Create app.py

app.py

@@ -0,0 +1,53 @@
+from unsloth import FastLanguageModel
+from trl import SFTTrainer
+from transformers import TrainingArguments
+from datasets import load_dataset
+
+model, tokenizer = FastLanguageModel.from_pretrained(
+    "unsloth/gemma-2-2b-it",
+    max_seq_length = 2048,
+    load_in_4bit = True,
+)
+
+model = FastLanguageModel.get_peft_model(
+    model,
+    r = 64,
+    target_modules = ["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"],
+    lora_alpha = 32,
+    lora_dropout = 0,
+    bias = "none",
+    use_gradient_checkpointing = "unsloth",
+    random_state = 3407,
+)
+
+dataset = load_dataset("json", data_files="python_security_dataset.json", split="train")
+
+trainer = SFTTrainer(
+    model = model,
+    tokenizer = tokenizer,
+    train_dataset = dataset,
+    dataset_text_field = "messages",
+    max_seq_length = 2048,
+    args = TrainingArguments(
+        per_device_train_batch_size = 2,
+        gradient_accumulation_steps = 4,
+        warmup_steps = 10,
+        max_steps = 300,
+        learning_rate = 2e-4,
+        fp16 = True,
+        logging_steps = 1,
+        output_dir = "k1ng_final",
+        optim = "adamw_8bit",
+    ),
+)
+
+trainer.train()
+
+model.save_pretrained("k1ng_by_alikay_h")
+tokenizer.save_pretrained("k1ng_by_alikay_h")
+
+# آپلود به HF
+from huggingface_hub import notebook_login, HfApi
+notebook_login()
+api = HfApi()
+api.upload_folder(folder_path="k1ng_by_alikay_h", repo_id="alikayh/k1ng-v1", repo_type="model")