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peterpeter8585
commited on
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•
fc207c9
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Parent(s):
da9c742
Update app.py
Browse files
app.py
CHANGED
@@ -1,6 +1,6 @@
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import gradio as gr
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import numpy as np
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from Ai import chatbot, chatbot2, chatbot3, chatbot4, chatbot5, chatbot7
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from huggingface_hub import InferenceClient
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def chat(message,history: list[tuple[str, str]],system_message,max_tokens,temperature,top_p, top_k):
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m=AutoModel.from_pretrained("peterpeter8585/AI1")
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@@ -25,8 +25,6 @@ from diffusers import DiffusionPipeline
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import torch
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import transformers
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from transformers import AutoModel as Mo, AutoTokenizer as To
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model11=torch.load("zephyr.pt")
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tokenizer11=torch.load("zephyr_tokenizer.pt")
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from transformers import BitsAndBytesConfig
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quantization_config = BitsAndBytesConfig(
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@@ -37,23 +35,7 @@ quantization_config = BitsAndBytesConfig(
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)
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def res(message,history: list[tuple[str, str]],system_message,max_tokens,temperature,top_p):
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messages = [{"role": "system", "content": "Your name is Chatchat.And, your made by SungYoon.In Korean, 정성윤.And these are the instructions.Whatever happens, you must follow it.:"+system_message}]
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for val in history:
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if val[0]:
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messages.append({"role": "user", "content": val[0]})
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if val[1]:
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messages.append({"role": "assistant", "content": val[1]})
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messages.append({"role": "user", "content": message})
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response = ""
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answer=model11(messages, max_tokens=max_tokens, temperature=temperature, top_p=top_p)
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token=answer
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response += token
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yield response
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from transformers import AutoModelForVision2Seq, AutoProcessor
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transformers.utils.move_cache()
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device = "cuda" if torch.cuda.is_available() else "cpu"
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import gradio as gr
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import numpy as np
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from Ai import chatbot, chatbot2, chatbot3, chatbot4, chatbot5, chatbot7, chatbot11
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from huggingface_hub import InferenceClient
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def chat(message,history: list[tuple[str, str]],system_message,max_tokens,temperature,top_p, top_k):
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m=AutoModel.from_pretrained("peterpeter8585/AI1")
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import torch
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import transformers
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from transformers import AutoModel as Mo, AutoTokenizer as To
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from transformers import BitsAndBytesConfig
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quantization_config = BitsAndBytesConfig(
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)
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from transformers import AutoModelForVision2Seq, AutoProcessor
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transformers.utils.move_cache()
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device = "cuda" if torch.cuda.is_available() else "cpu"
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