Spaces:
Running
on
Zero
Running
on
Zero
File size: 4,432 Bytes
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import os
import time
import spaces
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
import gradio as gr
from threading import Thread
MODEL_LIST = ["mistralai/Mistral-Nemo-Instruct-2407"]
HF_TOKEN = os.environ.get("HF_TOKEN", None)
MODEL = os.environ.get("MODEL_ID")
TITLE = "<h1><center>Mistral-Nemo</center></h1>"
PLACEHOLDER = """
<center>
<p>The Mistral-Nemo is a pretrained generative text model of 12B parameters trained jointly by Mistral AI and NVIDIA.</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
tokenizer = AutoTokenizer.from_pretrained(MODEL)
model = AutoModelForCausalLM.from_pretrained(
MODEL,
torch_dtype=torch.bfloat16,
device_map="auto",
ignore_mismatched_sizes=True)
@spaces.GPU()
def stream_chat(
message: str,
history: list,
temperature: float = 0.3,
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 = []
for prompt, answer in history:
conversation.extend([
{"role": "user", "content": prompt},
{"role": "assistant", "content": answer},
])
conversation.append({"role": "user", "content": message})
input_text=tokenizer.apply_chat_template(conversation, tokenize=False)
inputs = tokenizer.encode(input_text, return_tensors="pt").to(device)
streamer = TextIteratorStreamer(tokenizer, timeout=60.0, skip_prompt=True, skip_special_tokens=True)
generate_kwargs = dict(
input_ids=inputs,
max_new_tokens = max_new_tokens,
do_sample = False if temperature == 0 else True,
top_p = top_p,
top_k = top_k,
temperature = temperature,
streamer=streamer,
repetition_penalty=penalty,
pad_token_id = 10,
)
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)
with gr.Blocks(css=CSS, theme="Nymbo/Nymbo_Theme") 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.Slider(
minimum=0,
maximum=1,
step=0.1,
value=0.3,
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=[
["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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