TinyStoriesAdv / app.py
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import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer, StoppingCriteria, StoppingCriteriaList, TextIteratorStreamer
import torch
from threading import Thread
model_name = "fzmnm/TinyStoriesAdv_v2_92M"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
model.eval()
model.generation_config.pad_token_id = tokenizer.eos_token_id
max_tokens = 512
def build_input_str(message: str, history: 'list[list[str]]'):
history_str = ""
for entity in history:
if entity['role'] == 'user':
history_str += f"问:{entity['content']}\n\n"
elif entity['role'] == 'assistant':
history_str += f"答:{entity['content']}\n\n"
return history_str + f"问:{message}\n\n"
def stop_criteria(input_str):
return input_str.endswith("\n") and len(input_str.strip()) > 0
class StopOnTokens(StoppingCriteria):
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
input_str = tokenizer.decode(input_ids[0], skip_special_tokens=True)
return stop_criteria(input_str)
def chat(message, history):
input_str = build_input_str(message, history)
input_ids = tokenizer.encode(input_str, return_tensors="pt")
input_ids = input_ids[:, -max_tokens:]
streamer = TextIteratorStreamer(
tokenizer,
timeout=10,
skip_prompt=True,
skip_special_tokens=True)
stopping_criteria = StoppingCriteriaList([StopOnTokens()])
generate_kwargs = dict(
input_ids=input_ids,
streamer=streamer,
stopping_criteria=stopping_criteria,
max_new_tokens=512,
top_p=0.9,
do_sample=True,
temperature=0.7
)
t = Thread(target=model.generate, kwargs=generate_kwargs)
t.start()
output_str = ""
for new_str in streamer:
output_str += new_str
yield output_str
app = gr.ChatInterface(
fn=chat,
type='messages',
examples=['什么是鹦鹉?', '什么是大象?', '谁是李白?', '什么是黑洞?'],
title='聊天机器人',
)
app.launch()