fix for qwen w lora (axolotl-ai-cloud#906)

src/axolotl/utils/models.py

@@ -412,15 +412,22 @@ def load_model(
                 module.to(torch.float32)
 
     needs_fa2_dtype = cfg.adapter or cfg.fsdp
+    skip_prepare_model_for_kbit_training = False
+
+    if cfg.model_config_type == "qwen" and cfg.adapter == "lora":
+        # Qwen doesn't play nicely with LoRA if this is enabled
+        skip_prepare_model_for_kbit_training = True
+
     if (cfg.adapter == "lora" and load_in_8bit) or (
         cfg.adapter == "qlora" and cfg.load_in_4bit
     ):
         LOG.info("converting PEFT model w/ prepare_model_for_kbit_training")
         if cfg.gradient_checkpointing:
             model.gradient_checkpointing_enable()
-        model = prepare_model_for_kbit_training(
-            model, use_gradient_checkpointing=cfg.gradient_checkpointing
-        )
+        if not skip_prepare_model_for_kbit_training:
+            model = prepare_model_for_kbit_training(
+                model, use_gradient_checkpointing=cfg.gradient_checkpointing
+            )
         needs_fa2_dtype = True
 
     # LlamaRMSNorm layers are in fp32 after kbit_training or full finetune, so we need to