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🌌 Vega 1.6 128M Tetris

CNWPlayer

CNWPlayer
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AI & ML interests

SLMs, LLMs, really anything that ends in LM.

Recent Activity

reacted to DedeProGames's post with 🤗 1 day 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 DedeProGames's post with 🧠 1 day 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 DedeProGames's post with 🚀 1 day 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!
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