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Banaxi-Tech

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Banaxi-Tech

AI & ML interests

SLMs, training from scratch, LoRA, TTS, Ternary models. AI Interpretability. BCI. Contact at banaxitech@gmail.com

Recent Activity

repliedto their post about 3 hours ago

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 😟)

repliedto AtAndDev's post about 4 hours ago

@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).

repliedto AtAndDev's post about 4 hours ago

@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).

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Banaxi-Tech 's datasets 1

Banaxi-Tech/Deepseek-V4-Reasoning-Code-2500

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