Create train.py

train.py

@@ -0,0 +1,42 @@
+from transformers import AutoTokenizer, AutoModelForCausalLM, Trainer, TrainingArguments, TextDataset, DataCollatorForLanguageModeling
+
+model_id = "mistralai/Mistral-7B-Instruct" # if you have resources
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id)
+
+# Tokenize your dataset
+def load_dataset(file_path, tokenizer, block_size=512):
+    return TextDataset(
+        tokenizer=tokenizer,
+        file_path=file_path,
+        block_size=block_size
+    )
+
+train_dataset = load_dataset("coaching_data.txt", tokenizer)
+
+data_collator = DataCollatorForLanguageModeling(
+    tokenizer=tokenizer, mlm=False,
+)
+
+training_args = TrainingArguments(
+    output_dir="./skilllink-coach",
+    overwrite_output_dir=True,
+    num_train_epochs=3,
+    per_device_train_batch_size=2,
+    save_steps=100,
+    save_total_limit=1,
+    logging_dir="./logs",
+    fp16=True,  # If using GPU
+)
+
+trainer = Trainer(
+    model=model,
+    args=training_args,
+    data_collator=data_collator,
+    train_dataset=train_dataset,
+)
+
+trainer.train()
+trainer.save_model("./skilllink-coach")
+tokenizer.save_pretrained("./skilllink-coach")