optimisation

Finetunning/finetunning.py

     AutoTokenizer,
     AutoModelForCausalLM,
     TrainingArguments,
-    BitsAndBytesConfig
+    BitsAndBytesConfig,
 )
 from peft import (
     LoraConfig,
 print(f"Max sequence length: {tokenizer.model_max_length}")
 
 # ----------------------------
-# [2/7] Model loading (QLoRA)
+# [2/7] Quantization config (QLoRA)
 # ----------------------------
-print(f"{80 * '_'}\n[2/7] Loading model in 4-bit mode (QLoRA)...")
+print(f"{80 * '_'}\n[2/7] Configuring 4-bit quantization (BitsAndBytes)...")
+bnb_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_compute_dtype=torch.float16,
+    bnb_4bit_use_double_quant=True,
+)
+
+print("4-bit NF4 quantization configured.")
+
+print("Loading model...")
 model = AutoModelForCausalLM.from_pretrained(
     MODEL_NAME,
-    load_in_4bit=True,
     device_map="auto",
+    quantization_config=bnb_config,
     dtype=torch.float16,
     trust_remote_code=True,
 )
-print("Model loaded.")
+print("Model loaded successfully.")
 
 # ----------------------------
 # [3/7] Prepare model for k-bit training
     bias="none",
     task_type="CAUSAL_LM",
     target_modules=[
-        "q_proj", "k_proj", "v_proj", "o_proj",
-        "gate_proj", "up_proj", "down_proj"
+        "q_proj",
+        "k_proj",
+        "v_proj",
+        "o_proj",
+        "gate_proj",
+        "up_proj",
+        "down_proj",
     ],
-
 )
 
 model = get_peft_model(model, lora_config)
 # [5/7] Dataset loading & formatting
 # ----------------------------
 print(f"{80 * '_'}\n[5/7] Loading dataset from JSON file...")
-dataset = load_dataset(
-    "json",
-    data_files=DATA_FILE
-)
+dataset = load_dataset("json", data_files=DATA_FILE)
 
 print(f"Dataset loaded with {len(dataset['train'])} samples.")
 
 )
 
 print("Dataset formatting completed.")
-print(f"Example prompt:\n{dataset['train'][0]['text']}")
+print("Example prompt:\n")
+print(dataset["train"][0]["text"])
 
 # ----------------------------
 # [6/7] Training arguments
 print("Training arguments ready.")
 print(f"Output directory: {OUTPUT_DIR}")
 print(f"Epochs: {training_args.num_train_epochs}")
-print(f"Effective batch size: {training_args.per_device_train_batch_size * training_args.gradient_accumulation_steps}")
+print(
+    f"Effective batch size: "
+    f"{training_args.per_device_train_batch_size * training_args.gradient_accumulation_steps}"
+)
 
 # ----------------------------
 # Trainer

Traduction/main.py

 import json
 from reportlab.lib.pagesizes import letter
 from reportlab.lib.units import inch
-from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer, Flowable
+from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer
 from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle
 from reportlab.lib.enums import TA_JUSTIFY
 from reportlab.pdfbase import pdfmetrics
 from reportlab.pdfbase.ttfonts import TTFont
-import os
+import os, time
 
 # Configuration
 DEBUG = True
-PDF_PATH = "Traduction\TaniaBorecMemoir(Ukr).pdf"
+PDF_PATH = "Traduction/TaniaBorecMemoir(Ukr).pdf"
 OLLAMA_MODEL = "traductionUkrainienVersFrancais:latest"
 OLLAMA_URL = "http://localhost:11434/api/generate"
 TARGET_LANGUAGE = "français"
-CHECKPOINT_FILE = "Traduction\checkpoint.json"
-TEMP_OUTPUT_TXT = "Traduction\output_temp.txt"
-FINAL_OUTPUT_PDF = PDF_PATH.replace(".pdf",f"({TARGET_LANGUAGE.upper()[:2]})_V8.pdf")
-FINAL_OUTPUT_TXT = PDF_PATH.replace(".pdf",f"({TARGET_LANGUAGE.upper()[:2]})_V8.txt")
+CHECKPOINT_FILE = "Traduction/checkpoint.json"
+TEMP_OUTPUT_TXT = "Traduction/output_temp.txt"
+FINAL_OUTPUT_PDF = PDF_PATH.replace(".pdf",f"({TARGET_LANGUAGE.upper()[:2]})_V9.pdf")
+FINAL_OUTPUT_TXT = PDF_PATH.replace(".pdf",f"({TARGET_LANGUAGE.upper()[:2]})_V9.txt")
 
