Create finetune_full.py

finetune_full.py

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+# finetune_full.py
+import torch
+from datasets import load_dataset
+from transformers import AutoTokenizer, AutoModelForCausalLM, TrainingArguments, Trainer
+import os
+
+os.environ["OMP_NUM_THREADS"] = "8"
+
+base_model = "mistralai/Mistral-7B-Instruct-v0.3"
+new_model_dir = "./mistral-7b-brvm-full-finetuned"
+output_dir = "./results_full"
+
+# 1. Dataset
+dataset = load_dataset("lamekemal/brvm_finetune")
+
+# 2. Charger modèle + tokenizer en FP16
+model = AutoModelForCausalLM.from_pretrained(
+    base_model,
+    torch_dtype=torch.float16,
+    device_map="auto",
+    trust_remote_code=True,
+)
+model.config.use_cache = False
+model.gradient_checkpointing_enable()
+
+tokenizer = AutoTokenizer.from_pretrained(base_model, trust_remote_code=True)
+if tokenizer.pad_token is None:
+    tokenizer.pad_token = tokenizer.eos_token
+tokenizer.padding_side = "right"
+
+# 3. Prétraitement
+def tokenize_function(examples):
+    texts = [
+        f"Instruction: {instr}\nRéponse: {resp}"
+        for instr, resp in zip(examples["instruction"], examples["response"])
+    ]
+    return tokenizer(
+        texts,
+        truncation=True,
+        padding="max_length",
+        max_length=512,
+    )
+
+tokenized_datasets = dataset.map(tokenize_function, batched=True)
+
+# 4. Arguments d’entraînement
+training_args = TrainingArguments(
+    output_dir=output_dir,
+    num_train_epochs=3,
+    per_device_train_batch_size=4,   # full finetune = VRAM lourd
+    gradient_accumulation_steps=4,
+    optim="adamw_torch_fused",
+    save_steps=100,
+    logging_steps=10,
+    learning_rate=2e-5,
+    fp16=True,
+    max_grad_norm=1.0,
+    warmup_ratio=0.03,
+    lr_scheduler_type="cosine",
+    report_to="tensorboard",
+    evaluation_strategy="steps",
+    eval_steps=100,
+    save_total_limit=2,
+    load_best_model_at_end=True,
+    metric_for_best_model="eval_loss",
+)
+
+# 5. Trainer classique (pas LoRA)
+trainer = Trainer(
+    model=model,
+    train_dataset=tokenized_datasets["train"],
+    eval_dataset=tokenized_datasets["validation"],
+    args=training_args,
+)
+
+# 6. Entraînement
+trainer.train()
+
+# 7. Sauvegarde locale et push Hub
+trainer.save_model(new_model_dir)
+tokenizer.save_pretrained(new_model_dir)
+
+from huggingface_hub import HfApi
+api = HfApi()
+repo_id = "lamekemal/mistral-7b-brvm-full-finetuned"
+trainer.push_to_hub(repo_id)
+tokenizer.push_to_hub(repo_id)
+
+print(f"✅ Full fine-tune sauvegardé dans {new_model_dir} et poussé sur Hugging Face Hub")