u/paashabhai

Game Title: Mercuro

Playable Link: http://apps.apple.com/app/id6762402072

Platform: iOS (iPhone and iPad). Available on the App Store.

Description: Mercuro is a thermometer logic puzzle played on a grid of connected tubes. Every tube fills from its bulb upward, and your job is to fill them so that the number of filled cells in each row and column matches the clue printed along its edge. It is a game of pure deduction: there is no guessing, and every board has exactly one valid solution, so you can always reason your way to the answer. There are a few fresh puzzles every day, plus a large set of handcrafted levels that gradually increase in difficulty. Mercuro is fully offline and has no subscription.

I have tried to keep it polished and as least-annoying as possible, with a global leaderboard, achievements to chase, a hint engine for when you get stuck, and unlockable themes that restyle the whole board. I am a solo developer, and I mostly want honest feedback: what is confusing, what feels off, and what you would want more of.

Free to Play Status: Free to play

Involvement:
Solo developer.

I trained a 12B with one goal: prose that doesn't fall into the usual LLM tics. Sharing it here since this crowd will put it through real use.

• Model Name: Ozan-v1-12B
• Model URL: Ozan-v1-12B (full precision) · GGUF quants (Q4–Q8)
• Model Author: arbazsiddiqui (me — I made this)
• What's Different/Better: It's built and measured for low slop. The over-used tells like "barely above a whisper," "a testament to," the reflexive "not just X, but Y." On the EQ-Bench Creative Writing v3 slop metric it's the lowest-slop runnable 12B I tested (slop 5.30 over 96 stories), with the cleanest repetition of the field, so it holds up over long, multi-turn writing instead of drifting into purple mush. It writes ~1000-word turns naturally, native Mistral [INST], and it'll handle mature themes. Best judged by reading: there are 3 full unedited samples (with prompts) on the model card.
• Backend: koboldcpp (GGUF). Also runs on llama.cpp / Ollama / LM Studio. I run Q5_K_M for a good size/quality balance (Q4_K_M is the lighter default; Q6_K/Q8_0 if you have the VRAM).
• Settings (SillyTavern):
  • Instruct + Context template: Mistral (native [INST] … [/INST])
  • Temperature: 0.7
  • Min-P: 0.1
  • DRY: multiplier 0.8 / base 1.75 / allowed-length 2 (keeps long outputs clean — recommended on)
  • No special system prompt needed; no length-forcing needed.

How it was made (open): SFT on curated low-slop prose, then a Gutenberg anti-slop DPO pass. Full pipeline + the before/after numbers are open (Apache-2.0): github.com/arbazsiddiqui/Ozan

Honest caveats: "slop" is one axis of quality, not the whole story; it's a 12B, so it's lighter on emotional depth and surprise than bigger models. Read the samples and judge for yourself.

Feedback very welcome, this is my first time training any lora or finetuning, please let me know what can be/have been improved 🙏