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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 = ["mistralai/mathstral-7B-v0.1"]
HF_TOKEN = os.environ.get("HF_TOKEN", None)
MODEL = os.environ.get("MODEL_ID")
TITLE = "<h1><center>mistralai/mathstral-7B-v0.1 - Your Math advisor</center></h1>"
PLACEHOLDER = """
<center>
<p>Hi! I'm MisMath. A Math advisor. My model is based on mistralai/Mistral-Nemo-Instruct-2407. Feel free to ask your questions</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)
footer = """
<div style="text-align: center; margin-top: 20px;">
<a href="https://www.linkedin.com/in/pejman-ebrahimi-4a60151a7/" target="_blank">LinkedIn</a> |
<a href="https://github.com/arad1367" target="_blank">GitHub</a> |
<a href="https://arad1367.pythonanywhere.com/" target="_blank">Live demo of my PhD defense</a>
<br>
Made with 💖 by Pejman Ebrahimi
</div>
"""
with gr.Blocks(css=CSS, theme="Ajaxon6255/Emerald_Isle") 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 for Math questions and complex calculations and programming and your name is MisMath",
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=[
["How to make a self-driving car?"],
["Give me creative idea to establish a startup"],
["How can I improve my programming skills?"],
["Show me a code snippet of a website's sticky header in CSS and JavaScript."],
],
cache_examples=False,
)
gr.HTML(footer)
if __name__ == "__main__":
demo.launch() |