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import os | |
import time | |
import spaces | |
import torch | |
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer, AutoConfig | |
import gradio as gr | |
from threading import Thread | |
MODEL = "jwang2373/UW-SBEL-ChronoPhi-4b-it" | |
TITLE = "<h1><center>UW-SBEL-ChronoPhi-4b</center></h1>" | |
PLACEHOLDER = """ | |
<center> | |
<p>Hi! I'm a PyChrono Digital Twin expert. How can I assist 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" if torch.cuda.is_available() else "cpu" | |
# Load the fine-tuned model configuration | |
config = AutoConfig.from_pretrained("jwang2373/UW-SBEL-ChronoPhi-4b-it") | |
base_config = AutoConfig.from_pretrained("microsoft/Phi-3-mini-128k-instruct") | |
fine_tuned_config = AutoConfig.from_pretrained("jwang2373/UW-SBEL-ChronoPhi-4b-it") | |
print(base_config) | |
print(fine_tuned_config) | |
tokenizer = AutoTokenizer.from_pretrained(MODEL, trust_remote_code=True) | |
model = AutoModelForCausalLM.from_pretrained(MODEL, torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="auto",config=config) | |
model = model.eval() | |
def stream_chat( | |
message: str, | |
history: list, | |
system_prompt: str, | |
temperature: float = 0.1, | |
max_new_tokens: int = 32768, | |
top_p: float = 1.0, | |
top_k: int = 50, | |
): | |
print(f'message: {message}') | |
print(f'history: {history}') | |
full_prompt = f"<<SYS>>\n{system_prompt}\n<</SYS>>\n\n" | |
for prompt, answer in history: | |
full_prompt += f"[INST]{prompt}[/INST]{answer}" | |
full_prompt += f"[INST]{message}[/INST]" | |
inputs = tokenizer(full_prompt, truncation=False, return_tensors="pt").to(device) | |
context_length = inputs.input_ids.shape[-1] | |
streamer = TextIteratorStreamer(tokenizer, timeout=60.0, skip_prompt=True, skip_special_tokens=True) | |
generate_kwargs = dict( | |
inputs=inputs.input_ids, | |
max_new_tokens=max_new_tokens, | |
do_sample=True, | |
top_p=top_p, | |
top_k=top_k, | |
temperature=temperature, | |
num_beams=1, | |
streamer=streamer, | |
) | |
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 PyChrono expert.", | |
label="System Prompt", | |
render=False, | |
), | |
gr.Slider( | |
minimum=0, | |
maximum=1, | |
step=0.1, | |
value=0.5, | |
label="Temperature", | |
render=False, | |
), | |
gr.Slider( | |
minimum=1024, | |
maximum=4096, | |
step=1024, | |
value=4096, | |
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=100, | |
step=1, | |
value=100, | |
label="Top k", | |
render=False, | |
), | |
], | |
examples=[ | |
["Run a PyChrono simulation of a sedan driving on a flat surface with a detailed vehicle dynamics model."], | |
["Run a real-time simulation of an HMMWV vehicle on a bumpy and textured road."], | |
["Set up a Curiosity rover driving simulation on flat, rigid ground in PyChrono."], | |
["Simulate a FEDA vehicle driving on rigid terrain in PyChrono."], | |
], | |
cache_examples=False, | |
) | |
if __name__ == "__main__": | |
demo.launch() | |