 DEBUG = True
 
     print(f"Batches manquants détectés : {missing_batches}")
 
     # Traduction des paragraphes manquants
+    temps_cumule = 0.0
     for i in missing_batches:
         batch = paragraphs[i:i + batch_size]
         paragraph_cumul = "\n".join(batch)
         print(f"{15 * '-'} Traduction des paragraphes manquants {i+1} à {min(i + batch_size, len(paragraphs))} / {len(paragraphs)}")
 
         try:
+            debut_chrono = time.time()
             result = send_to_ollama(paragraph_cumul)
+            fin_chrono = time.time()
+            temps_paragraphe = fin_chrono - debut_chrono
+            temps_cumule += temps_paragraphe
+            
+            # Conversion en minutes et secondes
+            minutes_paragraphe, secondes_paragraphe = divmod(temps_paragraphe, 60)
+            minutes_cumule, secondes_cumule = divmod(temps_cumule, 60)
+            
             print(f"{result}")
             results[str(i)] = result
             save_checkpoint(len(paragraphs), results)  # Met à jour le dernier indice du batch
             save_temp_results(results)
         except Exception as e:
             print(f"Erreur lors de la traduction du paragraphe {i}: {e}")
+        print(f"  Temps de traduction : {int(minutes_paragraphe)} min {secondes_paragraphe:.2f} sec")
+        print(f"  Temps cumulé : {int(minutes_cumule)} min {secondes_cumule:.2f} sec")
 
     # Traitement des paragraphes suivants
     for i in range(last_index + 1, len(paragraphs), batch_size):
         print(f"{15 * '-'} Traduction des paragraphes {i+1} à {min(i + batch_size, len(paragraphs))} / {len(paragraphs)}")
 
         try:
+            debut_chrono = time.time()
             result = send_to_ollama(paragraph_cumul)
+            fin_chrono = time.time()
+            temps_paragraphe = fin_chrono - debut_chrono
+            temps_cumule += temps_paragraphe
+            
+            # Conversion en minutes et secondes
+            minutes_paragraphe, secondes_paragraphe = divmod(temps_paragraphe, 60)
+            minutes_cumule, secondes_cumule = divmod(temps_cumule, 60)
+
             print(f"{result}")
             results[str(i)] = result
             save_checkpoint(i + batch_size - 1, results)
         except Exception as e:
             print(f"Erreur : {e}")
             continue
+        print(f"  Temps de traduction : {int(minutes_paragraphe)} min {secondes_paragraphe:.2f} sec")
+        print(f"  Temps cumulé : {int(minutes_cumule)} min {secondes_cumule:.2f} sec")
+
 
     save_temp_results(results)
     create_pdf_from_results(results, FINAL_OUTPUT_PDF)

run.bat

 call %VENV_PATH%\Scripts\activate.bat
 
 REM Lancer la compilation du modèle LLM pour Ollama
+echo Compilation du modèle LLM pour Ollama
 ollama create traductionUkrainienVersFrancais -f .\Traduction\Modelfile
 
 :: 1. Vérifie si le processus ollama.exe est en cours d'exécution
 )
 
 REM Exécuter le script principal
+echo Lancement du script principal de traduction
 python %MAIN_SCRIPT_PATH%
 
 endlocal