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Hoglet-33
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AI & ML interests

Open source AI, datasets, parameter efficiency, SLMs, AI for the betterment of humanity. Contact at ash@basicallyai.co

Recent Activity

liked a dataset 3 minutes ago
XiaomiMiMo/MiMo-V2.6-RL-oss
repliedto their post 18 minutes ago
Everything going on here at basically AI: 1. Pebble 1.5 We're working on Pebble 1.5. Here's what we know so far: - They will be better than the last generation. 99.99% certain. - Expanded context lengths of at least 16,384 tokens, with the flagship potentially reaching 32,768. - A Mamba3-based architecture with some other new architectural designs we're experimenting with. - Native CPU compatibility — something we failed at with the last generation. - Natively multilingual and multimodal??? 2. SmolCodeBench A code benchmark designed specifically for small models, because there really isn't a good one right now. 3. SENTRY VOID is working on something called SENTRY — System for Evaluating Neural Threats, Responses, and Yields. More on that soon. 4. basically OS It's an operating system/app/harness. We're still deciding. 5. Finances Trying to balance the finances after purchasing a Hugging Face Pro subscription. Follow us for updates: @Hoglet-33 https://huggingface.co/basically-ai https://huggingface.co/basically-experimental https://huggingface.co/void-research
repliedto Banaxi-Tech's post about 20 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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