GEKO (up to 80% compute savings on LLM fine-tuning)
Mountain Curriculum routing: 5× compute to hard samples, skip mastered ones.
The personal AI that actually remembers what you teach it. Corrections become lessons + temporal-KG facts, retrieved on every future answer. Measured, not asserted. 69 monitors, fully local.
DPO self-fine-tuning from corrections in a sea of Open WebUI clones.
Privacy-conscious developers running local LLMs
Open WebUI · Khoj · Continue
Mountain Curriculum routing: 5× compute to hard samples, skip mastered ones.
Shard-based scheduling cuts GPU wait time, though Ray Tune offers similar early stopping.
Galaxy classification model, but model card has mostly empty fields.
Eval-synthesize-train loop automates custom model development better than manual fine-tuning.
Fine-tuned Qwen 30B that prioritizes output diversity over convergent accuracy.
Detachable PEFT modules that version independently, unlike LoRA's weight-coupled adapters.