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import gradio as gr
from transformers import TextIteratorStreamer
from threading import Thread
from transformers import StoppingCriteria, StoppingCriteriaList
import torch
import os
model_name = "microsoft/Phi-3-medium-128k-instruct"
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained(model_name, device_map='cuda', torch_dtype=torch.float16, trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained(model_name)
class StopOnTokens(StoppingCriteria):
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
stop_ids = [29, 0]
for stop_id in stop_ids:
if input_ids[0][-1] == stop_id:
return True
return False
def predict(message, history):
history_transformer_format = history + [[message, ""]]
stop = StopOnTokens()
messages = "".join(["".join(["\n<|end|>\n<|user|>\n"+item[0], "\n<|end|>\n<|assistant|>\n"+item[1]]) for item in history_transformer_format])
#messages = "".join(["".join(["<user>"+item[0], "<output>"+item[1]]) for item in history_transformer_format])
model_inputs = tokenizer([messages], 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=8192,
do_sample=True,
top_p=0.8,
top_k=40,
temperature=0.9,
stopping_criteria=StoppingCriteriaList([stop])
)
t = Thread(target=model.generate, kwargs=generate_kwargs)
t.start()
partial_message = ""
for new_token in streamer:
if new_token != '<':
partial_message += new_token
yield partial_message
demo = gr.ChatInterface(fn=predict, examples=["Write me a python snake game code", "Write me a ping pong game code"], title="Phi-3-medium-128k-instruct")
demo.launch(share=True) |