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.
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.
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.
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.
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)
Hi everyone! We've seen some people getting confused with the BananaMind Leaderboards so ill explain!
We have 2 leaderboards, THESE are NOT the same, first BananaMind/BananaMindBench-Leaderboard which is ONLY for BananaMind Base Bench 1.1. The 10/10 scores do NOT mean that the benchmark is saturated. It isnt saturated, these models score 10/10 because they are the current best models, our /10 ranking system works by taking the ELO scores and then comparing them to the scores in the same size range. So if a better model releases that gets 10/10 and the others get lower.
And we also have the BananaMind SLM leaderboard, not the BananaMindBench leaderboard which uses ARC EASY,PIQA,Hellaswag, Arithmark 3 and the BananaMind Base Bench 1.1. This is the newer and recommended version.
We're releasing the BananaMind SLM Leaderboard! It offers a easier look at which models are actually good for your specific needs. Its primary metric, Intelligence index is a composite of BananaMind Base Bench, PIQA, Hellaswag, ARC Easy and Arithmark 3. It also allows you to see specific categories like Commonsense on a model.
We're releasing BananaMind Arena. Its a Huggingface space where you can test out different models and see they're rankings! Check it out at Banaxi-Tech/BananaMind-Arena
Also please follow @CodeSoft for inspiring me to make it.
We're going to release our BananaMind 2.1 models very soon! We're also announcing 2 new models.
All of our models we will train are: BananaMind 2.1 Flash Lite, 10M parameters with 8M in transformer and 2M in n-gram. 50B pretraining tokens. BananaMind 2.1 Lite with 25M parameters, 5M in n-gram and 20M in transformer. 75B pretraining tokens. BananaMind 2.1 Flash with 50M parameters, with undecided n-gram count yet. 100B pretraining tokens. BananaMind 2.1 Pro with 145M parameters, with undecided n-gram count yet. 150-200B pretraining tokens. BananaMind 2.1 Coder with 149M parameters with undecided n-gram count yet. We're now announcing BananaMind 2.1 NanoCoder, a 10M parameter model focused specifically on coding and BananaMind 2.1 MiniCoder which is a 25M parameter model focused on coding.
We're excited to release BananaMind 2.1 Pico Preview!
It includes the first preview of our BananaMind 2.1 architecture! This model gets near BananaMind 2 Micro performance at half the parameters and 37.5x less tokens! Thats insane!
The current architectures includes about 500K parameters of the total 1.5M parameters in n-gram embeddings and the layer 2 is run twice.
It also includes XSA and the XSA refresh gate.
We're still going to improve the architecture in the final release.
- Complete modern UI redesign - Adds support for Qwen3.5 0.8B, LFM2.5 230M,350M, SmolLM2 360M, Gemma 3 270M. - Adds Q7,Q6,Q5,Q3,Q1 quantization formats with a easy to use precision slider - And more!
The new UI includes: - New 1024×768 High Quality interface. - Photographic QOI background. - Transparent BananaMind, CPU, cube, mouse, and Send icons. - Proper bitmap cursor. - Rounded translucent panels and cards. - Modern model-loading progress window. - Redesigned inference screen with response and prompt panels. - Localized redraws for the cursor, clicks, loading progress, and precision slider.
Notice: Qwen3.5 0.8B currently generates garbled text, it will be fixed tomorrow.
We're delaying BananaMind 2.1! When BananaMind 2.1 Lite was almost done, we benchmarked it and the results we're worse than BananaMind 2 Mini.
We're going to spend alot more time in research on tiny models and then scaling up our techniques to the actual BananaMind 2.1 models!
We're also announcing these new models: BananaMind 2.1 Coder: A 149M instruction tuned coder model trained on 75B tokens + 10B tokens of stack-v3-train. BananaMind 2.1 Pico: A 1M parameter model trained on 22B tokens of data. We also may release BananaMind 2.1 Large with around 100M parameters depending on how much compute we have.
We have updated the BananaMind Base Bench leaderboard! We now have these benchmark cards, they make it way easier to see which models are actually good! We've also added the model advisor. It asks you what you want to use the model for and the parameter range and gives you the best model for your task!
