Prepend Conference-talk-demo disclaimer + reference links

README.md

@@ -11,6 +11,17 @@ language:
 library_name: sklearn
 ---
 
+> **⚠️ Conference talk demo — not production weights.**
+>
+> This model accompanies a conference keynote on local on-device AI. Published as a reference for the fine-tuning patterns shown on stage — **not** a deployable artefact. No security audit, no SLA, pinned to the talk's state.
+
+- Source repository: [thinktecture-labs/local-multi-model-agent-slm](https://github.com/thinktecture-labs/local-multi-model-agent-slm)
+- Threat model + out-of-scope: [SECURITY.md](https://github.com/thinktecture-labs/local-multi-model-agent-slm/blob/main/SECURITY.md)
+- Licensing details: [MODEL_LICENSES.md](https://github.com/thinktecture-labs/local-multi-model-agent-slm/blob/main/finetune/MODEL_LICENSES.md)
+- All five models in the stack: [Collection — Local Multi-Model Agent — nextera fine-tunes](https://huggingface.co/collections/thinktecture/local-multi-model-agent-nextera-fine-tunes-6a04a8ff2a40e5696f3c2f18)
+
+---
+
 ## LogReg Intent Classifier
 
 | | |
@@ -25,9 +36,3 @@ library_name: sklearn
 | **Out of scope** | Anything that requires generation (it's a 3-way classifier). Low-confidence predictions (< 0.60 threshold, configurable in `intent_classifier_logreg.py`) are overridden to `direct_answer` as a safe fallback intent. The 1B generative classifier is only used as a load-time fallback when the LogReg model file is absent, not as a per-query confidence fallback. |
 | **Reference eval (Nextera)** | 96.1% on 180-query eval set. ~10ms per classification (single CPU thread). |
 | **Known failure modes** | When the EmbeddingGemma FT changes, the LogReg weights become invalid — `intent_classifier_logreg.py:13-15` warns about this coupling. Re-train both together. |
-
----
-
-Source repository: https://github.com/thinktecture-labs/local-multi-model-agent-slm
-
-Generated from `finetune/MODEL_CARDS.md` — see source repo for the full pipeline + reproducibility instructions.