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stephan 48c839d927 Initial public push: docs cosmos v4 + AI module + framework groundwork

This is the snapshot the production landing site (nibiru-framework.com) is
deployed from. Brings together the recent splash + docs migration to the v4
"Cosmos" design system, the new in-framework AI module, and the framework
groundwork that backs the framework-reference extraction.

What lands:
- docs/: Astro + Starlight site with the v4 dark cosmic palette, GalaxyHero
  canvas constellation, Mission Control chat (wired to /api/oracle →
  api.neuronetz.ai via providers.mjs Ollama), 5-panel MMVC stage
  (Model · AI · Module · Controller · View), translated EN/DE/JA/ES/FR
  content, PWA + sitemap + llms.txt + Umami analytics.
- docs/design-system/: canonical mockup bundle (source/index-v2.html for
  splash, source/docs-system.html + preview/ for docs, SPEC.md, tokens).
- docs/scripts/extraction/framework-reference-v2.md: deep framework
  reference (~1.6k lines, file:line citations, every public factory and
  idiom — basis for the LoRA training corpus.
- application/module/ai/: AI module with chat / embed / RAG / agent
  plugins, plus pdoQuery / httpGet / fileRead tools and Modelfile +
  smoke-test in training/.
- application/module/users/: user / ACL / form-factory traits used as the
  reference plugin pattern for the framework docs.
- application/settings/config/database/: schema + seed migrations
  including the AI module tables (200–203).
- Form factory + autogenerator changes the framework-reference-v2 covers.

Production secrets stay out: docs/.env, settings.production.ini and
ai.production.ini are all gitignored (.example files are in tree).

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

2026-05-08 15:22:18 +02:00

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Training nibiru-coder

This folder contains everything needed to register a Nibiru-flavoured chat model on your Ollama server (defaults to http://localhost:11434; override via the OLLAMA_BASE_URL env var or in application/module/ai/settings/ai.production.ini).

What the model is

nibiru-coder is a system-prompt-customised Qwen 2.5 Coder 14B. It's not a fine-tune in the LoRA sense — it's the same weights as the base model but with a baked-in system prompt that:

explains MMVC, modules, the dispatcher, and Nibiru's singletons,
enforces the framework's conventions (pageAction, navigationAction, View::assign, Form::create),
pushes the model toward Nibiru-idiomatic answers instead of generic Laravel / Symfony advice.

System-prompt customisation runs instantly (no GPU training time) and gives ~80 % of the value of a real LoRA at zero training cost. When you have budget for a real LoRA, the corpus exporter produces the JSONL you'd train on.

Build it

./application/module/ai/training/build.sh           # builds nibiru-coder:1.0
./application/module/ai/training/build.sh 1.1       # bump tag

The script:

Reads the Modelfile next to it.
POSTs to ${OLLAMA_BASE_URL}/api/create.
Runs a smoke-test chat call to confirm the new tag responds.

After it succeeds, set the model in application/module/ai/settings/ai.ini:

[AI]
chat.model = "nibiru-coder:1.0"

…and every \Nibiru\Module\Ai\Ai instance in your app talks to it.

Iterate on the system prompt

The Modelfile's SYSTEM """ ... """ block is the lever. Tighten the conventions, add new examples, or add citations to specific framework files. Re-run build.sh with a new tag (1.1, 1.2) and A/B against the previous tag in your app.

Real LoRA path (when you're ready)

Run npm run build:corpus in docs/ — produces dist/corpus/chat.jsonl.
Use Axolotl / Unsloth / LLaMA-Factory with that JSONL as your sharegpt training set.
Convert the resulting LoRA to GGUF (llama.cpp's convert-hf-to-gguf.py).
Build an Ollama Modelfile with FROM ./your-lora.gguf and re-register as nibiru-coder:2.0.

The framework code doesn't need to change — flip the model tag in ai.ini and you're on the new weights.

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