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import gradio as gr
#import torch
#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.float16)
#model = model.to('cuda:0')
#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, ""]]
messages = "".join(["".join(["\n<human>:"+item[0], "\n<bot>:"+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=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()
#partial_message = ""
#for new_token in streamer:
# if new_token != '<':
# partial_message += new_token
# yield partial_message
return "Hello"
gr.ChatInterface(predict).launch() |