Create adapter for training llama model

I want to create small adapter for asking llama model about my abstract game, but when I query it afterward, it doesn't seem to know anything about game. What might be the problem?

This is text of for train dataset.

[
    {
        "input": "Karapulkatanus is pc game?",
        "output": "Karapulkatanus is an action-adventure survival game developed and published by MyHomeAbstractGames."
    },
    ...
]

this is my code for creating adapter

from transformers import AutoTokenizer, AutoModelForCausalLM, TrainingArguments
from peft import LoraConfig, get_peft_model, TaskType
from datasets import Dataset
access_token='hf_xxxx' #my token from huggingface
# Open and read the JSON file
with open('training_data.json', 'r') as file:
    data = json.load(file)

dataset = Dataset.from_list(data)

model_name = "meta-llama/Llama-3.2-1B-Instruct"

tokenizer = AutoTokenizer.from_pretrained(model_name,token=access_token)
tokenizer.pad_token = tokenizer.eos_token  
model = AutoModelForCausalLM.from_pretrained(model_name,token=access_token)


def preprocess_function(examples):
    inputs = tokenizer(
        examples["input"], max_length=512, truncation=True, padding="max_length"
    )
    outputs = tokenizer(
        examples["output"], max_length=512, truncation=True, padding="max_length"
    )
    inputs["labels"] = outputs["input_ids"]
    inputs["loss_weights"] = [2.0 if token == tokenizer.encode("Karapulkatanus")[0] else 1.0 for token in inputs["input_ids"]]
    return inputs

tokenized_dataset = dataset.map(preprocess_function, batched=True)


lora_config = LoraConfig(
    task_type=TaskType.CAUSAL_LM,
    inference_mode=False,
    r=8,
    lora_alpha=32,
    lora_dropout=0.05,
)


model = get_peft_model(model, lora_config)


training_args = TrainingArguments(
    output_dir="./llama_with_adapter",
    per_device_train_batch_size=1,
    num_train_epochs=3,
    save_steps=500,
    logging_dir="./logs",
    gradient_accumulation_steps=8,
    eval_strategy="no",
    learning_rate=5e-5,  
    weight_decay=0.01,
    logging_steps=10,
)


from transformers import Trainer

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=tokenized_dataset,
    tokenizer=tokenizer,
)

trainer.train()

model.save_pretrained("./llama_with_adapter")

script will make folder llama_with_adapter with files after that I will ask model about this game. And model don't know anything about this game.

from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel
import os


base_model_path = "meta-llama/Llama-3.2-1B-Instruct" 
adapter_path = "./llama_with_adapter" 


offload_dir = "./offload"
os.makedirs(offload_dir, exist_ok=True)


tokenizer = AutoTokenizer.from_pretrained(base_model_path)

tokenizer.pad_token = tokenizer.eos_token

base_model = AutoModelForCausalLM.from_pretrained(
    base_model_path,
    device_map="cpu",         
    offload_folder=offload_dir 
)


model = PeftModel.from_pretrained(base_model, adapter_path)


model.eval()
example_input = "Karapulkatanus is pc game?"
input_ids = tokenizer(example_input, return_tensors="pt").input_ids.to(model.device)
outputs = model.generate(input_ids, max_length=500)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))