Create train.py

train.py

@@ -0,0 +1,47 @@
+from datasets import load_dataset
+from transformers import AutoTokenizer, AutoModelForCausalLM, TrainingArguments, Trainer, DataCollatorForLanguageModeling
+
+model_id = "bigcode/starcoderbase-7b"
+dataset_repo = "Percy3822/python_ai_coder"  # Your HF dataset repo
+
+# Load dataset
+dataset = load_dataset(dataset_repo, split="train")
+
+# Load tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True)
+
+# Tokenize
+def tokenize(example):
+    result = tokenizer(example["prompt"] + "\n" + example["completion"], truncation=True, max_length=512)
+    return result
+
+tokenized = dataset.map(tokenize, remove_columns=["prompt", "completion"])
+data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)
+
+# Training config
+args = TrainingArguments(
+    output_dir="./python-ai-model",
+    per_device_train_batch_size=2,
+    gradient_accumulation_steps=4,
+    num_train_epochs=3,
+    logging_steps=50,
+    save_steps=500,
+    save_total_limit=2,
+    evaluation_strategy="no",
+    fp16=True,
+    push_to_hub=True,
+    hub_model_id="Percy3822/python_ai_coder",
+    hub_token="<your_HF_token_here>"  # Optional if you run in a linked HF Space
+)
+
+trainer = Trainer(
+    model=model,
+    train_dataset=tokenized,
+    tokenizer=tokenizer,
+    args=args,
+    data_collator=data_collator,
+)
+
+trainer.train()
+trainer.push_to_hub()