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import os
import spaces
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
import gradio as gr
from threading import Thread
model_id = os.environ.get("MODEL_ID")
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.float16,
device_map="sequential",
offload_folder="offload", # Specify the offload folder
offload_state_dict=True # offload the state_dict as needed
)
TITLE = "<h1><center>Meta-Llama-3.1-70B-Instruct-AWQ-INT4</center></h1>"
PLACEHOLDER = """
<center>
<p>Hi! How can I help you today?</p>
</center>
"""
DESCRIPTION = """
<h3>MODEL: <a href="https://hf.co/hugging-quants/Meta-Llama-3.1-70B-Instruct-AWQ-INT4">Meta-Llama-3.1-70B-Instruct-AWQ-INT4</a></h3>
<center>
<p>This model is designed for conversational interactions.</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
@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)
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.Textbox(
value="You are a helpful assistant",
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=[
["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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