Upload DPO-Training/merge_dpo_adapters.py with huggingface_hub

DPO-Training/merge_dpo_adapters.py

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+#!/usr/bin/env python3
+"""
+Merge LoRA adapters from downloaded DPO model with base Qwen3-0.6B.
+"""
+
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+import os
+
+BASE_MODEL = "Qwen/Qwen3-0.6B"
+LORA_PATH = "/home/ma/models/Qwen3-0.6B-DPO"
+OUTPUT_PATH = "/home/ma/models/Qwen3-0.6B-DPO-merged"
+
+print("=" * 60)
+print("Merging DPO LoRA Adapters")
+print("=" * 60)
+
+# Load tokenizer
+print("\n📥 Loading tokenizer...")
+tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL, trust_remote_code=True)
+
+# Load base model
+print("📥 Loading base model (this may take a few minutes on CPU)...")
+base_model = AutoModelForCausalLM.from_pretrained(
+    BASE_MODEL,
+    torch_dtype=torch.float32,
+    device_map="cpu",
+    trust_remote_code=True,
+)
+
+# Load LoRA adapters
+print("📥 Loading DPO LoRA adapters...")
+model = PeftModel.from_pretrained(base_model, LORA_PATH)
+
+# Merge adapters
+print("🔧 Merging adapters...")
+model = model.merge_and_unload()
+
+# Save merged model
+print(f"💾 Saving merged model to {OUTPUT_PATH}...")
+os.makedirs(OUTPUT_PATH, exist_ok=True)
+model.save_pretrained(OUTPUT_PATH)
+tokenizer.save_pretrained(OUTPUT_PATH)
+
+print("\n" + "=" * 60)
+print("✅ Merge Complete!")
+print("=" * 60)
+print(f"Merged model saved to: {OUTPUT_PATH}")
+print(
+    f"Size: {sum(os.path.getsize(os.path.join(OUTPUT_PATH, f)) for f in os.listdir(OUTPUT_PATH) if os.path.isfile(os.path.join(OUTPUT_PATH, f))) / (1024 * 1024):.1f} MB"
+)