fix: save PEFT adapter (not full model), remove random suffix from LoRA names, fix epoch cap to 1000

app.py

@@ -573,13 +573,9 @@ def gradio_main():
             return
 
         lora_name = (lora_name or "").strip() or "my-lora"
-        # Sanitize: alphanumeric, dash, underscore only
         lora_name = "".join(c if c.isalnum() or c in "-_" else "-" for c in lora_name)
-        # Append random suffix to prevent naming collisions between users
-        suffix = "".join(random.choices(string.ascii_lowercase + string.digits, k=4))
-        lora_name = f"{lora_name}-{suffix}"
 
-        epochs = max(1, min(int(epochs), 10))
+        epochs = max(1, min(int(epochs), 1000))
         lr = float(lr)
         rank = max(1, min(int(rank), 64))
 

train_engine.py

@@ -442,7 +442,10 @@ def inject_lora(model, lora_cfg: LoRAConfig) -> Tuple[Any, Dict[str, Any]]:
 
 def save_lora_adapter(model, output_dir: str) -> None:
     os.makedirs(output_dir, exist_ok=True)
-    decoder = _unwrap_decoder(model)
+    # Use the PEFT-wrapped decoder (model.decoder), NOT the unwrapped base model.
+    # _unwrap_decoder strips the PEFT wrapper, causing save_pretrained to save
+    # the full model instead of just the LoRA adapter weights.
+    decoder = model.decoder if hasattr(model, "decoder") else model
 
     if hasattr(decoder, "save_pretrained"):
         decoder.save_pretrained(output_dir)