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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>
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
Modelfilenext 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:corpusindocs/— producesdist/corpus/chat.jsonl. - Use Axolotl / Unsloth / LLaMA-Factory with that JSONL as your sharegpt training set.
- Convert the resulting LoRA to GGUF (
llama.cpp'sconvert-hf-to-gguf.py). - Build an Ollama Modelfile with
FROM ./your-lora.ggufand re-register asnibiru-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.