ne pas versioner le lora

Finetunning/.gitignore

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+qwen2.5*/

Finetunning/mergeLora.py

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+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+
+# ----------------------------
+# Configuration
+# ----------------------------
+BASE_MODEL = "Qwen/Qwen2.5-7B-Instruct"
+LORA_DIR = "./qwen2.5-7b-uk-fr-lora"      # dossier issu du fine-tuning
+OUTPUT_DIR = "./qwen2.5-7b-uk-fr-merged"  # modèle fusionné final
+
+DTYPE = torch.float16   # GGUF-friendly
+DEVICE = "cpu"          # merge sur CPU (stable, sûr)
+
+print("=== LoRA merge script started ===")
+
+# ----------------------------
+# Load base model
+# ----------------------------
+print("[1/4] Loading base model...")
+base_model = AutoModelForCausalLM.from_pretrained(
+    BASE_MODEL,
+    torch_dtype=DTYPE,
+    device_map=DEVICE,
+    trust_remote_code=True,
+)
+print("Base model loaded.")
+
+# ----------------------------
+# Load tokenizer
+# ----------------------------
+print("[2/4] Loading tokenizer...")
+tokenizer = AutoTokenizer.from_pretrained(
+    BASE_MODEL,
+    trust_remote_code=True
+)
+tokenizer.pad_token = tokenizer.eos_token
+print("Tokenizer loaded.")
+
+# ----------------------------
+# Load LoRA adapter
+# ----------------------------
+print("[3/4] Loading LoRA adapter...")
+model = PeftModel.from_pretrained(
+    base_model,
+    LORA_DIR,
+)
+print("LoRA adapter loaded.")
+
+# ----------------------------
+# Merge LoRA into base model
+# ----------------------------
+print("[4/4] Merging LoRA into base model...")
+model = model.merge_and_unload()
+print("LoRA successfully merged.")
+
+# ----------------------------
+# Save merged model
+# ----------------------------
+print("Saving merged model...")
+model.save_pretrained(
+    OUTPUT_DIR,
+    safe_serialization=True,
+)
+tokenizer.save_pretrained(OUTPUT_DIR)
+
+print("=== Merge completed successfully ===")
+print(f"Merged model saved in: {OUTPUT_DIR}")