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Banaxi PRO

Banaxi-Tech
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AI & ML interests

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

Recent Activity

new activity 12 minutes ago
hugging-science/ripp:Hugging Science is DEAD???
repliedto their post about 2 hours ago
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.
repliedto their post about 3 hours ago
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.
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