Card: One name (While, whileai SDK), current links

README.md

@@ -27,7 +27,7 @@ On a hard, held-out benchmark it reaches **75.3% macro-averaged intent-type accu
 
 ### Model Description
 
-- **Developed by:** While AI
+- **Developed by:** While
 - **Model type:** E-commerce payment-intent classifier; structured JSON output over seven intent types
 - **Language:** English
 - **License:** Gemma (inherited from the base model)
@@ -85,7 +85,7 @@ The model returns one JSON object per message:
 
 ### Training Data
 
-The differentiator is the data. While AI builds e-commerce intent training data as **randomized conversational simulations**: role-played customers with independently sampled personas, tones, financial situations, life stages, devices, and behaviors, including adversarial actors, simulated turn by turn between two independently drawn models. Generation is label-blind (the generating models never see the intent schema), labels are assigned in a separate pass, and every candidate passes a structural data gate: deduplicated by message signature, with zero train/eval leakage.
+The differentiator is the data. While builds e-commerce intent training data as **randomized conversational simulations**: role-played customers with independently sampled personas, tones, financial situations, life stages, devices, and behaviors, including adversarial actors, simulated turn by turn between two independently drawn models. Generation is label-blind (the generating models never see the intent schema), labels are assigned in a separate pass, and every candidate passes a structural data gate: deduplicated by message signature, with zero train/eval leakage.
 
 This release was fine-tuned on **17,144 conversations**. The held-out evaluation set contains **1,977 conversations** and has no conversation or identifier overlap with the training split.
 
@@ -107,7 +107,7 @@ Held-out eval of 1,977 conversations, zero train/eval leakage, macro-averaged (e
 
 ### Results
 
-![While AI e-commerce intent evaluation: accuracy, per-action accuracy, and cost to serve](1b-v1.png)
+![While e-commerce intent evaluation: accuracy, per-action accuracy, and cost to serve](1b-v1.png)
 
 - Fine-tuning takes intent-type accuracy from **14.4% to 75.3%** on the public six-action rubric, macro-averaged across the 1,977-row held-out evaluation.
 - Intent detection reaches **80.7%**, and structured order-detail extraction reaches **66.3%**, both macro-averaged on the same public rubric.
@@ -126,4 +126,4 @@ Served as an OpenAI-compatible endpoint (base + adapter) under vLLM. Measured on
 
 ## Model Card Contact
 
-While AI, https://huggingface.co/while-ai
+While, https://huggingface.co/while-ai