Upload train.py with huggingface_hub

train.py

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+# /// script
+# dependencies = ["trl>=0.12.0", "peft>=0.7.0", "trackio", "datasets", "transformers", "accelerate"]
+# ///
+
+from datasets import load_dataset
+from peft import LoraConfig
+from trl import SFTTrainer, SFTConfig
+import trackio
+import os
+
+print("🚀 Starting FunctionGemma 2B Fine-tuning")
+
+# Load dataset
+dataset = load_dataset("epinfomax/vn-function-calling-dataset", split="train")
+
+# Training configuration
+config = SFTConfig(
+    output_dir="vn-function-gemma-finetuned",
+    push_to_hub=True,
+    hub_model_id="epinfomax/vn-function-gemma-finetuned",
+    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=50,
+    report_to="trackio",
+    project="vn-function-calling",
+    run_name="function-gemma-2b-baseline"
+)
+
+# LoRA configuration
+peft_config = LoraConfig(
+    r=16,
+    lora_alpha=32,
+    target_modules=["q_proj", "v_proj", "k_proj", "o_proj"],
+    task_type="CAUSAL_LM",
+)
+
+# Initialize and train
+trainer = SFTTrainer(
+    model="google/function-gemma-2b",
+    train_dataset=dataset,
+    peft_config=peft_config,
+    args=config,
+)
+
+trainer.train()
+trainer.push_to_hub()
+print("✅ Training complete and pushed to Hub!")