Train.
Fine-tunes, tracked end to end.
Run a fine-tune, watch the progress, ship the result. Roll back when you need to. Adapt a base model on your own data and deploy it next to the catalog.
Bring your data. Watch the run. Deploy the result.
Train turns a base model into your model — in five steps. Pick from a verified catalogue, connect your data, choose your hardware, and let the run go. Loss, epochs, GPU and eval are all live. Stop, resume or roll back any run without leaving the page.
Choose from a curated catalogue of verified open-weight models — filtered by category, size and VRAM requirement. Need something specific? Search HuggingFace Hub directly, with a compatibility check before you commit.
Upload a file (JSONL, CSV, Parquet), pull from your indexed workspace data, or select documents from the Public Library — a pool of creator-opted-in content where a one-time royalty goes 88% to the owner. Train inspects the data before you spend a GPU-second.
Live loss curve, epoch counter, GPU and vRAM telemetry. Pause, resume or stop any run. The cost estimate ticks up alongside the chart — no quarter-end surprises.
An eval pass runs automatically at the end — loss, perplexity, and task-specific checks. Compare to the base, then promote the adapter to your private model catalogue with one click. Every run is versioned; roll back to any checkpoint.
Run on askFinz cloud infrastructure, link your own GCP, AWS or Azure billing account, or download the Training Agent and run on your own GPU. On-premise training is free — you provide the compute, we handle the orchestration.
Loss curve, epochs, GPU, eval — all live.
A scripted walk-through of a LoRA fine-tune mid-run. Loss points stream in, the epoch counter ticks up, the GPU gauge oscillates, eval metrics land when training finishes. The real surface lives at train.askfinz.ai.
What ships today.
train.askfinz.ai·BetaTrain is in beta. These features are live but still being refined based on early user feedback.
- Fine-tune base models on your data — upload training examples, pick a base model, start a run
- Real-time training progress — watch loss curves, validation metrics and estimated time to completion
- Cost estimation before you commit — see what a training run will cost based on dataset size and model choice
- Speed tiers you control — pick fast (expensive) or slow (cheap) training based on your timeline
- One-click deployment — ship a trained model to production, it appears in Chat and other workspaces immediately
- Rollback and versioning — unship a model that's not performing, revert to an earlier checkpoint
- Training data stays private — your examples never leave your account, models train in isolated environments
Where Train runs.
One account, many surfaces. Train is reachable from the surfaces lit below. Sign in once, pick up where you left off — your threads, files and history follow you across every surface.
Teams with specialist data who want a model that knows their domain can upload examples, pick a base model and watch the run in real time. Once training is done, one click ships the result to every workspace in the platform. Training data stays private to your account.
Read the guide: Fine-tune AI models on your own dataSee also — other vertical workspaces.
Train shares one login, one memory and one model layer with every other workspace under askfinz.ai.
Browse the rest of the catalog at /apps.
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