version fonctionnelle

Finetunning/finetunning.py

@@ -15,9 +15,12 @@ from peft import (
 from trl import SFTTrainer
 
 # ----------------------------
-# Environment safety (Windows)
+# Environment safety (Windows + AMP fixes)
 # ----------------------------
 os.environ["TORCHDYNAMO_DISABLE"] = "1"
+os.environ["ACCELERATE_MIXED_PRECISION"] = "no"  # ✅ disable AMP completely
+os.environ["TORCH_AMP_DISABLE"] = "1"            # ✅ disable GradScaler
+os.environ["CUDA_VISIBLE_DEVICES"] = "0"         # optional: force first GPU
 
 # ----------------------------
 # Global configuration
@@ -25,7 +28,7 @@ os.environ["TORCHDYNAMO_DISABLE"] = "1"
 MODEL_NAME = "Qwen/Qwen2.5-7B-Instruct"
 OUTPUT_DIR = "./qwen2.5-7b-uk-fr-lora"
 DATA_FILE = "paires_clean.json"
-MAX_SEQ_LENGTH = 1024
+MAX_SEQ_LENGTH = 512  # Reduce for RTX 4080 SUPER VRAM
 
 print(f"\n=== Starting fine-tuning script for {MODEL_NAME} ===\n")
 
@@ -35,54 +38,50 @@ print(f"\n=== Starting fine-tuning script for {MODEL_NAME} ===\n")
 print(f"{80 * '_'}\n[1/7] Loading tokenizer...")
 tokenizer = AutoTokenizer.from_pretrained(
     MODEL_NAME,
-    trust_remote_code=True
+    trust_remote_code=True,
 )
-
 tokenizer.pad_token = tokenizer.eos_token
 tokenizer.model_max_length = MAX_SEQ_LENGTH
-
 print("Tokenizer loaded.")
 print(f"Pad token id: {tokenizer.pad_token_id}")
 print(f"Max sequence length: {tokenizer.model_max_length}")
 
 # ----------------------------
-# [2/7] Quantization config (QLoRA)
+# [2/7] Load model in 4-bit (QLoRA)
 # ----------------------------
-print(f"{80 * '_'}\n[2/7] Loading model in 4-bit mode (optimized QLoRA)...")
+print(f"{80 * '_'}\n[2/7] Loading model in 4-bit mode (QLoRA)...")
-
 assert torch.cuda.is_available(), "CUDA GPU not detected!"
 print(f"Using GPU: {torch.cuda.get_device_name(0)}")
 
 bnb_config = BitsAndBytesConfig(
     load_in_4bit=True,
     bnb_4bit_quant_type="nf4",
-    bnb_4bit_compute_dtype=torch.float16,
+    bnb_4bit_compute_dtype=torch.float16,  # fp16 internally
     bnb_4bit_use_double_quant=True,
 )
 
 model = AutoModelForCausalLM.from_pretrained(
     MODEL_NAME,
-    device_map="cuda",          # 🔥 SAFE
+    device_map="auto",
     quantization_config=bnb_config,
     low_cpu_mem_usage=True,
     trust_remote_code=True,
 )
 
+# Align model tokens with tokenizer
+model.config.pad_token_id = tokenizer.pad_token_id
+model.config.bos_token_id = tokenizer.bos_token_id
+model.config.eos_token_id = tokenizer.eos_token_id
 print("Model loaded successfully in 4-bit mode on GPU.")
 
-
 # ----------------------------
 # [3/7] Prepare model for k-bit training
 # ----------------------------
 print(f"{80 * '_'}\n[3/7] Preparing model for k-bit training...")
 model = prepare_model_for_kbit_training(model)
-
+model.gradient_checkpointing_enable(gradient_checkpointing_kwargs={"use_reentrant": False})
-model.gradient_checkpointing_enable(
+model.config.use_cache = False  # Important with gradient checkpointing + QLoRA
-    gradient_checkpointing_kwargs={"use_reentrant": False}
-)
-
 print("Model prepared for k-bit training.")
-print("Gradient checkpointing enabled (non-reentrant).")
 
 # ----------------------------
 # [4/7] LoRA configuration
@@ -104,10 +103,8 @@ lora_config = LoraConfig(
         "down_proj",
     ],
 )
-
 model = get_peft_model(model, lora_config)
 model.print_trainable_parameters()
-
 print("LoRA adapters successfully attached.")
 
