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README.md

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+# OpenMind Labs
+
+We explore efficient ways to train, customize, and deploy AI models.
+
+## What We Do
+
+We focus on making AI more accessible by:
+
+- **Efficient Fine-Tuning** — Training small models to punch above their weight
+- **Identity Baking** — Embedding knowledge directly into model weights, not just prompts
+- **Local-First AI** — Tools that work on consumer hardware without cloud dependencies
+- **Ollama Integration** — Seamless deployment of custom models
+
+## Our Approach
+
+Big models aren't always the answer. We believe in:
+
+1. **Small but capable** — A well-trained 500M model can outperform a generic 7B model on specific tasks
+2. **Knowledge over size** — Baking information into weights is more robust than system prompts
+3. **Practical tooling** — If it doesn't run on your laptop, it's not useful enough
+
+## Projects
+
+### QEBits
+Quantum computing simulation library using IBM Qiskit for experimental training approaches.
+
+### Quant-1 *(in development)*
+Small language model experiments with identity baking and efficient fine-tuning techniques.
+
+## Philosophy
+
+We're not trying to build the biggest model. We're trying to build models that:
+- Know who they are (without being told every time)
+- Run locally without expensive hardware
+- Can be customized by anyone
+
+## Get Involved
+
+We're always experimenting. Check out our repos, try our models, break things, and let us know what works.
+
+---
+
+*Making AI smaller, smarter, and more personal.*
+