OrionLLM (Orion LLM Labs)

posted an update about 5 hours ago

Post

55

Hi everyone, we're back!

And we're releasing something major again now, if you have seen Grok Bot, its very powerful, but there isn't really a local alternative yet.

Today we're releasing Minds, they're personal AI bots that live on your computer.
They can run anywhere, on your phone, on your mac, on windows, and even on linux.

They support OpenAI Oauth as the main integration because most people already have a ChatGPT sub.

But locally, It supports ANY openai compatible api which allows you to setup ollama and llama.cpp and everything else.

It uses Jev for command approval on OpenRouter and Muse Spark Transcribe for transcription.

Its fully Open Source!

Check it out: https://github.com/BananaMind/Minds

DedeProGames

posted an update about 20 hours ago

Post

1052

🚀 Introducing OpenWork — Deploy agents everywhere.

OpenWork is an open-source AI agent workspace built on top of OpenCode.

Deploy autonomous agents, schedule tasks, manage projects, and let AI work for you — directly from your terminal.

Features:

- 🤖 Deploy autonomous AI agents
- 📅 Schedule recurring tasks
- 🧠 Persistent memory and workspaces
- 🔌 MCP integrations and custom tools
- 📥 Agent Inbox and task management
-⚡ Support for local and cloud AI models
- 🖥️ Interactive terminal interface (TUI)

Your agents. Your models. Your workflow.

Fully open-source. MIT licensed.

⭐ GitHub: https://github.com/dedeprogames-official/openwork

#OpenSource #AI #AIAgents #OpenWork #OpenCode

Banaxi-Tech

posted an update 1 day ago

Post

3023

Well GPT X3 takes the lead. As of right now.

We are now announcing BananaMind 3 🍌! (not ai for those emoji guys)

All models will use BGA (which is almost just NSA) and our BM3X architecture.
Its sizes will be:
BananaMind 3 Flash Lite, 3M parameters at a context of 8K context.
BananaMind 3 Lite, 10M parameters with 16K context.
BananaMind 3 Flash, 25M Parameters with 16K context.
BananaMind 3 Pro, 50M parameters with 24K context.
BananaMind 3 Ultra, 100M parameters with 32K context.
And lastly, BananaMind 3 Max with 150M parameters and 64K CONTEXT.

I can assure you BananaMind 3 Max WILL beat GPT X3 or match it, we won't release it otherwise. We hope for a 40+ INTELLIGENCE INDEX!

BananaMind 3 may also be partnered with dot labs.

We will cancel BananaMind 2.1 and BananaMind 2 Ultra.

As of the BETU SLM Leaderboard we may need to release it after October 11, im very busy right now (even though we said We will release BETU leaderboard before Oct 11 😟)

11 replies

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Banaxi-Tech

posted an update 2 days ago

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3144

We have released BGA!
And wow, It provides 256x (and 512x at the end of 1M) yes 256x LESS attention compute at 1M context window.
That means you can train a 1M context window at the compute of a ~4K context window.

Check IT OUT: BananaMind/blog

The Accuracy Should BE WAy better than DSA but untested yet.

And, now some updates on BananaMind 3:

BananaMind 3 Will start training Soon!
Sizes: 10M, 25M, 50M, 100M, 150M

And the context windows ARE INSANE: 10M, 16K context, 25M 16k context, 50M 32K context, 100M and 150M, 64K context!!!!

13 replies

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Banaxi-Tech

posted an update 3 days ago

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127

Hi! We're right now experimenting with so many new architectures!

We are also going to release BananaMind Gate Attention very soon, its a new attention that can make attention 128x cheaper at 1M Context! Check my account for HF blogs it will release there!

Banaxi-Tech

posted an update 5 days ago

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82

We're introducing ACR 1.0.
We trained this model on a 5070 Ti for weeks, here are some of the architecture details:
57M parameters, with one M and one G stream.
When we tested it on benchmarks, we got these results:
Benchmark Full G-Only Delta
PIQA 62.24% 53.43% +8.81
ARC-Easy 41.96% 32.28% +9.68
HellaSwag 33.19% 29.08% +4.11
Tiny ToM 40.65% 33.75% +6.90
ArithMark 3.0 33.40% 32.80% +0.60
Base Bench 1.1 51.71% 40.29% +11.42

Check it out at saicr/ACR-1.0

3 replies

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DedeProGames

posted an update 5 days ago

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3999

Im working on a 23M ASR model, trained on 100k hours of audio

5 replies

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Banaxi-Tech

posted an update 6 days ago

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164

Its SAICR time tomorrow.
Get ready!

saicr

28 replies

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Banaxi-Tech

posted an update 7 days ago

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3061

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 9 days ago

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5825

how is this possible

13 replies

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Banaxi-Tech

posted an update 10 days ago

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5162

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 12 days ago

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6286

🧱 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 14 days ago

Post

7616

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 14 days ago

Post

2889

🧱 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 16 days ago

Post

152

🚀 Training GPT-U-20M on 2.6B tokens

Banaxi-Tech

posted an update 16 days ago

Post

4872

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 17 days ago

Post

96

🚀 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 17 days ago

Post

1901

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.