none-yet (None yet)

posted an update about 21 hours ago

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966

BananaMind 3 is going to be an comeback. Sorry for the BGA things ( i genuinely didnt know about nsa existing and yes I used aiiii)
Were going to use our BananaMind 2.1 Unified architecture with some changes + NSA and some other stuff.

6 replies

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prithivMLmods

posted an update 2 days ago

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289

Flux-3-Image-Klein-LoRAs-Fast is now live on Hugging Face Spaces.🤗🔥

Plug and play with any custom LoRAs based on flux-3-image-klein. 16-step distilled fast inference, with support for prompt upsampling. More LoRAs will be added in the coming days!

🔗 Space: prithivMLmods/flux-3-image-klein-loras-fast

🔗 Image Generation Apps Collection: https://huggingface.co/collections/prithivMLmods/image-generation-apps-collection

🔗 GitHub: https://github.com/PRITHIVSAKTHIUR/flux-3-image-klein-loras-fast

To learn more, visit the app page or the respective model pages.

Banaxi-Tech

posted an update 2 days ago

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4,645

BananaMind is in the google situation before argon right now, right?

29 replies

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Banaxi-Tech

posted an update 5 days ago

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186

So... im on vacation for 5 more days and I just realized... my hotel doesent have a grounding port 😟 so my pc shocked me 3 times.

So no progress for 5 days... only me and this phone

2 replies

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namanvats

submitted a paper to Daily Papers 5 days ago

Good Judgment, Bad Advice: Decision-Model Controllers Raise Agent Cost Without Improving Success

Paper • 2610.24649 • Published 10 days ago • 16

Banaxi-Tech

posted an update 6 days ago

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4,234

Hi everyone
We at

saicr have found a very interesting "thing" we call ADN via interpretabillity. It appears on EVERY prompt sent to the model. More on that later.
Also I made a X account follow it for more updates:
@banaxitech

And KleinML:
@kleinmlai

Here's my website for Klein ML: https://kleinml.com/?ref=huggingface.co

4 replies

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namanvats

authored a paper 6 days ago

Good Judgment, Bad Advice: Decision-Model Controllers Raise Agent Cost Without Improving Success

Paper • 2610.24649 • Published 10 days ago • 16

Banaxi-Tech

posted an update 9 days ago

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205

bci is the future.

and i dont like that

4 replies

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Banaxi-Tech

posted an update 10 days ago

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7,298

hi everyone
we at

klein-ml are announcing KleinMem

These models will have Native memory, without any system-prompt or prompt, it still remembers exactly.

Get early access at https://tinyurl.com/klein-eap limited to 15 users.

Architecture details are coming soon toon.

If you know alot about architecture and want to help build it, join

klein-ml .

Banaxi-Tech

posted an update 11 days ago

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3,808

KV1 updates:

We at

klein-ml have finished stage 1 of KV1 1.2B, we don't think we will need additional stages.

KV1 2.6B is near the midpoint of training, going well so far, its going to be our most agentic model yet.

We have released Klein Code Bench klein-ml/klein-code-bench, a benchmark testing whether models can build complete projects from scratch. It contains a easy, intermediate, hard and max subset.

Here are some benchmarks of the current KV1 1.2B

It retains 75.8% of FP16 intelligence.

We are also announcing that we at

klein-ml are working on Klein 24B A2B which will be our first model featuring ternary weight quantization.

2 replies

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Banaxi-Tech

posted an update 12 days ago

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2,273

We're announcing KV1. Now.

We're going to take some models, do a bit of continued training on them and QAT them for only a 2 bit KV cache.
And because of what happened with BGA, and this is also kind of some research stuff, here's what we're going to change:
We're going to always, before we release how and make any claims, release our results.
We also would like (idk if we can) to have some external reviewers before releasing them, I'M talking to YOU @AtAndDev and @mithulaartigala
And we're going to properly cite other research.

The models we are going to release is:
KV1 1.2B, based on LFM 2.5 1.2B Instruct
KV1 2.6B, based on LFM 2.5 2.6B Instruct
KV1 4B, based on Qwen3.5 4B.

If we get enough compute, we may also train:
KV1 9B

And if you guys want to a ~27B model, you guys decide which, either on LFM 2.5 24B A2B, Qwen3.8 27B, or Qwen3.6 35B A3B.

Our goal is to get 2 bit, at 4 bit quality.

Here's an image of our current progress, KV1 is at 3 bit, not 4 bit, but it will improve!

43 replies

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Banaxi-Tech

posted an update 13 days ago

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4,225

Should I even continue making models?

27 replies

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Banaxi-Tech

posted an update 15 days ago

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4,530

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

9 replies

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AtAndDev

posted an update 16 days ago

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5,261

@Banaxi-Tech stop hiding my comments. AND STOP STEALING PAPERS AND SPREADING MISINFORMATION.
your BGA blog is a copy of NSA (deepseek, 2025) branded under your name. literally the same top16 selected blocks, 512 local window, router over block summaries, all you did was change block size from 64 to 128.
you didnt cite NSA once but you put a “please cite BGA” bibtex at the bottom.
i commented under your post and said that there is no way that you can support claims like: “The Accuracy Should BE WAy better than DSA but untested yet.” you didnt run a single experiment. and the 256x isnt from BGA, its just n/2k with k=2048 so the exact same k DSA uses. if opus wrote this for you, at least read it before posting.
i commented again after you hid my comment despite it having constructive and correct feedback and you hid that too. and again.
you can hide the truth and just try to get hf post likes..... but is it really the thing that needs to be done? do you really want to take papers and make them yours while barely even changing the params?

admitting your mistakes and doing something about them needs humbleness, intelligence, humanness.
i encourage you to admit your mistakes and try to do better next time (at least read what blog your ai wrote or do proper experiments to back your stuff up).

49 replies

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Banaxi-Tech

posted an update 16 days ago

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4,698

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 😟)

12 replies

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Banaxi-Tech

posted an update 17 days ago

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3,228

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 18 days ago

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135

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!

prithivMLmods

posted an update 19 days ago

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4,748

OneDecision-VisionGuard-Demo is now available on Hugging Face Spaces!

🤗 Space: prithivMLmods/OneDecision-VisionGuard-Demo

This demo showcases the OneDecision-VisionGuard family of multimodal image classification models for detecting NSFW and other sensitive visual content, with structured JSON reasoning, improved accuracy, and better handling of edge cases such as sensitive imagery, uncensored analysis, scene descriptions, and classification reasoning.

📦 Models: 27B, 9B, 4B — prithivMLmods/OneDecision-VisionGuard-27B-SFT, prithivMLmods/OneDecision-VisionGuard-9B-SFT, prithivMLmods/OneDecision-VisionGuard-4B-SFT

↗️ Collection: https://huggingface.co/collections/prithivMLmods/onedecision-visionguard

To learn more, visit the app page or the respective model pages.

Banaxi-Tech

posted an update 19 days ago

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89

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