Add Divinci-AI organization card

README.md

@@ -1,10 +1,57 @@
 ---
-title: README
-emoji: 😻
-colorFrom: pink
-colorTo: green
-sdk: static
-pinned: false
+title: Divinci AI
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Divinci AI
+
+Feature-level interpretability artifacts for open transformers — built openly, validated empirically.
+
+A **vindex** is a transformer's weights decompiled into a queryable feature database. It exposes the entity associations, circuit structure, and knowledge-editing surfaces that live inside a model's FFN layers — without requiring GPU inference for most operations.
+
+Think of it as the model's index: the thing you search before you run it.
+
+---
+
+## Published vindexes
+
+| Model | Architecture | Params | Vindex |
+|-------|-------------|--------|--------|
+| Gemma 4 E2B-it | Dense (Gemma 4) | 2B | [Divinci-AI/gemma-4-e2b-vindex](https://huggingface.co/Divinci-AI/gemma-4-e2b-vindex) |
+| Qwen3.6-35B-A3B | MoE (Qwen3.6) | 35B / 3B active | [Divinci-AI/qwen3.6-35b-a3b-vindex](https://huggingface.co/Divinci-AI/qwen3.6-35b-a3b-vindex) |
+| GPT-OSS 120B | MoE (OpenAI) | 120B / ~13B active | *building* |
+
+Three organizations, three architectures: Gemma dense, Qwen MoE, OpenAI MoE.
+
+---
+
+## What's a vindex?
+
+Standard model weights tell you *what* a model computes. A vindex tells you *where* it stores specific knowledge and *which features* need to change for a targeted edit.
+
+Concretely: given a query like `"Paris → capital"`, a vindex walk returns the layers, feature directions, and token associations that encode that fact. A patch operation writes a rank-1 ΔW that suppresses or overwrites that association — compiled back to standard HuggingFace safetensors for inference.
+
+LarQL (the toolchain that builds vindexes) is open-source: [github.com/chrishayuk/larql](https://github.com/chrishayuk/larql) | [github.com/Divinci-AI/larql](https://github.com/Divinci-AI/larql).
+
+---
+
+## Research
+
+**Paper 1 — Architectural Invariants of Transformer Computation** *(arXiv forthcoming)*  
+Five properties measured across every model in this collection. Three hold within ±15% coefficient of variation across architectures, organizations, and scales. One collapses under 1-bit quantization. One scales monotonically with model size.
+
+**Paper 2 — Constellation Edits** *(draft)*  
+Mechanistic knowledge editing in transformer feature space. Includes a negative result: why activation-space edits fail in 1-bit models, and what weight-space geometry reveals about why.
+
+Working notebooks: [github.com/Divinci-AI/server/tree/preview/notebooks](https://github.com/Divinci-AI/server/tree/preview/notebooks)
+
+---
+
+## Working in public
+
+Every measurement in our papers traces back to a notebook and a commit. Negative results ship alongside positive ones — the compensation mechanism that defeats knowledge editing in 1-bit models is in the notebooks, not buried in a supplement.
+
+If you replicate a result and find a discrepancy, open an issue on the LarQL repo.
+
+---
+
+*Vindexes on this org are free for academic and research use (CC-BY-NC 4.0). Commercial licensing: mike@divinci.ai*