docs: clarify inference path via larql compile into model

README.md

@@ -25,12 +25,26 @@ A **vindex** is a transformer's weights decompiled into a queryable feature data
 | A feature-space index for Gemma4-e2b-it | A language model |
 | Exposes entity associations via `/v1/walk` | `/v1/infer` does NOT produce factual completions |
 | Enables rank-1 knowledge edits (DELETE/INSERT) | Not a replacement for the base Gemma4 weights |
-| Circuit analysis (broadcast→domain→entity→prediction) | Not a general inference engine |
+| Circuit analysis (broadcast→domain→entity→prediction) |
+| Editing surface for `larql compile into model` → standard HuggingFace safetensors inference | Not a general inference engine |
 
-**Critical note on `/v1/infer`:** This endpoint returns a feature-modulated projection of the host model's activations — not a coherent text-generation distribution. Output is incoherent subword tokens by design (the vindex is a feature graph, not a full transformer forward pass). For factual completion, use `google/gemma-4-e2b-it` directly. Use `/v1/walk` and `/v1/patch` for the validated operations this vindex is designed for.
+**Critical note on `/v1/infer`:** This endpoint returns a feature-modulated projection of the host model's activations — not a coherent text-generation distribution. Output is incoherent subword tokens by design (the vindex is a feature graph, not a full transformer forward pass). For factual text generation from the *base* model, use `google/gemma-4-e2b-it` directly. To run inference on an **edited** model (after DELETE/INSERT patches), use `larql compile into model` — this exports MEMIT-edited weights to HuggingFace safetensors that load like any standard `transformers` model. Use `/v1/walk` and `/v1/patch` for the validated vindex operations.
 
 **Validated surfaces:** `/v1/walk` (entity-association retrieval), `/v1/describe` (feature neighborhood), `/v1/patch` DELETE/INSERT (rank-1 weight editing, Gate 3 confirmed).
 
+**Compile edited vindex to a runnable model:**
+```bash
+# After applying patches, export to safetensors for standard inference
+larql compile into model \
+  --vindex Divinci-AI/gemma-4-e2b-vindex \
+  --output ./edited-gemma4 \
+  --format safetensors
+
+# Run with standard Transformers
+from transformers import AutoModelForCausalLM, AutoTokenizer
+model = AutoModelForCausalLM.from_pretrained('./edited-gemma4')
+```
+
 ## Quick start
 
 ```bash