Spaces:
Running
on
Zero
Running
on
Zero
import os | |
import time | |
import spaces | |
import torch | |
from transformers import AutoModelForCausalLM, AutoTokenizer | |
import gradio as gr | |
MODEL_LIST = ["internlm/internlm2_5-7b-chat", "internlm/internlm2_5-7b-chat-1m"] | |
HF_TOKEN = os.environ.get("HF_TOKEN", None) | |
MODEL_ID = os.environ.get("MODEL_ID", None) | |
MODEL_NAME = MODEL_ID.split("/")[-1] | |
TITLE = "<h1><center>internlm2.5-7b-chat</center></h1>" | |
DESCRIPTION = f""" | |
<h3>MODEL: <a href="https://hf.co/{MODEL_ID}">{MODEL_NAME}</a></h3> | |
""" | |
PLACEHOLDER = """ | |
<center> | |
<p>Feel free to test models <b>without</b> any logs.</p> | |
</center> | |
""" | |
CSS = """ | |
.duplicate-button { | |
margin: auto !important; | |
color: white !important; | |
background: black !important; | |
border-radius: 100vh !important; | |
} | |
h3 { | |
text-align: center; | |
} | |
""" | |
model = AutoModelForCausalLM.from_pretrained( | |
MODEL_ID, | |
torch_dtype=torch.float16, | |
trust_remote_code=True).cuda() | |
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, trust_remote_code=True) | |
model = model.eval() | |
def stream_chat(message: str, history: list, temperature: float, max_new_tokens: int, top_p: float, top_k: int, penalty: float): | |
print(history) | |
for resp, history in model.stream_chat( | |
tokenizer, | |
query = message, | |
history = history, | |
max_new_tokens = max_new_tokens, | |
do_sample = True if temperature == 0 else False, | |
top_p = top_p, | |
top_k = top_k, | |
temperature = temperature, | |
): | |
print(resp) | |
yield resp | |
chatbot = gr.Chatbot(height=600, placeholder=PLACEHOLDER) | |
with gr.Blocks(css=CSS, theme="soft") as demo: | |
gr.HTML(TITLE) | |
gr.HTML(DESCRIPTION) | |
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.Slider( | |
minimum=0, | |
maximum=1, | |
step=0.1, | |
value=0.8, | |
label="Temperature", | |
render=False, | |
), | |
gr.Slider( | |
minimum=128, | |
maximum=2048, | |
step=1, | |
value=1024, | |
label="Max New Tokens", | |
render=False, | |
), | |
gr.Slider( | |
minimum=0.0, | |
maximum=1.0, | |
step=0.1, | |
value=0.8, | |
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=1.0, | |
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() | |