Post-train a model in the next three minutes.
This is the POFE workspace, running entirely in your browser on sample data. Pick a base model and a dataset, choose a method, source GPUs, watch the run, check the evals and ship an endpoint. No signup, nothing uploaded.
The demo is the shape. Production is the depth.
Everything you just clicked through exists in the product, plus the parts that only matter on real data.
Your data, connected
S3, GCS, Azure Blob, Snowflake, BigQuery, Delta Lake and Unity Catalog connectors, with PII redaction and de-duplication pipelines that re-run as new data lands.
Custom rewards and judges
Write reward functions and verifiers in Python, calibrate LLM judges against your own human labels, and gate every release on slice-level regressions.
Real GPUs, real endpoints
H100, H200 and B200 capacity sourced across providers or your own clusters, and OpenAI-compatible endpoints with multi-LoRA, canaries and autoscaling.
New to post-training? Start with SFT vs DPO vs GRPO or estimate your own run with the cost calculator.
Now do it on your data.
Send us the task and a sample of the data. We'll set up a private workspace with your base model, a baseline eval and a first run already configured.