Add standalone model card

README.md

@@ -1,6 +1,6 @@
 ---
 license: apache-2.0
-base_model: google/gemma-4-E2B-it
+base_model: google/gemma-4-e2b-it
 tags:
   - decision-model
   - system-1
@@ -24,12 +24,19 @@ Measured on **NVIDIA GeForce RTX 5060 Ti** (CUDA, float16), 30 iterations after
 
 | Metric | ClassOne (Single-Pass) | Autoregressive (50 tokens) |
 |---|---|---|
-| Mean Latency | **64.62 ms** | 845.77 ms |
-| P50 (Median) | **64.95 ms** | 844.45 ms |
-| P95 Latency | **65.66 ms** | 861.20 ms |
-| Throughput | **15.5 req/s** | 1.2 req/s |
+| Mean Latency | **47.07 ms** | 1,048.57 ms |
+| P50 (Median) | **46.96 ms** | 1,047.23 ms |
+| P95 Latency | **48.79 ms** | 1,062.45 ms |
+| Throughput | **21.2 req/s** | 1.0 req/s |
 | Output Tokens | **0** | 50 |
-| **Speedup** | **13.1× faster** | — |
+| **Speedup** | **22.3× faster** | — |
+
+### Edge vs Cloud Latency (ClassOne vs TypeSafe Jev API)
+
+Measured against TypeSafe AI's Jev (v1.13) cloud API:
+- **ClassOne (Local RTX 5060 Ti):** **52.49 ms** mean latency (19.1 req/s, $0.00 inference cost, 100% private)
+- **TypeSafe Jev (Cloud API):** **329.90 ms** mean latency (3.0 req/s)
+- **Edge Speedup:** **6.3× faster** than cloud API round-trip latency
 
 ## Decision Primitives
 
@@ -119,5 +126,5 @@ print("Anger score:   ", results["anger"].score)
 
 ## Attribution & Legal
 
-- Derived from [google/gemma-4-E2B-it](https://huggingface.co/google/gemma-4-E2B-it) (Google) — Apache License 2.0
+- Derived from [google/gemma-4-e2b-it](https://huggingface.co/google/gemma-4-e2b-it) (Google) — Apache License 2.0
 - Architecture & training code: [devops-thiago/class-one](https://github.com/devops-thiago/class-one) — Apache 2.0