I am trying to run the code that perform llama multi turn chat which posted on huggingface locally in my pc in vs code, the original huggingface code:
"
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 = "<h1><center>Llama 3.2 1B</center></h1>"

PLACEHOLDER = """
<center>
<p>Hi, I'm Llama. Ask me anything.</p>
</center>
"""


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()
"
My code in vs code:
"
import os
import time
#import spaces
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer, BitsAndBytesConfig
import gradio as gr
from threading import Thread

MODEL = r'C:\Users\shermanhonsf\Desktop\Python\llama_develop\model\Llama3.2-1B-Instruct-HF-Model_try'

TITLE = "<h1><center>Llama 3.2 1B</center></h1>"

PLACEHOLDER = """
<center>
<p>Hi, I'm Llama. Ask me anything.</p>
</center>
"""


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)

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(debug = True)
"
The huggingface code can be run on huggingface normally, but my code:
"
ValueError: `temperature` (=1024) has to be a strictly positive float, otherwise your next token scores will be invalid.
message: Help me study vocabulary: write a sentence for me to fill in the blank, and I'll try to pick the correct option.
history: []
Traceback (most recent call last):
  File "c:\Users\shermanhonsf\Desktop\Python\.conda\Lib\site-packages\gradio\queueing.py", line 624, in process_events
    response = await route_utils.call_process_api(
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "c:\Users\shermanhonsf\Desktop\Python\.conda\Lib\site-packages\gradio\route_utils.py", line 323, in call_process_api
    output = await app.get_blocks().process_api(
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "c:\Users\shermanhonsf\Desktop\Python\.conda\Lib\site-packages\gradio\blocks.py", line 2015, in process_api
    result = await self.call_function(
             ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "c:\Users\shermanhonsf\Desktop\Python\.conda\Lib\site-packages\gradio\blocks.py", line 1574, in call_function
    prediction = await utils.async_iteration(iterator)
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "c:\Users\shermanhonsf\Desktop\Python\.conda\Lib\site-packages\gradio\utils.py", line 710, in async_iteration
    return await anext(iterator)
           ^^^^^^^^^^^^^^^^^^^^^
  File "c:\Users\shermanhonsf\Desktop\Python\.conda\Lib\site-packages\gradio\utils.py", line 815, in asyncgen_wrapper
    response = await iterator.__anext__()
               ^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "c:\Users\shermanhonsf\Desktop\Python\.conda\Lib\site-packages\gradio\chat_interface.py", line 678, in _stream_fn
    first_response = await async_iteration(generator)
                     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "c:\Users\shermanhonsf\Desktop\Python\.conda\Lib\site-packages\gradio\utils.py", line 710, in async_iteration
    return await anext(iterator)
           ^^^^^^^^^^^^^^^^^^^^^
  File "c:\Users\shermanhonsf\Desktop\Python\.conda\Lib\site-packages\gradio\utils.py", line 704, in __anext__
    return await anyio.to_thread.run_sync(
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "c:\Users\shermanhonsf\Desktop\Python\.conda\Lib\site-packages\anyio\to_thread.py", line 56, in run_sync
    return await get_async_backend().run_sync_in_worker_thread(
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "c:\Users\shermanhonsf\Desktop\Python\.conda\Lib\site-packages\anyio\_backends\_asyncio.py", line 2441, in run_sync_in_worker_thread
    return await future
           ^^^^^^^^^^^^
  File "c:\Users\shermanhonsf\Desktop\Python\.conda\Lib\site-packages\anyio\_backends\_asyncio.py", line 943, in run
    result = context.run(func, *args)
             ^^^^^^^^^^^^^^^^^^^^^^^^
  File "c:\Users\shermanhonsf\Desktop\Python\.conda\Lib\site-packages\gradio\utils.py", line 687, in run_sync_iterator_async
    return next(iterator)
           ^^^^^^^^^^^^^^
  File "C:\Users\shermanhonsf\AppData\Local\Temp\ipykernel_23352\3853318046.py", line 91, in stream_chat
    for new_text in streamer:
  File "c:\Users\shermanhonsf\Desktop\Python\.conda\Lib\site-packages\transformers\generation\streamers.py", line 223, in __next__
    value = self.text_queue.get(timeout=self.timeout)
            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "c:\Users\shermanhonsf\Desktop\Python\.conda\Lib\queue.py", line 179, in get
    raise Empty
_queue.Empty
"
What happen?