初始化项目，由ModelHub XC社区提供模型

Model: npc-worldwide/TinyTimV1
Source: Original Platform

fine_tune_joyce.py

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+from transformers import AutoTokenizer, AutoModelForCausalLM, Trainer, TrainingArguments
+from datasets import load_dataset, load_from_disk
+
+dataset = load_from_disk('finn_wake_dataset')
+
+tokenizer = AutoTokenizer.from_pretrained("tinyllama/tinyllama-1.1b-chat-v1.0")
+
+tokenizer.save_pretrained(".results/checkpoint-12000/")
+
+model = AutoModelForCausalLM.from_pretrained("tinyllama/tinyllama-1.1b-chat-v1.0")
+
+if tokenizer.pad_token is None:
+    print("Tokenizer does not have a pad token set. Setting pad_token to eos_token.")
+    tokenizer.pad_token = tokenizer.eos_token
+
+def tokenize_function(examples):
+    
+    tokenized_inputs = tokenizer(examples['text'], padding="max_length", truncation=True, max_length=128)
+    tokenized_inputs["labels"] = tokenized_inputs["input_ids"].copy()
+    
+    return  tokenized_inputs
+    
+tokenized_dataset = dataset.map(tokenize_function, batched=True, remove_columns=["text"])
+train_test_split = tokenized_dataset.train_test_split(test_size=0.1)  
+
+train_dataset = train_test_split['train']
+eval_dataset = train_test_split['test']
+training_args = TrainingArguments(
+    output_dir="./results",
+    num_train_epochs=3, 
+    per_device_train_batch_size=1,  
+    warmup_steps=500,
+    weight_decay=0.01,
+    logging_dir="./logs",
+    logging_steps=10,
+    save_strategy="steps",
+    save_steps=500,
+    save_total_limit=2,
+    use_cpu=True)
+trainer = Trainer(
+    model=model,  
+    args=training_args,
+    train_dataset=train_dataset, 
+    eval_dataset=eval_dataset, 
+)
+#below has been modified because i ran out of disk storage initially so had to resume and adjust the save_strategy above.
+trainer.train(resume_from_checkpoint="./results/checkpoint-10000")