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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()
@spaces.GPU()
def stream_chat(message: str, history: list, temperature: float, max_new_tokens: int, top_p: float, top_k: int, penalty: float):
print(history)
resp, hist = 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,
)
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()
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