 # ----------------------------
@@ -115,18 +112,19 @@ print("LoRA adapters successfully attached.")
 # ----------------------------
 print(f"{80 * '_'}\n[5/7] Loading dataset from JSON file...")
 dataset = load_dataset("json", data_files=DATA_FILE)
-
 print(f"Dataset loaded with {len(dataset['train'])} samples.")
 
 print("Formatting dataset for Ukrainian → French translation...")
-
 def format_prompt(example):
     return {
-        "text": ("<|user|>\n"
+        "text": (
+            "<|im_start|>user\n"
             "Translate the following Ukrainian text into French.\n"
             f"Ukrainian: {example['text']}\n"
-            "<|assistant|>\n"
+            "<|im_end|>\n"
+            "<|im_start|>assistant\n"
             f"{example['translation']}"
+            "<|im_end|>"
         )
     }
 
@@ -134,7 +132,6 @@ dataset = dataset.map(
     format_prompt,
     remove_columns=dataset["train"].column_names
 )
-
 print("Dataset formatting completed.")
 print("Example prompt:\n")
 print(dataset["train"][0]["text"])
@@ -146,46 +143,49 @@ print(f"{80 * '_'}\n[6/7] Initializing training arguments...")
 training_args = TrainingArguments(
     output_dir=OUTPUT_DIR,
     per_device_train_batch_size=1,
-    gradient_accumulation_steps=8,
+    gradient_accumulation_steps=16,
     learning_rate=1e-4,
     num_train_epochs=3,
-    fp16=False,
+    max_steps=1000,
-    bf16=False,
+
+    fp16=False,      # ⚠ disable AMP
+    bf16=False,      # ⚠ disable BF16
+
     optim="paged_adamw_32bit",
     logging_steps=10,
     save_steps=500,
     save_total_limit=2,
     report_to="none",
-    dataloader_pin_memory=False,
-)
 
+    dataloader_pin_memory=False,
+    max_grad_norm=0.0,  # avoid AMP gradient clipping
+)
 print("Training arguments ready.")
 print(f"Output directory: {OUTPUT_DIR}")
 print(f"Epochs: {training_args.num_train_epochs}")
-print(f"Effective batch size: "
+print(f"Effective batch size: {training_args.per_device_train_batch_size * training_args.gradient_accumulation_steps}")
-    f"{training_args.per_device_train_batch_size * training_args.gradient_accumulation_steps}"
-)
 
 # ----------------------------
-# Trainer
+# [7/7] Trainer
 # ----------------------------
-print("Initializing SFTTrainer...")
+print(f"{80 * '_'}\nInitializing SFTTrainer...")
 trainer = SFTTrainer(
     model=model,
     train_dataset=dataset["train"],
-    processing_class=tokenizer,
     args=training_args,
 )
 print("Trainer initialized.")
 
 # ----------------------------
-# [7/7] Training
+# Training
 # ----------------------------
-print(f"{80 * '_'}\n[7/7] Starting training...")
+print(f"{80 * '_'}\nStarting training...")
-checkpoint_exists = any(
+checkpoint_exists = False
+if os.path.exists(OUTPUT_DIR):
+    checkpoint_exists = any(
         d.startswith("checkpoint-")
         for d in os.listdir(OUTPUT_DIR)
-) if os.path.exists(OUTPUT_DIR) else False
+    )
 
 if checkpoint_exists:
     print("Checkpoint found → resuming training")
@@ -194,7 +194,6 @@ else:
     print("No checkpoint found → starting fresh training")
     train_output = trainer.train()
 
-
 print("\n=== Training summary ===")
 print(f"Global steps: {train_output.global_step}")
 print(f"Training loss: {train_output.training_loss}")
@@ -202,11 +201,10 @@ print(f"Metrics: {train_output.metrics}")
 print("Training completed successfully.")
 
 # ----------------------------
-# Save LoRA adapter
+# Save LoRA adapter and tokenizer
 # ----------------------------
 print(f"{80 * '_'}\nSaving LoRA adapter and tokenizer...")
 trainer.model.save_pretrained(OUTPUT_DIR)
 tokenizer.save_pretrained(OUTPUT_DIR)
-
 print("\n=== Fine-tuning finished ===")
 print(f"LoRA adapter saved in: {OUTPUT_DIR}")

README.md

@@ -27,7 +27,7 @@ pip install -r requirements.txt
 
 Puis faire :
 ```bash
-pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
+pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121
 ```
 
 3. **Placer votre fichier PDF** dans le répertoire `Traduction` du projet avec le nom configuré dans `main.py` (par défaut : `TaniaBorecMemoir(Ukr).pdf`)