Initial commit

merge.py

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+import os
+import torch
+import unsloth
+from unsloth import FastLanguageModel
+from unsloth.chat_templates import get_chat_template
+from peft import AutoPeftModelForCausalLM
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+print("="*60)
+print("UNSLOTH LORA")
+print("="*60)
+
+# ==========================================
+# 1. CONFIGURATION
+# ==========================================
+
+BASE_MODEL_PATH = ""
+LORA_DIR = "./model"
+MERGED_MODEL_PATH = "./merged_model"
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+print(f"Using device: {device}")
+
+# ==========================================
+# 2. LOAD BASE MODEL
+# ==========================================
+
+print("Loading Base Model from local path...")
+base_model = AutoModelForCausalLM.from_pretrained(
+    BASE_MODEL_PATH,
+    device_map="auto",
+    torch_dtype=torch.float16, # Adjust if you only did 4bit finetuning
+    trust_remote_code=True
+)
+
+print("✓ Base model loaded successfully")
+
+# ==========================================
+# 3. LOAD LORA ADAPTERS USING PEFT
+# ==========================================
+
+print("Loading LoRA adapters using PEFT...")
+lora_model = AutoPeftModelForCausalLM.from_pretrained(
+    LORA_DIR,
+    torch_dtype=torch.float16,  # Adjust if you only did 4bit finetuning
+    device_map="auto"
+)
+
+print("✓ LoRA adapters loaded successfully")
+
+# ==========================================
+# 4. MERGE LORA INTO BASE MODEL
+# ==========================================
+
+print("Merging LoRA adapters into base model...")
+merged_model = lora_model.merge_and_unload()
+
+print("✓ Adapters merged")
+
+# ==========================================
+# 5. CONFIGURE TOKENIZER
+# ==========================================
+
+print("Configuring tokenizer")
+
+tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL_PATH, trust_remote_code=True)
+# Set pad token
+tokenizer.pad_token = tokenizer.eos_token
+tokenizer.padding_side = "right"
+
+print("✓ Tokenizer configured")
+
+# ==========================================
+# 6. SAVE FOR VLLM DEPLOYMENT
+# ==========================================
+
+print("Saving merged model...")
+merged_model.save_pretrained(MERGED_MODEL_PATH, safe_serialization=True)
+tokenizer.save_pretrained(MERGED_MODEL_PATH)
+
+print(f"✓ Merged model saved to {MERGED_MODEL_PATH}")
+
+# ==========================================
+# 7. VERIFY OUTPUT
+# ==========================================
+
+model_files = os.listdir(MERGED_MODEL_PATH)
+print(f"\n✓ Model files created: {len(model_files)} files")
+print(f"  Files: {model_files}")