No peft

.gitattributes

@@ -34,3 +34,16 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
 model/tokenizer.json filter=lfs diff=lfs merge=lfs -text
+model/adapter_model.safetensors filter=lfs diff=lfs merge=lfs -text
+model/optimizer.pt filter=lfs diff=lfs merge=lfs -text
+model/README.md filter=lfs diff=lfs merge=lfs -text
+model/scheduler.pt filter=lfs diff=lfs merge=lfs -text
+model/vocab.json filter=lfs diff=lfs merge=lfs -text
+model/adapter_config.json filter=lfs diff=lfs merge=lfs -text
+model/merges.txt filter=lfs diff=lfs merge=lfs -text
+model/rng_state.pth filter=lfs diff=lfs merge=lfs -text
+model/special_tokens_map.json filter=lfs diff=lfs merge=lfs -text
+model/tokenizer_config.json filter=lfs diff=lfs merge=lfs -text
+model/trainer_state.json filter=lfs diff=lfs merge=lfs -text
+model/training_args.bin filter=lfs diff=lfs merge=lfs -text
+model/added_tokens.json filter=lfs diff=lfs merge=lfs -text

app.py

@@ -3,7 +3,6 @@ import re
 import gradio as gr
 import torch
 from transformers import AutoProcessor, Qwen2AudioForConditionalGeneration
-from peft import PeftModel, PeftConfig
 
 # Model path and configuration
 model_path = "./model"
@@ -25,9 +24,6 @@ def load_model():
         device_map="auto",
     )
     
-    # Load the LoRA adapter
-    model = PeftModel.from_pretrained(base_model, model_path)
-    
     model.eval()
     
     return model, processor