The code of deploying llama model in huggingface:
"
import os
import time
import spaces
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer, BitsAndBytesConfig
import gradio as gr
from threading import Thread

MODEL_LIST = ["meta-llama/Llama-3.2-1B-Instruct"]
HF_TOKEN = os.environ.get("HF_TOKEN", None)
MODEL = os.environ.get("MODEL_ID")

TITLE = "

Llama 3.2 1B
"
PLACEHOLDER = """

Hi, I'm Llama. Ask me anything.

"""
CSS = """
.duplicate-button {
margin: auto !important;
color: white !important;
background: black !important;
border-radius: 100vh !important;
}
h3 {
text-align: center;
}
"""

device = "cuda" # for GPU usage or "cpu" for CPU usage

quantization_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_compute_dtype=torch.bfloat16,
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type= "nf4")

tokenizer = AutoTokenizer.from_pretrained(MODEL)
model = AutoModelForCausalLM.from_pretrained(
MODEL,
torch_dtype=torch.bfloat16,
device_map="auto",
quantization_config=quantization_config)

@spaces.GPU()
def stream_chat(
message: str,
history: list,
system_prompt: str,
temperature: float = 0.8,
max_new_tokens: int = 1024,
top_p: float = 1.0,
top_k: int = 20,
penalty: float = 1.2,
):
print(f'message: {message}')
print(f'history: {history}')


conversation = [
    {"role": "system", "content": system_prompt}
]
for prompt, answer in history:
    conversation.extend([
        {"role": "user", "content": prompt}, 
        {"role": "assistant", "content": answer},
    ])

conversation.append({"role": "user", "content": message})

input_ids = tokenizer.apply_chat_template(conversation, add_generation_prompt=True, return_tensors="pt").to(model.device)

streamer = TextIteratorStreamer(tokenizer, timeout=60.0, skip_prompt=True, skip_special_tokens=True)

generate_kwargs = dict(
    input_ids=input_ids, 
    max_new_tokens = max_new_tokens,
    do_sample = False if temperature == 0 else True,
    top_p = top_p,
    top_k = top_k,
    temperature = temperature,
    eos_token_id=[128001,128008,128009],
    streamer=streamer,
)

with torch.no_grad():
    thread = Thread(target=model.generate, kwargs=generate_kwargs)
    thread.start()
    
buffer = ""
for new_text in streamer:
    buffer += new_text
    yield buffer

        
chatbot = gr.Chatbot(height=600, placeholder=PLACEHOLDER)

with gr.Blocks(css=CSS, theme="Nymbo/Nymbo_Theme") as demo:
gr.HTML(TITLE)
gr.ChatInterface(
fn=stream_chat,
chatbot=chatbot,
fill_height=True,
additional_inputs_accordion=gr.Accordion(label="⚙️ Parameters", open=False, render=False),
additional_inputs=[
gr.Textbox(
value="You are a helpful assistant",
label="System Prompt",
render=False,
),
gr.Slider(
minimum=0,
maximum=1,
step=0.1,
value=0.8,
label="Temperature",
render=False,
),
gr.Slider(
minimum=128,
maximum=8192,
step=1,
value=1024,
label="Max new tokens",
render=False,
),
gr.Slider(
minimum=0.0,
maximum=1.0,
step=0.1,
value=1.0,
label="top_p",
render=False,
),
gr.Slider(
minimum=1,
maximum=20,
step=1,
value=20,
label="top_k",
render=False,
),
gr.Slider(
minimum=0.0,
maximum=2.0,
step=0.1,
value=1.2,
label="Repetition penalty",
render=False,
),
],
examples=[
["Help me study vocabulary: write a sentence for me to fill in the blank, and I'll try to pick the correct option."],
["What are 5 creative things I could do with my kids' art? I don't want to throw them away, but it's also so much clutter."],
["Tell me a random fun fact about the Roman Empire."],
["Show me a code snippet of a website's sticky header in CSS and JavaScript."],
],
cache_examples=False,
)

if name == "main":
demo.launch()
"
Is the code deploying online or locally?