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Script merge lora to HF

merge.py

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+import os, time
+import tempfile
+import json
+import torch
+import argparse
+import transformers
+from transformers import LlamaTokenizer, LlamaConfig, LlamaForCausalLM
+from peft import PeftModel, LoraConfig, LoraModel
+
+# args
+parser = argparse.ArgumentParser()
+# The original base model checkpoint dir
+parser.add_argument("--model_path", type=str, default='llama-7b-hf')
+# The finetuned lora model checkpoint dir
+parser.add_argument("--lora_path",type=str, default='lora')
+# The output dir
+parser.add_argument("--out_path", type=str, default='lora-merged')
+
+args = parser.parse_args()
+
+
+
+print(f">>> load model from {args.model_path} and lora from {args.lora_path}....")
+
+tokenizer = LlamaTokenizer.from_pretrained(args.model_path)
+
+#transformer loaded. load  model.
+
+model = LlamaForCausalLM.from_pretrained(
+    args.model_path,
+    load_in_8bit=False,
+    torch_dtype=torch.float16,
+    device_map={"": "cpu"},
+    
+)
+
+
+#peft loaded. load lora.
+model = PeftModel.from_pretrained(
+    model,
+    args.lora_path,
+    torch_dtype=torch.float16,
+    device_map={"": "cpu"},
+)
+
+print(f">>> merging lora...")
+
+#Why 'LlamaForCausalLM' object has no attribute 'merge_and_unload' ????????
+#okay, it works, i don't know why it didn't.
+
+model = model.merge_and_unload()
+model.save_pretrained(args.out_path)
+