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ErenAta00
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reacted to DedeProGames's post with 🔥 about 4 hours ago
🧱 SLM Tetris Arena: can a small language model play Tetris without ever being trained on it? I built an arena where tiny decoder-only LMs (50K–250M params) play Tetris zero-shot. There is no fine-tuning and no game data. They only use what they picked up from pre-training on text. How it works: - For every piece, the engine simulates each legal placement and describes the result in plain English ("clears one line, creates no new holes, keeps the stack low…"). - The model never sees the grid. It reads each description, and the arena compares log P(" good move") with log P(" bad move"). The best-rated placement is played. - Every player gets the same piece sequence, so it's a fair race. - There are two protocols: Guided (the rules are in the prompt) and Blind (no rules, only pre-training knowledge). Two ways to play: - Match: pick any models (even your own, custom architectures welcome) and watch them play side by side on retro 8-bit boards. - Ranked: press Play and the arena picks up to 4 models at random from a curated pool of 29. Nobody chooses their opponents, so Elo can't be farmed. Matches run on the server and count even if you close the tab. First results (~225 ranked matches): - gpt2 (124M) leads with 1283 Elo, but SupraNeo-4M (4M) is right behind at 1239. Next come LowOnMind-5M and BananaMind-2.1-Pico (1.5M!). - Model size barely predicts Elo (r ≈ 0.06). Survival does (r ≈ 0.9): the models that avoid holes and keep the stack low are the ones that win. Every ranked match (seed, model commit SHAs, scores, Elo before/after) is logged in a public dataset. ▶ Play: https://huggingface.co/spaces/DedeProGames/SLM-Tetris-Arena 📊 Results: https://huggingface.co/datasets/DedeProGames/lm-tetris-arena-results Want your model in the Ranked pool? Drop it in the comments!
reacted to theirpost with 🔥 about 7 hours ago
Maverick-4B-Unity-XR-Agent is now on Hugging Face. It's a 4B model that turns spoken or typed English into actions in Unity scenes. Say "put the red mug on the table" or "turn on the lamp", and it returns the tool call your app executes. If a command could mean two objects, it asks which one. If it can't do something, it says so instead of guessing. Everything runs on the user's machine through llama.cpp: no API key, no internet connection. The Q4_K_M GGUF is 2.5 GB and needs about 3 GB of GPU memory, so it fits on a 4 GB laptop GPU and usually answers in one to three seconds. It is fine-tuned from Qwen3-4B with QLoRA on about 20,000 English conversations. Results: - 83.8% on 499 human-written ALFRED instructions (right action on the right object). The base model, Qwen3-4B, scores 57.1%. The strongest of the five other models we tested, from 1.7B to 120B parameters, was Ministral 3 14B at 67.1%. - 91.7% on object types it never saw in training. - 97.3% on 440 commands run through a live Unity scene. There is also a Unity package that starts the model, describes the scene to it and carries out its tool calls. You install it from the Package Manager with a Git URL. Model: https://huggingface.co/ErenAta00/Maverick-4B-Unity-XR-Agent-GGUF Unity package: https://huggingface.co/ErenAta00/Maverick-Unity Full write-up: https://huggingface.co/blog/ErenAta00/maverick-4b-unity-xr-agent Built at the Extended Reality Laboratory (XRLab), Manisa Celal Bayar University: https://huggingface.co/ExtendedRealityLabMCBU Released under Apache-2.0. Feedback and bug reports are welcome in the Community tab.
liked a model about 7 hours ago
ErenAta00/Maverick-Unity
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