Roadmap
What we're building next in Arkor.
Arkor is in alpha, so this page is intentionally sparse. Items are grouped by what state they're in: actively being built, scoped and waiting their turn, or under consideration. We don't commit to dates yet.
In progress
Up next
Silent refresh on expiry, so long-running sessions stop getting interrupted by re-login.
Upload a local JSONL file as the training dataset, alongside the existing HuggingFace name and blob URL paths.
Run training on your own GPU instead of routing every job through Arkor's managed GPUs.
Surface the existing dry-run option in the Studio UI for fast smoke tests before kicking off a full training run.
Cover the full Gemma 4 family in the model field. Today every starter template uses gemma-4-E4B-it; opening up the rest of the family lets you pick the size that fits your latency and quality budget.
Backlog
Run the training backend on your own infrastructure, with a documented ARKOR_CLOUD_API_URL knob and versioned API guarantees.
Grow createArkor into an umbrella for shipping and evaluating models, not only training.
Export a trained model as a file you can run on your own machine or deploy target, instead of staying on Arkor's managed inference.
Generate training data from a small seed set, for cases where you don't already have a labeled dataset.
Templates that pair compatible teacher and student models so distillation runs work out of the box.
Templates aimed at small models suitable for WebGPU and mobile targets.
Expand support beyond the Gemma 4 family, so additional open-weight families join it as curated, hosted options.
Fine-tune on image (and eventually audio) inputs alongside text. Today every template is text-in, text-out.