Fine-tuned language models for content management systems
Specialized models trained on real-world CMS data to assist content editors with component selection, page building, and content structure decisions.
| Approach | Description |
|---|---|
| Surgical LoRA | Train upper reasoning layers while preserving base model capabilities |
| Multi-format Datasets | Flat, embedded, hierarchical, and conversational training formats |
| Data Augmentation | Query paraphrasing for linguistic diversity |
| Thought Anchors | Structured <think> reasoning in responses |
Optimized for Apple Silicon using the MLX framework.
Building AI tools for the nonprofit sector