One sentence in. A trained model, a registered run and your own weights out.
Below is the complete Tensovy pipeline. Every stage runs inside one agent loop, on Tensovy managed infrastructure or your own cloud account, with no manual file shuffling between steps.
Tell the agent the outcome, not the hyperparameters
You open with a goal in plain language. The agent turns that into an architecture decision, a memory budget and a compute plan before a single dollar of GPU time is spent.
- Base model recommendation matched to task type and output schema
- VRAM estimate calculated from sequence length, batch size and adapter rank
- GPU tier suggestion with the live hourly rate for the route you pick
- A cost and duration estimate you approve before anything launches
Bring a labelled dataset, or find one
The dataset stage is where most fine tuning projects die. Tensovy takes the labelled data you already have, or helps you find a public dataset when you have almost none, and previews and profiles every row so you see exactly what the trainer will see before a GPU starts.
- Upload of labelled JSONL, CSV, an image folder or a zip of one
- Public connectors that search and import Hugging Face and Kaggle datasets
- Row preview and a dataset profile so you see what the trainer will see before it runs
- A data loader written for your recipe inside the generated training package, so the trainer reads your dataset as it is
Managed, or your own cloud
Run training on Tensovy managed infrastructure and get one bill, or connect your own cloud account and let your provider bill you directly with nothing added by us. Same agent, same workflow, same weights either way. Both routes are in early access.
- Managed by Tensovy, with no cloud account to set up and one bill
- Or your own cloud account, with the key held server side and used only for infrastructure operations
- Live inventory, hardware plan and hourly price cap shown for approval, and the instance starts only when you say yes
- Training package and data pushed over SSH, no manual scp or volume wiring
Failures get fixed, not forwarded to you
Before the long run, the agent smoke tests the package on the GPU. When it breaks, the agent reads the traceback, classifies the failure and applies the correction that an experienced engineer would apply, then reruns until it passes. Only then does training launch and stream live to your dashboard.
- Live loss curves, log tail, VRAM headroom and running spend
- Dependency pins, API changes and tokenizer errors fixed by the agent, not by you
- Dry run passes before the long run, so GPU hours go to training instead of debugging
- Hourly price cap guard and a cost meter in view, so a run cannot quietly burn budget
A registry entry you can trace and a file you can keep
When the run finishes, the weights are pulled off the instance into Tensovy object storage under your account and recorded in your model registry. Shutting the instance down is one click, and the raw artifacts stay yours.
- One click shutdown of the instance with the meter in view, so you decide when billing stops
- Adapter or merged model recorded in the registry with its config, dataset and metrics
- Roadmap: an OpenAI compatible endpoint served from your registered weights
- Direct download of safetensors weights, plus PyTorch weights for detection models
Standard tooling, generated for you instead of by you.
Tensovy does not invent a proprietary trainer. It writes configs for the libraries the ecosystem already trusts, which means every run is reproducible outside the product.
Unsloth
Memory efficient LoRA and QLoRA fine tuning for language models
LibreYOLO
YOLO family detection training on your own labelled images
Hugging Face
Model hub and dataset search, imported from inside the workspace
Kaggle
Public dataset search and import when you have almost no data
PyTorch
The runtime underneath every generated training package
RunPod
The GPU cloud behind bring your own key today, alongside Tensovy managed infrastructure
Run the whole pipeline yourself.
Early access is open now. A task is all you need to start, and bringing your own cloud account is optional.