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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) | |
MODEL = os.environ.get("MODEL_ID") | |
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_8bit=True) | |
tokenizer = AutoTokenizer.from_pretrained(MODEL) | |
model = AutoModelForCausalLM.from_pretrained( | |
MODEL, | |
torch_dtype=torch.bfloat16, | |
device_map="auto", | |
rope_scaling={"type": "llama3", "factor": 8.0}, | |
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="soft") as demo: | |
gr.HTML(TITLE) | |
gr.DuplicateButton(value="Duplicate Space for private use", elem_classes="duplicate-button") | |
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() |