OrionLLM (Orion LLM Labs)

posted an update about 17 hours ago

Post

122

We will release the BEST SLM Leaderboard before Oct 11.

It will feature everything:
Easy to use model picker.
EXTREMELY Easy way to add your own models (2 click)
MULTIPLE leaderboard for different model types

And more!

So why don't you help us build it?

Join

betu-slm-leaderboard-testers

3 replies

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DedeProGames

posted an update 3 days ago

Post

5762

how is this possible

13 replies

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Banaxi-Tech

posted an update 4 days ago

Post

5141

ACR 1.0 launch is being prepared and researched now!
Also I'm going to vacation tomorrow but it should still be released!

saicr

3 replies

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DedeProGames

posted an update 6 days ago

Post

6262

🧱 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: DedeProGames/SLM-Tetris-Arena
📊 Results: DedeProGames/lm-tetris-arena-results

Want your model in the Ranked pool? Drop it in the comments!

1 reply

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Banaxi-Tech

posted an update 8 days ago

Post

7596

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.

11 replies

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DedeProGames

posted an update 9 days ago

Post

2875

🧱 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: DedeProGames/SLM-Tetris-Arena
📊 Results: DedeProGames/lm-tetris-arena-results

Want your model in the Ranked pool? Drop it in the comments!

1 reply

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DedeProGames

posted an update 10 days ago

Post

150

🚀 Training GPT-U-20M on 2.6B tokens

Banaxi-Tech

posted an update 10 days ago

Post

4864

We're excited to release BananaAll, our SLM Super App.

It allows you to do EVERYTHING you need to do to trains SLMs in a single app, no terminal, no 30 chrome tabs.

The train tab allows you to train models, select datasets from presets, and use other ones with auto mapping, model size slider, it automatically generates a training script for you.

Then after you've trained the model or want to compare it to competitors, the evaluation tab, run ARC EASY, ARC Challenge, Hellaswag, PIQA, Arithmark 3, BananaMind Base Bench and more! Simple Results screen.

And lastly the inference tab, run your trained models or others.

Normally you would need seperate apps or scripts for that, but the BananaAll Super App lets you do all of that in a single app.

We also trained a small 2.5M parameter model on 200M tokens of Fineweb edu, The results: BananaMind Base Bench 854 and 53% on PIQA. On only 200M tokens.

Check it out at https://github.com/BananaMind/BananaAll.

25 replies

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DedeProGames

posted an update 11 days ago

Post

95

🚀 Possible new model in the GRM-3.2 family — GRM-3.2-Mist

A new model may be joining the GRM-3.2 family, originating from an experimental finetune currently under evaluation.

If this model passes internal testing and demonstrates strong performance, it will be released as GRM-3.2-Mist — a portable 1.7B model targeting reasoning and agentic coding tasks.

The model already exists and is currently undergoing evaluation. Follow this page for further updates:

OrionLLM

Banaxi-Tech

posted an update 11 days ago

Post

1899

hi everyone
we have released nacr
its not just any model, its nacr
we have 6 more features and this model only uses 20% of its total capacity!
check it out at saicr/nacr
we're currently working on expanding access as we do more research but right now you have to use our gated access form

follow

saicr if you're interested
if you want to join saicr, first read the entire nacr readme, then press the join button.

14 replies

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Banaxi-Tech

posted an update 12 days ago

Post

1528

saicr is going to have its first model launch around October 2.
We're working so hard to get the models available as soon as possible.

7 replies

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DedeProGames

updated 3 models 13 days ago

posted an update 13 days ago

Post

101

This day is Sol nice.

DedeProGames

in OrionLLM/GRM-3.2-Sky 14 days ago

This is a good model, This is my assessment of the model

5

#8 opened about 1 month ago by

funnygeeker

Banaxi-Tech

posted an update 14 days ago

Post

103

We have some updates to @BananaMindBot 🍌
It can now train models, ask it to train a model, and i will train it for you.
It now can also merge PRs And like models.

28 replies

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DedeProGames

posted an update 15 days ago

Post

122

@BananaMindBot im alergic to trees

17 replies

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Banaxi-Tech

posted an update 15 days ago

Post

2755

We've released @BananaMindBot .

Most things you do on HuggingFace, BananaMindBot can do. Fast

Mention @BananaMindBot on a model, dataset, Space discussion, paper, blog comment, or top-level post and it'll reply there.

It's powered by North Code Mini (Qwen3.8 27B, with GPT OSS 120B as fallback).

A few things it can do:

Search for models and datasets
Look up users and orgs and see what they've published
Read model cards, configs, dataset files, blog posts, and org profiles
Answer questions about what it finds
Write and run its own code in a locked-down sandbox when it needs to verify something
Check things like a model's real parameter count from the safetensors headers instead of just repeating the model card
Remember something for later if you explicitly ask it to
Forward a message to @Banaxi-Tech
Post a daily roundup of developments in the small-language-model space

It won't execute code you give it. It can read and review that code, but anything it runs is code it wrote itself.

It also can't access private data or credentials.

Mention it somewhere.

It's going to also find this post!

(Some parts inspired by CompactBot and @CompactAI Follow them please)