train.py

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")