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appvoid
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https://ko-fi.com/appvoid

AI & ML interests

Working on small sota models

Recent Activity

reacted to danielhanchen's post with 🔥 about 6 hours ago
Introducing Unsloth for AMD 🚀 You can now train & run LLMs on your AMD hardware • We collaborated with AMD to enable you to train & run 500+ models on AMD GPUs • Works on Windows, WSL, Linux • Train Qwen, Gemma on just 3GB VRAM GitHub: https://github.com/unslothai/unsloth Blog + Guide: https://unsloth.ai/docs/basics/amd
repliedto their post about 7 hours ago
A Small Model is All You Need. Meet `palmer-006` (90M) After 3 years of experiments, we are finally releasing our flagship tiny model: **palmer-006**. If you are building for edge hardware, SBCs (Raspberry Pi, etc.), or low-power devices, this is for you. Inspired by Andrej Karpathy's idea of a self-contained "cognitive core," we wanted to see how much power we could pack into a sub-100M parameter footprint. 🧠 **How we "Palmerized" it:** We believe in starting our experiments with the absolute strongest baseline possible. 1. Light fine-tuning on highly curated data 2. Model merging 3. Another light fine-tuning round 4. Adjusted Mamba for maximum token speed ⚡️ ⚠️ *Note: This is a foundational language model. It has not been instruction-tuned yet!* Also, since this needs instruction tuning next to become a chat assistant—**what dataset would you recommend we use for the instruct tune?** --- 🔗 **Quick Links & Info:** * **License:** Open for research, education, hobby, and modification! (For commercial use/hosted APIs, shoot an email to nosoyhackercodigo@gmail.com. *PS: Donators can claim a free commercial license!*) * **Attribution:** Built using AI tech from the Technology Innovation Institute (TII). Can't wait to see what you build at the edge. Let me know your prompt completions below! 👇 https://huggingface.co/appvoid/palmer-006
repliedto their post about 8 hours ago
A Small Model is All You Need. Meet `palmer-006` (90M) After 3 years of experiments, we are finally releasing our flagship tiny model: **palmer-006**. If you are building for edge hardware, SBCs (Raspberry Pi, etc.), or low-power devices, this is for you. Inspired by Andrej Karpathy's idea of a self-contained "cognitive core," we wanted to see how much power we could pack into a sub-100M parameter footprint. 🧠 **How we "Palmerized" it:** We believe in starting our experiments with the absolute strongest baseline possible. 1. Light fine-tuning on highly curated data 2. Model merging 3. Another light fine-tuning round 4. Adjusted Mamba for maximum token speed ⚡️ ⚠️ *Note: This is a foundational language model. It has not been instruction-tuned yet!* Also, since this needs instruction tuning next to become a chat assistant—**what dataset would you recommend we use for the instruct tune?** --- 🔗 **Quick Links & Info:** * **License:** Open for research, education, hobby, and modification! (For commercial use/hosted APIs, shoot an email to nosoyhackercodigo@gmail.com. *PS: Donators can claim a free commercial license!*) * **Attribution:** Built using AI tech from the Technology Innovation Institute (TII). Can't wait to see what you build at the edge. Let me know your prompt completions below! 👇 https://huggingface.co/appvoid/palmer-006
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ZeroGPU Explorers's profile picture Social Post Explorers's profile picture BananaMind Model Previewers's profile picture Community Early Access Models from appvoid's profile picture