Add training script

train.py

@@ -0,0 +1,37 @@
+# /// script
+# dependencies = ["trl>=0.12.0", "transformers>=4.36.0", "accelerate>=0.24.0", "trackio"]
+# ///
+
+from datasets import load_dataset
+from transformers import AutoTokenizer, T5ForConditionalGeneration
+from trl import SFTTrainer, SFTConfig
+
+dataset = load_dataset("mindchain/container-status-de", split="train")
+split = dataset.train_test_split(test_size=0.15, seed=42)
+
+def fmt(ex):
+    return {"text": f"Status: {ex['text']}", "label": ex["label"]}
+
+train_ds = split["train"].map(fmt, remove_columns=split["train"].column_names)
+eval_ds = split["test"].map(fmt, remove_columns=split["test"].column_names)
+
+tokenizer = AutoTokenizer.from_pretrained("google/t5gemma-2-270m")
+model = T5ForConditionalGeneration.from_pretrained("google/t5gemma-2-270m")
+
+config = SFTConfig(
+    output_dir="out",
+    push_to_hub=True,
+    hub_model_id="mindchain/t5gemma-270m-container-status",
+    num_train_epochs=5,
+    per_device_train_batch_size=2,
+    gradient_accumulation_steps=4,
+    learning_rate=3e-4,
+    logging_steps=5,
+    max_length=256,
+    report_to="trackio",
+)
+
+trainer = SFTTrainer(model=model, tokenizer=tokenizer, train_dataset=train_ds, eval_dataset=eval_ds, args=config)
+trainer.train()
+trainer.push_to_hub()
+print('DONE')