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.