diff --git a/train_my_qlora.py b/train_my_qlora.py
new file mode 100644
index 0000000..2ab1e3f
--- /dev/null
+++ b/train_my_qlora.py
@@ -0,0 +1,88 @@
+import os
+from datasets import load_dataset
+from transformers import AutoTokenizer, AutoModelForCausalLM, TrainingArguments, Trainer
+from transformers import DataCollatorForLanguageModeling, BitsAndBytesConfig
+from peft import prepare_model_for_kbit_training, LoraConfig, get_peft_model
+from datasets import load_dataset
+
+# Configurações
+model_name = "mistralai/Mistral-7B-Instruct-v0.2"
+data_file_path = "../datasets/qa_dataset.json"  # <<== Ajuste aqui o nome do seu arquivo JSON
+output_dir = "./qlora-output"
+max_length = 512
+
+# Quantização com bitsandbytes
+bnb_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_use_double_quant=True,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_compute_dtype="float16"
+)
+
+# Tokenizador e modelo
+tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+tokenizer.pad_token = tokenizer.eos_token
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    quantization_config=bnb_config,
+    device_map="auto",
+    trust_remote_code=True
+)
+model = prepare_model_for_kbit_training(model)
+
+# LoRA
+peft_config = LoraConfig(
+    r=8,
+    lora_alpha=16,
+    target_modules=["q_proj", "k_proj", "v_proj", "o_proj"],
+    lora_dropout=0.05,
+    bias="none",
+    task_type="CAUSAL_LM"
+)
+model = get_peft_model(model, peft_config)
+
+# Dataset local em JSON
+dataset = load_dataset("json", data_files=data_file_path, split="train")
+
+# Tokenização
+def tokenize(example):
+    tokenized = tokenizer(
+        example["text"],
+        truncation=True,
+        max_length=max_length,
+        padding="max_length"
+    )
+    tokenized["labels"] = tokenized["input_ids"].copy()
+    return tokenized
+
+tokenized_dataset = dataset.map(tokenize, remove_columns=dataset.column_names)
+
+# Data collator
+data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)
+
+# Argumentos de treino
+training_args = TrainingArguments(
+    output_dir=output_dir,
+    per_device_train_batch_size=2,
+    gradient_accumulation_steps=4,
+    num_train_epochs=3,
+    learning_rate=2e-4,
+    fp16=True,
+    logging_steps=10,
+    save_strategy="epoch",
+    report_to="none"
+)
+
+# Trainer
+trainer = Trainer(
+    model=model,
+    args=training_args,
+    train_dataset=tokenized_dataset,
+    data_collator=data_collator
+)
+
+# Treino
+trainer.train()
+model.save_pretrained(output_dir)
+tokenizer.save_pretrained(output_dir)