initial upload

utils/merge_from_lora.py

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+#!/usr/bin/env python3
+"""
+Merge a LoRA adapter (saved by your finetune run) into the full-precision
+base model and write the merged fp16 weights to disk.
+
+Requires:
+  pip install unsloth peft transformers accelerate
+"""
+
+from pathlib import Path
+import torch
+from unsloth import FastLanguageModel
+from peft import PeftModel
+from transformers import AutoTokenizer
+
+# ---------------------------------------------------------------------
+# Adjust these three paths if your directory layout is different.
+# ---------------------------------------------------------------------
+BASE_MODEL   = "unsloth/gemma-3-4b-it"
+ADAPTER_DIR  = (
+    "results/auto_antislop_runs/run_20250608_102159/"
+    "finetuned_model_ftpo_exp01/lora_adapters"
+)
+OUT_DIR      = (
+    "results/auto_antislop_runs/run_20250608_102159/"
+    "finetuned_model_ftpo_exp01/merged_manual_fp16"
+)
+
+# ---------------------------------------------------------------------
+def main() -> None:
+    print("→ loading base model …")
+    base_model, _ = FastLanguageModel.from_pretrained(
+        model_name      = BASE_MODEL,
+        max_seq_length  = 4096,          # keep consistent with training
+        load_in_4bit    = False,         # full-precision
+        dtype           = torch.float16,
+        device_map      = {"": "cpu"},   # CPU merge; change to {"": 0} for GPU
+    )
+
+    print("→ plugging in LoRA adapter …")
+    peft_model = PeftModel.from_pretrained(
+        base_model,
+        ADAPTER_DIR,
+        device_map = {"": "cpu"},
+    )
+
+    print("→ merging and unloading …")
+    merged_model = peft_model.merge_and_unload()  # returns a plain nn.Module
+
+    Path(OUT_DIR).mkdir(parents=True, exist_ok=True)
+
+    print(f"→ saving merged model to {OUT_DIR} …")
+    merged_model.save_pretrained(
+        OUT_DIR,
+        safe_serialization = True,       # *.safetensors shards
+        max_shard_size     = "5GB",
+    )
+
+    # save the tokenizer so the directory is immediately usable
+    tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL)
+    tokenizer.save_pretrained(OUT_DIR)
+
+    print("✓ done")
+
+# ---------------------------------------------------------------------
+if __name__ == "__main__":
+    main()