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Update app.py
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app.py
CHANGED
@@ -3,12 +3,21 @@ import gradio as gr
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
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from transformers import TextStreamer
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import spaces
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tokenizer = AutoTokenizer.from_pretrained("Rorical/0-roleplay", trust_remote_code=True)
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tokenizer.chat_template = "{% for message in messages %}{{'<|im_start|>' + ((message['role'] + ':\n') if message['role'] != '' else '') + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>ζι:\n' }}{% endif %}" # Be careful that this model used custom chat template.
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# Define the response function
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@spaces.GPU
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def respond(
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@@ -19,9 +28,6 @@ def respond(
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temperature,
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top_p,
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):
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# Load model and tokenizer
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model = AutoModelForCausalLM.from_pretrained("Rorical/0-roleplay", return_dict=True, trust_remote_code=True)
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# Construct the messages for the chat
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messages = [{"role": "", "content": system_message}]
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for user_message, bot_response in history:
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
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from transformers import TextStreamer
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import transformers
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import spaces
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# Load tokenizer
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tokenizer = AutoTokenizer.from_pretrained("Rorical/0-roleplay", trust_remote_code=True)
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tokenizer.chat_template = "{% for message in messages %}{{'<|im_start|>' + ((message['role'] + ':\n') if message['role'] != '' else '') + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>ζι:\n' }}{% endif %}" # Be careful that this model used custom chat template.
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# Load model
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@spaces.GPU
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def load_pretrained_model():
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return AutoModelForCausalLM.from_pretrained("Rorical/0-roleplay", return_dict=True, trust_remote_code=True)
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model = load_pretrained_model()
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# Define the response function
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@spaces.GPU
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def respond(
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temperature,
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top_p,
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):
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# Construct the messages for the chat
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messages = [{"role": "", "content": system_message}]
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for user_message, bot_response in history:
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