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import gradio as gr
import os
import spaces
from transformers import AutoTokenizer, AutoModelForCausalLM, TextIteratorStreamer
from threading import Thread
# Set an environment variable
HF_TOKEN = os.environ.get("HF_TOKEN", None)
DESCRIPTION = '''
<div>
<h1 style="text-align: center;">StevenChen16 Llama3 8B Lawyer</h1>
<p>This Space demonstrates the instruction-tuned model <a href="https://huggingface.co/StevenChen16/llama3-8b-Lawyer"><b>StevenChen16 Llama3 8B Lawyer</b></a>. This model is fine-tuned to provide legal advice based on US and Canada law.</p>
<p>Feel free to play with it, or duplicate to run privately!</p>
</div>
'''
LICENSE = """
<p/>
---
Built with StevenChen16 Llama3 8B Lawyer
"""
PLACEHOLDER = """
<div style="padding: 30px; text-align: center; display: flex; flex-direction: column; align-items: center;">
<h1 style="font-size: 28px; margin-bottom: 2px; opacity: 0.55;">StevenChen16 Llama3 8B Lawyer</h1>
<p style="font-size: 18px; margin-bottom: 2px; opacity: 0.65;">Ask me anything about US and Canada law...</p>
</div>
"""
css = """
h1 {
text-align: center;
display: block;
}
#duplicate-button {
margin: auto;
color: white;
background: #1565c0;
border-radius: 100vh;
}
"""
# Load the tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("StevenChen16/llama3-8b-Lawyer")
model = AutoModelForCausalLM.from_pretrained("StevenChen16/llama3-8b-Lawyer", device_map="auto")
terminators = [
tokenizer.eos_token_id,
tokenizer.convert_tokens_to_ids("")
]
@spaces.GPU(duration=120)
def chat_llama3_8b(message: str,
history: list,
temperature: float,
max_new_tokens: int
) -> str:
"""
Generate a streaming response using the llama3-8b model.
Args:
message (str): The input message.
history (list): The conversation history used by ChatInterface.
temperature (float): The temperature for generating the response.
max_new_tokens (int): The maximum number of new tokens to generate.
Returns:
str: The generated response.
"""
conversation = []
for user, assistant in history:
conversation.extend([{"role": "user", "content": user}, {"role": "assistant", "content": assistant}])
conversation.append({"role": "user", "content": message})
input_ids = tokenizer.apply_chat_template(conversation, return_tensors="pt").to(model.device)
streamer = TextIteratorStreamer(tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True)
generate_kwargs = dict(
input_ids= input_ids,
streamer=streamer,
max_new_tokens=max_new_tokens,
do_sample=True,
temperature=temperature,
eos_token_id=terminators,
)
# This will enforce greedy generation (do_sample=False) when the temperature is passed 0, avoiding the crash.
if temperature == 0:
generate_kwargs['do_sample'] = False
t = Thread(target=model.generate, kwargs=generate_kwargs)
t.start()
outputs = []
for text in streamer:
outputs.append(text)
yield "".join(outputs)
# Gradio block
chatbot = gr.Chatbot(height=450, placeholder=PLACEHOLDER, label='Gradio ChatInterface')
with gr.Blocks(fill_height=True, css=css) as demo:
gr.Markdown(DESCRIPTION)
gr.DuplicateButton(value="Duplicate Space for private use", elem_id="duplicate-button")
gr.ChatInterface(
fn=chat_llama3_8b,
chatbot=chatbot,
fill_height=True,
additional_inputs_accordion=gr.Accordion(label="⚙️ Parameters", open=False, render=False),
additional_inputs=[
gr.Slider(minimum=0,
maximum=1,
step=0.1,
value=0.95,
label="Temperature",
render=False),
gr.Slider(minimum=128,
maximum=4096,
step=1,
value=512,
label="Max new tokens",
render=False ),
],
examples=[
['How to setup a human base on Mars? Give short answer.'],
['Explain theory of relativity to me like I’m 8 years old.'],
['What is 9,000 * 9,000?'],
['Write a pun-filled happy birthday message to my friend Alex.'],
['Justify why a penguin might make a good king of the jungle.']
],
cache_examples=False,
)
gr.Markdown(LICENSE)
if __name__ == "__main__":
demo.launch()
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