docs: rebrand legacy clemsail/eu-kiki-apertus-math-lora -> Ailiance-fr/apertus-math-lora

README.md

@@ -17,7 +17,7 @@ language:
 library_name: peft
 ---
 
-# eu-kiki-apertus-math-lora
+# apertus-math-lora
 
 LoRA adapter for **swiss-ai/Apertus-70B-Instruct-2509**, part of the [ailiance](https://github.com/L-electron-Rare/ailiance) project. Live demo: https://www.ailiance.fr.
 
@@ -44,7 +44,7 @@ LoRA adapter for **swiss-ai/Apertus-70B-Instruct-2509**, part of the [ailiance](
 
 | Field | Value |
 |---|---|
-| **Versioned model name(s)** | `clemsail/eu-kiki-apertus-math-lora` (this LoRA adapter, v0.4.2) |
+| **Versioned model name(s)** | `Ailiance-fr/apertus-math-lora` (this LoRA adapter, v0.4.2) |
 | **Model dependencies** | This is a **fine-tune (LoRA, rank 16)** of the general-purpose AI model [`swiss-ai/Apertus-70B-Instruct-2509`](https://huggingface.co/swiss-ai/Apertus-70B-Instruct-2509). Refer to the base-model provider's PST for the underlying training summary. |
 | **Date of placement of the model on the Union market** | 2026-05-06 |
 
@@ -166,7 +166,7 @@ from mlx_lm.tuner.utils import linear_to_lora_layers
 from huggingface_hub import snapshot_download
 
 base_path = snapshot_download("swiss-ai/Apertus-70B-Instruct-2509")
-adapter_path = snapshot_download("clemsail/eu-kiki-apertus-math-lora")
+adapter_path = snapshot_download("Ailiance-fr/apertus-math-lora")
 
 model, tokenizer = load(base_path)
 linear_to_lora_layers(model, num_layers=32, config={"rank": 16, "alpha": 32})
@@ -179,7 +179,7 @@ Or fuse and serve as a self-contained checkpoint:
 python -m mlx_lm fuse \
     --model swiss-ai/Apertus-70B-Instruct-2509 \
     --adapter-path <adapter_path> \
-    --save-path /tmp/eu-kiki-apertus-math-lora-fused \
+    --save-path /tmp/apertus-math-lora-fused \
     --dequantize
 ```