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.