OrangeFantasy

We're excited to release BananaMind 2 Micro, our smallest model yet.
It fits a compact architecture in only 2.9M parameters achieving the highest parameter efficiency on BananaMind Base Bench against comparable models.
It achieves comparable performance to GPT S2 5M and GPT S 5M at almost half the size while beating CMA 1M Mini.
BananaMind 2 Micro achieved the #1 spot on the Open SLM Leaderboard for the sub 3M category (not added yet but it achieves #1)
For the training we used Muon + the XSA Refresh Gate with a 5e-2 lr for Muon and 4e-3 for the 1D weights.
Its score on our efficiency measure is 0.326 getting the first place with Syn 2.6M on the second place scoring 0.291 and GPT S 5M at 0.235*
Check it out at BananaMind/BananaMind-2-Micro and follow us at:
@vovaRL
@DedeProGames
@Banaxi-Tech

BananaMind

Our new releases aren't stopping 🚀 August 13-14 BananaMind 2 Pro

9 replies

reacted to KlondikeDev's post with 🔥 17 days ago

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2169

Boris-2 coming soon!

The Models:

Boris-2-75M: Trained on 26B tokens -- estimated to start training on August 12th.
Boris-2-125M: Trained on 90B tokens -- Estimated to start training on August 20th.
Boris-2-250M: Trained on 60B tokens -- Estimated to start training on September 10th.

Why does 125M get more tokens than 250M?

Well, the straight answer is time. It saves time, while still allowing the 250M model to exceed the 125M model.

Furthermore, we are attempting a unique architecture and layering scheme to hopefully end up around the strength of SmolLM2-135M. Fingers crossed!

We hope to end up in the ballpark of AxiomicLabs/GPT-X2.5-135M or BananaMind/BananaMind-2-Pro-Preview

Following this, we will release the Pro, Instruct and Pro-Instruct variants. More info will be coming soon!

4 replies

reacted to OppaAI's post with 🔥 18 days ago

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2158

So many ideas, so little time and energy:

Another idea pops up today after spending the past weekend polishing the DAG Agentic workflow system of my AI Waifu system.
These past few days, as I was building workflows for my AI Waifu, the patterns and basic building blocks look very similar and probably can recycle for each workflow. So I propose a 5-layers Graph Engine to generalize all my workflows:
(I'm sure there are apps like that in the market, only the scale, complexity and price differs. But if f there is a generic app that can use local AI to let people construct workflows to run automated tasks, non-tech users or small companies can benefit from this.)

1) Planning - Users writes a detailed a spec of the workflow they want. Or even draws it in a n8n-like environment, defining each building block + args + queries, moving them around, arranging them to run in parallel/sequence, format the output, destination of the output, etc.

2) Orchestrating: AI system then interprets the spec, organizing the nodes into workflows, verified and tested by humans, then run the workflows and do the works. Reduce coding to the minimum for users.

3) Executing: Node is the basic building block of the graph and can be customizable, and sharable among different workflows; each node will call the registered tools or skills to do the work.

4) Tools and skills: exposed to AI system just like what we have right now.

5) Outputs: Generate reports of WIP and final results and users choose to get notified

With enough time and effort, and fund to invest, this and many other components of my AI system could potentially be a startup idea. Unfortunately I have neither the time, energy, fund, or talent to build productivity tools to benefit others, I can only build the components just good enough for my own use, and provide the source codes to other people to make the ideas scalable to production environment if they want.