Create hybridModel.py

hybridModel.py

@@ -0,0 +1,29 @@
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, AutoConfig
+
+# Load base model
+base_model_name = "NousResearch/Llama-2-13b-hf"
+base_model = AutoModelForCausalLM.from_pretrained(base_model_name)
+tokenizer = AutoTokenizer.from_pretrained(base_model_name)
+
+# Load LoRA weights
+lora_model_name = "FinGPT/fingpt-sentiment_llama2-13b_lora"
+lora_model = AutoModelForCausalLM.from_pretrained(lora_model_name)
+
+# Apply LoRA weights to the base model
+def apply_lora_weights(base_model, lora_model):
+    base_model_state_dict = base_model.state_dict()
+    lora_model_state_dict = lora_model.state_dict()
+
+    for name, param in lora_model_state_dict.items():
+        if name in base_model_state_dict:
+            base_model_state_dict[name].copy_(param)
+    
+    base_model.load_state_dict(base_model_state_dict)
+
+apply_lora_weights(base_model, lora_model)
+
+# Save the merged model
+output_dir = "./hybrid_model"
+base_model.save_pretrained(output_dir)
+tokenizer.save_pretrained(output_dir)