Hallucination issue on 13b-chat model

[np-n]

Hi, I have tested the performance of Me-LLaMA-13b-chat model on CPU but I ended with the random results. Model is often hallucinated. I have attached the example of the prompt and response below:

Prompt: how to treat flu in home?
Response: how to treat flu in home? How to treat flu in home? What are the home remedies for flu?

I have attached the code snippets below:

from transformers import AutoModelForCausalLM, AutoTokenizer

model_file = "./physionet.org/files/me-llama/1.0.0/MeLLaMA-13B-chat"

tokenizer = AutoTokenizer.from_pretrained(model_file)
model = AutoModelForCausalLM.from_pretrained(model_file)

# Tokenizing input text for the model.
 input_ids = tokenizer([prompt], return_tensors="pt").input_ids

# Generating output based on the input_ids.
# You can adjust the max_length parameter as necessary for your use case.
generated_tokens = model.generate(input_ids, max_length=1000)

# Decoding the generated tokens to produce readable text.
generated_text = tokenizer.decode(generated_tokens[0], skip_special_tokens=True)

print(generated_text)

Is that model is that much hallucinated? I have followed github documentation to load and infer the model.Is there are any hack that helps to generate the expected response from the model. Thank you.