those results. Still grinding. If you are on AMD and want to compare notes on ROCm training or eval, ask away. MQL5 is a trademark of its owner. Not affiliated, independent project.