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Update app.py
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app.py
CHANGED
@@ -18,27 +18,16 @@ class OrcaChatBot:
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def __init__(self, model, tokenizer, system_message="You are Orca, an AI language model created by Microsoft. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior."):
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self.model = model
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self.tokenizer = tokenizer
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self.
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self.conversation_history = []
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def
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def format_prompt(self):
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prompt = f"<|im_start|>assistant\n{self.system_message}<|im_end|>\n"
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for role, message in self.conversation_history:
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if message.strip():
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prompt += f"<|im_start|>{role}\n{message}<|im_end|>\n"
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# if role == "assistant":
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# prompt += f"<|im_end|>\n"
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prompt += "<|im_start|> assistant\n"
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return prompt
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def predict(self, user_message, temperature=0.4, max_new_tokens=70, top_p=0.99, repetition_penalty=1.9):
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self.
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prompt = self.format_prompt()
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inputs = self.tokenizer(prompt, return_tensors='pt', add_special_tokens=False)
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input_ids = inputs["input_ids"].to(self.model.device)
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@@ -48,19 +37,17 @@ class OrcaChatBot:
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temperature=temperature,
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top_p=top_p,
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repetition_penalty=repetition_penalty,
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# pad_token_id=self.tokenizer.eos_token_id,
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do_sample=True
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response = self.tokenizer.decode(output_ids[0], skip_special_tokens=True)
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self.update_conversation_history("", response)
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return response
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Orca_bot = OrcaChatBot(model, tokenizer)
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def gradio_predict(user_message, system_message, max_new_tokens, temperature, top_p, repetition_penalty):
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return
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iface = gr.Interface(
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fn=gradio_predict,
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def __init__(self, model, tokenizer, system_message="You are Orca, an AI language model created by Microsoft. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior."):
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self.model = model
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self.tokenizer = tokenizer
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self.default_system_message = system_message
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def format_prompt(self, user_message, system_message):
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if system_message is None:
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system_message = self.default_system_message
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prompt = f"<|im_start|>assistant\n{self.system_message}<|im_end|>\n<|im_start|>\nuser\n{user_message}<|im_end|>\nassistant\n"
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return prompt
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def predict(self, user_message, system_message=None, temperature=0.4, max_new_tokens=70, top_p=0.99, repetition_penalty=1.9):
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prompt = self.format_prompt(user_message, system_message)
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inputs = self.tokenizer(prompt, return_tensors='pt', add_special_tokens=False)
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input_ids = inputs["input_ids"].to(self.model.device)
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temperature=temperature,
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top_p=top_p,
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repetition_penalty=repetition_penalty,
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do_sample=True
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)
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response = self.tokenizer.decode(output_ids[0], skip_special_tokens=True)
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return response
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def gradio_predict(user_message, system_message, max_new_tokens, temperature, top_p, repetition_penalty):
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response = Orca_bot.predict(user_message, system_message, temperature, max_new_tokens, top_p, repetition_penalty)
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return response
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Orca_bot = OrcaChatBot(model, tokenizer)
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iface = gr.Interface(
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fn=gradio_predict,
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