Update app.py
Browse files
app.py
CHANGED
@@ -5,7 +5,7 @@ from threading import Thread
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tokenizer = AutoTokenizer.from_pretrained("togethercomputer/RedPajama-INCITE-Chat-3B-v1")
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model = AutoModelForCausalLM.from_pretrained("togethercomputer/RedPajama-INCITE-Chat-3B-v1", torch_dtype=torch.float16)
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model = model.to('cuda:0')
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class StopOnTokens(StoppingCriteria):
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def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
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@@ -23,7 +23,7 @@ def predict(message, history):
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messages = "".join(["".join(["\n<human>:"+item[0], "\n<bot>:"+item[1]]) #curr_system_message +
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for item in history_transformer_format])
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model_inputs = tokenizer([messages], return_tensors="pt")
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streamer = TextIteratorStreamer(tokenizer, timeout=10., skip_prompt=True, skip_special_tokens=True)
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generate_kwargs = dict(
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model_inputs,
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tokenizer = AutoTokenizer.from_pretrained("togethercomputer/RedPajama-INCITE-Chat-3B-v1")
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model = AutoModelForCausalLM.from_pretrained("togethercomputer/RedPajama-INCITE-Chat-3B-v1", torch_dtype=torch.float16)
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#model = model.to('cuda:0')
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class StopOnTokens(StoppingCriteria):
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def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
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messages = "".join(["".join(["\n<human>:"+item[0], "\n<bot>:"+item[1]]) #curr_system_message +
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for item in history_transformer_format])
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model_inputs = tokenizer([messages], return_tensors="pt")
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streamer = TextIteratorStreamer(tokenizer, timeout=10., skip_prompt=True, skip_special_tokens=True)
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generate_kwargs = dict(
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model_inputs,
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