Hoglet (Ash) PRO
Hoglet-33
AI & ML interests
Open source AI, datasets, parameter efficiency, SLMs, AI for the betterment of humanity. Contact at ash@basicallyai.co
Recent Activity
reacted to DedeProGames's post with 🔥 about 1 hour 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! repliedto DedeProGames's post about 1 hour 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! repliedto Banaxi-Tech's post about 2 hours ago
We're releasing a MAJOR update to the BananaAll SLM Super App.
If you want to use a custom architecture, previously you had to go trough reviewing the code yourself, now add an Openrouter API key and review it with GPT 6 Luna in one button. A review cost be half a cent so anyone can try it. This is one of the main features.
Now ROCm, AMD and Windows, Mac support.
Colab and Molab support.
Detailed list of features:
Get improved Windows Python detection and support paths for compatible AMD ROCm, Intel XPU, and Apple MPS setups.
Choose local training or export a self-contained Python script for Colab or Molab. Notebook runs produce a downloadable model ZIP.
Start pretraining with an existing model’s tokenizer, or train a new one from your datasets.
Try experimental 1.58-bit Ternary fake-quantized training on NVIDIA GPUs.
Watch live tokens per second. Model compilation is on by default and falls back automatically if it fails.
Build custom architectures with separate configuration and modeling files, then review the training code manually or with optional OpenRouter AI Review.
Install from source with the new coding-agent instructions.
This release also fixes inflated loss reporting for custom models.
And for those users who didn't want to try it out just because installation would be so hard, it isnt now.
Go to any coding agent (Pi, Claude Code, Codex, OpenCode, basically all work), and just paste "Install BananaAll for me. Fetch and follow https://raw.githubusercontent.com/BananaMind/BananaAll/main/agent_install.txt."
That's it.
Check it out at https://github.com/BananaMind/BananaAll/
Also on SAICR, we're currently training a new major model (NACR v2) and ACR 1.0 is in the finishing.
Organizations
Add Hogleto
8
#170 opened about 18 hours ago
by Hoglet-33
Hello beta testers.
5
#1 opened 1 day ago
by 
Bc-AI
YOO WHAT?
57
#1 opened 5 days ago
by 
Bc-AI
ORION FLAGSHIP MINI PREVIEW IS HERE!
9
#1 opened 4 days ago
by Bc-AI
#general
82
#5 opened 7 days ago
by 
GGUFGuy
yeah
9
#1 opened 10 days ago
by 
Banaxi-Tech
Adding Pebble.
1
#132 opened 20 days ago
by Hoglet-33
Missing Transformers library from the metadata
#3 opened 28 days ago
by 
FlameF0X
Holy sh, a small language model that is mamab!
👀 1
10
#1 opened 28 days ago
by FlameF0X
Fixed the benchmark scores.
#2 opened 28 days ago
by 
Hoglet-33
Data Distribution
1
#1 opened about 2 months ago
by Hoglet-33