MODULE 02 / TRAINING
Custom Training
Codebase → dataset → LoRA adapter, in your infra. Parse a repo, generate instruction-response pairs, validate JSONL, hand off to a fine-tune API. Bring your own keys, or run demo mode.
847 examples / 45min A100BYOK · OpenAI / Anthropic / GoogleJSONL chat-format output
STEP 1 / 6
Mode
# output.jsonl · chat format{"messages": [{"role": "system", "content": "You are a helpful code assistant."},{"role": "user", "content": "How do I create a Supabase client in Next.js?"},{"role": "assistant", "content": "Use createBrowserClient from @supabase/ssr…"}]}
SMALL
Adapter weights are MBs, not GBs. Swap behaviors per request.
CHEAP
Train on consumer GPUs or one A100 hour. No fleet required.
REVERSIBLE
Bad fine-tune? Drop the adapter. The base model is untouched.