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import gradio as gr
import torch
from torch import LongTensor, FloatTensor
from transformers import AutoModelForCausalLM, AutoTokenizer, StoppingCriteria, StoppingCriteriaList, TextIteratorStreamer
from threading import Thread
tokenizer = AutoTokenizer.from_pretrained("togethercomputer/RedPajama-INCITE-Chat-3B-v1")
model = AutoModelForCausalLM.from_pretrained("togethercomputer/RedPajama-INCITE-Chat-3B-v1", torch_dtype=torch.bfloat16)
class StopOnTokens(StoppingCriteria):
def __call__(self, input_ids: LongTensor, scores: 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):
try:
history_transformer_format = history+[[message, ""]]
stop=StopOnTokens()
messages="".join(["".join(["\n<human>:"+item[0], "\n<bot>:"+item[1]]) for item in history_transformer_format])
model_inputs =tokenizer([messages], return_tensors="pt")
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=1000,
temperature=1.0,
num_beams=1,
stopping_criteria=StoppingCriteriaList([stop])
)
t=Thread(target=model.generate, kwargs=generate_kwargs)
t.start()
partical_message=""
for new_token in streamer:
if new_token !='<':
partical_message+=new_token
yield partical_message
except Exception as e:
yield "Sorry, I don't understand that."
gr.ChatInterface(predict).queue().launch() |