Modify lora inference api

docs/finetune.md

@@ -271,10 +271,10 @@ LoRA supports dynamic loading, unloading, and switching at inference time withou
 ### API Reference
 
 ```python
-from voxcpm.model import VoxCPMModel
+from voxcpm.core import VoxCPM
 from voxcpm.model.voxcpm import LoRAConfig
 
-# 1. Load model with LoRA structure
+# 1. Load model with LoRA structure and weights
 lora_cfg = LoRAConfig(
     enable_lm=True, 
     enable_dit=True, 
@@ -283,15 +283,20 @@ lora_cfg = LoRAConfig(
     target_modules_lm=["q_proj", "v_proj", "k_proj", "o_proj"],
     target_modules_dit=["q_proj", "v_proj", "k_proj", "o_proj"],
 )
-model = VoxCPMModel.from_local(
-    pretrained_path,
-    optimize=True,       # Enable torch.compile acceleration
-    lora_config=lora_cfg
+model = VoxCPM.from_pretrained(
+    hf_model_id="openbmb/VoxCPM1.5",  # or local path
+    load_denoiser=False,              # Optional: disable denoiser for faster loading
+    optimize=True,                    # Enable torch.compile acceleration
+    lora_config=lora_cfg,
+    lora_weights_path="/path/to/lora_checkpoint",
 )
 
-# 2. Load LoRA weights (works after torch.compile)
-loaded, skipped = model.load_lora_weights("/path/to/lora_checkpoint")
-print(f"Loaded {len(loaded)} params, skipped {len(skipped)}")
+# 2. Generate audio
+audio = model.generate(
+    text="Hello, this is LoRA fine-tuned result.",
+    prompt_wav_path="/path/to/reference.wav",  # Optional: for voice cloning
+    prompt_text="Reference audio transcript",   # Optional: for voice cloning
+)
 
 # 3. Disable LoRA (use base model only)
 model.set_lora_enabled(False)
@@ -300,23 +305,39 @@ model.set_lora_enabled(False)
 model.set_lora_enabled(True)
 
 # 5. Unload LoRA (reset weights to zero)
-model.reset_lora_weights()
+model.unload_lora()
 
 # 6. Hot-swap to another LoRA
-model.load_lora_weights("/path/to/another_lora_checkpoint")
+loaded, skipped = model.load_lora("/path/to/another_lora_checkpoint")
+print(f"Loaded {len(loaded)} params, skipped {len(skipped)}")
 
 # 7. Get current LoRA weights
 lora_state = model.get_lora_state_dict()
 ```
 
+### Simplified Usage (Auto LoRA Config)
+
+If you only have LoRA weights and don't need custom config, just provide the path:
+
+```python
+from voxcpm.core import VoxCPM
+
+# Auto-create default LoRAConfig when only lora_weights_path is provided
+model = VoxCPM.from_pretrained(
+    hf_model_id="openbmb/VoxCPM1.5",
+    lora_weights_path="/path/to/lora_checkpoint",  # Will auto-create LoRAConfig
+)
+```
+
 ### Method Reference
 
 | Method | Description | torch.compile Compatible |
 |--------|-------------|--------------------------|
-| `load_lora_weights(path)` | Load LoRA weights from file | ✅ |
+| `load_lora(path)` | Load LoRA weights from file | ✅ |
 | `set_lora_enabled(bool)` | Enable/disable LoRA | ✅ |
-| `reset_lora_weights()` | Reset LoRA weights to initial values | ✅ |
+| `unload_lora()` | Reset LoRA weights to initial values | ✅ |
 | `get_lora_state_dict()` | Get current LoRA weights | ✅ |
+| `lora_enabled` | Property: check if LoRA is configured | ✅ |
 
 ---
 
@@ -346,7 +367,7 @@ lora_state = model.get_lora_state_dict()
 ### 4. LoRA Not Taking Effect at Inference
 
 - Ensure inference config matches training config LoRA parameters
-- Check `load_lora_weights` return value - `skipped_keys` should be empty
+- Check `load_lora()` return value - `skipped_keys` should be empty
 - Verify `set_lora_enabled(True)` is called
 
 ### 5. Checkpoint Loading Errors