Overview of self-supervised learning of tiny models, including distillation-based methods (aks. self-supervised distillation) and non-distillation methods.
⚡ A talking desktop Pikachu that runs 100% on-device — MiniCPM5-1B brain + VoxCPM voice + Nemotron ears, every model ≤1B params. No cloud, works with the Wi-Fi off.
🔍 Explore recursive reasoning with TinyRecursiveModels, a compact 7M parameter neural network achieving high scores on tough tasks without massive resources.
🚀 Streamline parallel development with Ralph MCP: run multiple PRDs in isolated workspaces, ensuring quality and efficient merging.
First 1-bit (BitNet b1.58) recursive reasoner for Sudoku-Extreme - distilled from a 7M-param FP TRM teacher into a 1.4 MB ternary student
Test-Time Training deployed on an STM32 NPU — self-adapting ResNet from PyTorch to Cortex-M55 edge hardware.
Local, project-scoped memory system for LLMs with evidence-based truth validation. Provides reliable long-term context via OpenAI-compatible Proxy and MCP, using Chain-of-Verification (CoVe) to eliminate hallucinations and the Ralph Loop for autonomous codebase repair.
Phi-3-Vision model test - running locally
Testing the Moondream tiny vision model
A playground for pushing local AI agents as far as possible on edge/mobile and local devices.
Train the smallest LM you can that fits in 16MB. Best model wins!
🚀 Implement the Tiny Recursive Model (TRM) for improved performance in recursive tasks, building on the HRM framework by Sapient AI.
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