Upload train_pentest_sft.py with huggingface_hub

train_pentest_sft.py

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+# /// script
+# dependencies = [
+#     "trl>=0.12.0",
+#     "peft>=0.7.0",
+#     "transformers>=4.36.0",
+#     "accelerate>=0.24.0",
+#     "trackio",
+# ]
+# ///
+
+from datasets import load_dataset
+from peft import LoraConfig
+from trl import SFTTrainer, SFTConfig
+
+# Load dataset (ChatML format)
+print("Loading pentest dataset...")
+dataset = load_dataset(
+    "jason-oneal/pentest-agent-dataset",
+    data_files="chatml_train.jsonl",
+    split="train"
+)
+print(f"Dataset loaded: {len(dataset)} examples")
+
+# Train/eval split
+dataset_split = dataset.train_test_split(test_size=0.1, seed=42)
+train_dataset = dataset_split["train"]
+eval_dataset = dataset_split["test"]
+
+# Training configuration
+config = SFTConfig(
+    output_dir="qwen2.5-coder-1.5b-pentest",
+    push_to_hub=True,
+    hub_model_id="fawazo/qwen2.5-coder-1.5b-pentest",
+    hub_strategy="every_save",
+    
+    num_train_epochs=3,
+    per_device_train_batch_size=4,
+    gradient_accumulation_steps=4,
+    learning_rate=2e-5,
+    
+    logging_steps=10,
+    save_strategy="steps",
+    save_steps=200,
+    save_total_limit=2,
+    
+    eval_strategy="steps",
+    eval_steps=200,
+    
+    warmup_ratio=0.1,
+    lr_scheduler_type="cosine",
+    
+    report_to="trackio",
+    project="pentest-coder",
+    run_name="qwen2.5-coder-1.5b-sft",
+)
+
+# LoRA config for efficient training
+peft_config = LoraConfig(
+    r=16,
+    lora_alpha=32,
+    lora_dropout=0.05,
+    bias="none",
+    task_type="CAUSAL_LM",
+    target_modules=["q_proj", "k_proj", "v_proj", "o_proj"],
+)
+
+# Train
+print("Starting training...")
+trainer = SFTTrainer(
+    model="Qwen/Qwen2.5-Coder-1.5B",
+    train_dataset=train_dataset,
+    eval_dataset=eval_dataset,
+    args=config,
+    peft_config=peft_config,
+)
+
+trainer.train()
+trainer.push_to_hub()
+print("Model saved to: https://huggingface.co/fawazo/qwen2.5-coder-1.5b-pentest")