69 lines
1.8 KiB
Python
69 lines
1.8 KiB
Python
import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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from peft import PeftModel
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# ----------------------------
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# Configuration
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# ----------------------------
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BASE_MODEL = "Qwen/Qwen2.5-7B-Instruct"
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LORA_DIR = "./qwen2.5-7b-uk-fr-lora" # dossier issu du fine-tuning
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OUTPUT_DIR = "./qwen2.5-7b-uk-fr-merged" # modèle fusionné final
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DTYPE = torch.float16 # GGUF-friendly
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DEVICE = "cpu" # merge sur CPU (stable, sûr)
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print("=== LoRA merge script started ===")
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# ----------------------------
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# Load base model
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# ----------------------------
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print(f"{80 * '_'}\n[1/4] Loading base model...")
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base_model = AutoModelForCausalLM.from_pretrained(
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BASE_MODEL,
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torch_dtype=DTYPE,
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device_map=DEVICE,
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trust_remote_code=True,
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)
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print("Base model loaded.")
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# ----------------------------
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# Load tokenizer
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# ----------------------------
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print(f"{80 * '_'}\n[2/4] Loading tokenizer...")
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tokenizer = AutoTokenizer.from_pretrained(
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BASE_MODEL,
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trust_remote_code=True
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)
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tokenizer.pad_token = tokenizer.eos_token
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print("Tokenizer loaded.")
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# ----------------------------
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# Load LoRA adapter
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# ----------------------------
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print(f"{80 * '_'}\n[3/4] Loading LoRA adapter...")
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model = PeftModel.from_pretrained(
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base_model,
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LORA_DIR,
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)
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print("LoRA adapter loaded.")
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# ----------------------------
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# Merge LoRA into base model
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# ----------------------------
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print(f"{80 * '_'}\n[4/4] Merging LoRA into base model...")
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model = model.merge_and_unload()
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print("LoRA successfully merged.")
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# ----------------------------
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# Save merged model
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# ----------------------------
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print("Saving merged model...")
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model.save_pretrained(
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OUTPUT_DIR,
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safe_serialization=True,
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)
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tokenizer.save_pretrained(OUTPUT_DIR)
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print("=== Merge completed successfully ===")
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print(f"Merged model saved in: {OUTPUT_DIR}")
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