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
git_repoparserchunkerprompt_genjsonlvalidationfine_tunelora_adapterDATASET PIPELINE
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.