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title: README
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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.

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*Making AI smaller, smarter, and more personal.*