Spaces:
Running
on
Zero
Running
on
Zero
File size: 2,814 Bytes
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import gradio as gr
import spaces
from peft import AutoPeftModelForCausalLM
from transformers import AutoTokenizer, TextIteratorStreamer
import torch
from threading import Thread
from typing import Generator
# Load the model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("./lora_model")
model = AutoPeftModelForCausalLM.from_pretrained("./lora_model", device_map=0, torch_dtype="auto")
@spaces.GPU()
@torch.no_grad()
def respond(
message,
history: list[tuple[str, str]],
system_message,
max_tokens,
temperature,
top_p,
) -> Generator[str, None, None]:
torch.cuda.empty_cache()
messages = [{"role": "system", "content": system_message}]
for val in history:
if val[0]:
messages.append({"role": "user", "content": val[0]})
if val[1]:
messages.append({"role": "assistant", "content": val[1]})
messages.append({"role": "user", "content": message})
convo_string = tokenizer.apply_chat_template(messages, tokenize = False, add_generation_prompt = True)
assert isinstance(convo_string, str)
# Tokenize the conversation
convo_tokens = tokenizer.encode(convo_string, add_special_tokens=False, truncation=False)
input_ids = torch.tensor(convo_tokens, dtype=torch.long)
attention_mask = torch.ones_like(input_ids)
# Move to GPU
input_ids = input_ids.unsqueeze(0).to("cuda")
attention_mask = attention_mask.unsqueeze(0).to("cuda")
streamer = TextIteratorStreamer(tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True)
generate_kwargs = dict(
input_ids=input_ids,
attention_mask=attention_mask,
max_new_tokens=max_tokens,
do_sample=True,
suppress_tokens=None,
use_cache=True,
temperature=temperature,
top_k=None,
top_p=top_p,
streamer=streamer,
)
if temperature == 0:
generate_kwargs["do_sample"] = False
t = Thread(target=model.generate, kwargs=generate_kwargs)
t.start()
outputs = []
for text in streamer:
outputs.append(text)
yield "".join(outputs)
demo = gr.ChatInterface(
respond,
additional_inputs=[
gr.Textbox(value="You are a helpful image generation prompt writing AI. You write image generation prompts based on user requests. The prompt you write should be 150 words or longer.", label="System message"),
gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
gr.Slider(minimum=0.1, maximum=4.0, value=0.6, step=0.1, label="Temperature"),
gr.Slider(
minimum=0.1,
maximum=1.0,
value=0.9,
step=0.05,
label="Top-p (nucleus sampling)",
),
],
)
if __name__ == "__main__":
demo.launch()
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