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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/Meta-Llama-3.1-8B-Instruct"]
HF_TOKEN = os.environ.get("HF_TOKEN", None)
TITLE = "<h1><center>Meta-Llama3.1-8B</center></h1>"
PLACEHOLDER = """
<center>
<p>Hi! How can I help you today?</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("meta-llama/Meta-Llama-3.1-8B-Instruct")
model = AutoModelForCausalLM.from_pretrained(
"meta-llama/Meta-Llama-3.1-8B-Instruct",
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,
repetition_penalty=penalty,
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="soft") as demo:
gr.HTML(TITLE)
gr.ChatInterface(
fn=stream_chat,
chatbot=chatbot,
fill_height=True,
cache_examples=False,
)
if _name_ == "__main__":
demo.launch()
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