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python.traduction
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utilisation du GPU

This commit is contained in:
Alex 2026-01-15 16:59:25 +01:00
parent 70e4932cd0
commit fa3ad61dd7
1 changed files with 20 additions and 14 deletions
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34
Finetunning/finetunning.py
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@ -48,7 +48,11 @@ print(f"Max sequence length: {tokenizer.model_max_length}")
# ---------------------------- # ----------------------------
# [2/7] Quantization config (QLoRA) # [2/7] Quantization config (QLoRA)
# ---------------------------- # ----------------------------
print(f"{80 * '_'}\n[2/7] Configuring 4-bit quantization (BitsAndBytes)...") print(f"{80 * '_'}\n[2/7] Loading model in 4-bit mode (optimized QLoRA)...")
assert torch.cuda.is_available(), "CUDA GPU not detected!"
print(f"Using GPU: {torch.cuda.get_device_name(0)}")
bnb_config = BitsAndBytesConfig( bnb_config = BitsAndBytesConfig(
load_in_4bit=True, load_in_4bit=True,
bnb_4bit_quant_type="nf4", bnb_4bit_quant_type="nf4",
@ -56,17 +60,16 @@ bnb_config = BitsAndBytesConfig(
bnb_4bit_use_double_quant=True, bnb_4bit_use_double_quant=True,
) )
print("4-bit NF4 quantization configured.")
print("Loading model...")
model = AutoModelForCausalLM.from_pretrained( model = AutoModelForCausalLM.from_pretrained(
MODEL_NAME, MODEL_NAME,
device_map="auto", device_map="cuda", # 🔥 SAFE
quantization_config=bnb_config, quantization_config=bnb_config,
dtype=torch.float16, low_cpu_mem_usage=True,
trust_remote_code=True, trust_remote_code=True,
) )
print("Model loaded successfully.")
print("Model loaded successfully in 4-bit mode on GPU.")
# ---------------------------- # ----------------------------
# [3/7] Prepare model for k-bit training # [3/7] Prepare model for k-bit training
@ -119,8 +122,7 @@ print("Formatting dataset for Ukrainian → French translation...")
def format_prompt(example): def format_prompt(example):
return { return {
"text": ( "text": ("<|user|>\n"
"<|user|>\n"
"Translate the following Ukrainian text into French.\n" "Translate the following Ukrainian text into French.\n"
f"Ukrainian: {example['text']}\n" f"Ukrainian: {example['text']}\n"
"<|assistant|>\n" "<|assistant|>\n"
@ -154,13 +156,13 @@ training_args = TrainingArguments(
save_steps=500, save_steps=500,
save_total_limit=2, save_total_limit=2,
report_to="none", report_to="none",
dataloader_pin_memory=False,
) )
print("Training arguments ready.") print("Training arguments ready.")
print(f"Output directory: {OUTPUT_DIR}") print(f"Output directory: {OUTPUT_DIR}")
print(f"Epochs: {training_args.num_train_epochs}") print(f"Epochs: {training_args.num_train_epochs}")
print( print(f"Effective batch size: "
f"Effective batch size: "
f"{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}"
) )
@ -171,7 +173,7 @@ print("Initializing SFTTrainer...")
trainer = SFTTrainer( trainer = SFTTrainer(
model=model, model=model,
train_dataset=dataset["train"], train_dataset=dataset["train"],
tokenizer=tokenizer, processing_class=tokenizer,
args=training_args, args=training_args,
) )
print("Trainer initialized.") print("Trainer initialized.")
@ -181,12 +183,16 @@ print("Trainer initialized.")
# ---------------------------- # ----------------------------
print(f"{80 * '_'}\n[7/7] Starting training...") print(f"{80 * '_'}\n[7/7] Starting training...")
try: try:
trainer.train(resume_from_checkpoint=True) train_output = trainer.train(resume_from_checkpoint=True)
except Exception as e: except Exception as e:
print("No checkpoint found or resume failed, starting fresh training.") print("No checkpoint found or resume failed, starting fresh training.")
print(f"Reason: {e}") print(f"Reason: {e}")
trainer.train() train_output = trainer.train()
print("\n=== Training summary ===")
print(f"Global steps: {train_output.global_step}")
print(f"Training loss: {train_output.training_loss}")
print(f"Metrics: {train_output.metrics}")
print("Training completed successfully.") print("Training completed successfully.")
# ---------------------------- # ----------------------------