Fine-tune a model on your data. Own the weights.
OwnLLM trains LoRA adapters on datasets you upload, against base models from 3B to 70B parameters. You get back a downloadable adapter, a measured eval report, and a bill that itemizes every token. Below: what teams use that for.
Common use cases.
Models that actually know your product.
Fine-tune on your documentation, transcripts, or playbooks so the model answers in your vocabulary and cites the source material instead of hallucinating.
- Support knowledge bases, changelogs, internal SOPs
- 3B–8B bases work well for retrieval-style workloads
- Re-run weekly or monthly as the corpus grows
Stay inside the four walls.
Some data can’t leave your environment. Train on OwnLLM, download the adapter, serve it on your own GPUs behind whatever perimeter your compliance team requires.
- Dataset wiped from runner after every job
- Adapter download in standard safetensors format
- DPA draft available on request during alpha
Ship a custom model into a shipping product.
Teams with an existing LLM feature often want to swap the generic API for a fine-tuned adapter trained on their own interactions. Per-token pricing and no lock-in make that a reversible experiment.
- Instruction, chat, or continued-pretraining
- OpenAI-compatible hosted inference · post-alpha
- Self-host the adapter from day one if you prefer
How a run actually goes.
Read the quickstart →Prepare your dataset
JSONL with the task shape you want — instruction pairs, chat turns, or raw continued-pretraining text. The docs page has exact schemas.
Submit a job
CLI: `ownllm finetune --data ./corpus.jsonl --task instruct --model 7b`. Or upload it in the web wizard with a live price estimate.
Review the job report
Training loss, eval loss, before / after samples on a held-out slice. If the run fails before the first gradient step, credits are refunded in full.
Download the adapter
Every completed job produces a LoRA adapter you can serve on any vLLM or TGI-compatible stack. Or wait for hosted inference to launch after alpha.
Not sure whether fine-tuning is the right tool?
It probably isn’t for one-off prompt experiments — that’s what prompt engineering and retrieval-augmentation are for. Fine-tuning earns its keep when you have a lot of examples of the kind of output you want, and the generic model keeps drifting away from it. Reach out if you want a sanity check before you commit to a run.