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import gradio as gr
import torch
import spaces
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers import StoppingCriteria, StoppingCriteriaList, TextIteratorStreamer
from threading import Thread
# Loading the tokenizer and model from Hugging Face's model hub.
if torch.cuda.is_available():
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen1.5-7B-Chat")
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen1.5-7B-Chat", torch_dtype=torch.float16, device_map="auto")
# Defining a custom stopping criteria class for the model's text generation.
class StopOnTokens(StoppingCriteria):
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
stop_ids = [2] # IDs of tokens where the generation should stop.
for stop_id in stop_ids:
if input_ids[0][-1] == stop_id: # Checking if the last generated token is a stop token.
return True
return False
# Function to generate model predictions.
@spaces.GPU(duration=180)
def predict(message, history):
stop = StopOnTokens()
conversation = []
for user, assistant in history:
conversation.extend([{"role": "user", "content": user}, {"role": "assistant", "content": assistant}])
conversation.append({"role": "user", "content": message})
prompt = tokenizer.apply_chat_template(conversation, tokenize=False, add_generation_prompt=True)
model_inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
streamer = TextIteratorStreamer(tokenizer, timeout=10., skip_prompt=True, skip_special_tokens=True)
generate_kwargs = dict(
model_inputs,
streamer=streamer,
max_new_tokens=1024,
do_sample=True,
top_p=0.95,
top_k=50,
temperature=0.7,
repetition_penalty=1.0,
num_beams=1,
stopping_criteria=StoppingCriteriaList([stop])
)
t = Thread(target=model.generate, kwargs=generate_kwargs)
t.start() # Starting the generation in a separate thread.
partial_message = ""
for new_token in streamer:
partial_message += new_token
if '</s>' in partial_message: # Breaking the loop if the stop token is generated.
break
yield partial_message
# Setting up the Gradio chat interface.
gr.ChatInterface(predict,
title="Qwen1.5 7B Chat Demo",
description="Warning. All answers are generated and may contain inaccurate information.",
examples=['How do you cook fish?', 'Who is the president of the United States?']
).launch() # Launching the web interface.
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