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764f34e
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Parent(s):
ba913e7
mostly working
Browse files
app.py
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import gradio as gr
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examples = [["Adventurer is approached by a mysterious stranger in the tavern for a new quest."]]
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import gradio as gr
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import torch
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model = "mistralai/Mistral-7B-Instruct-v0.1"
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model = "TinyLlama/TinyLlama-1.1B-Chat-v0.3"
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# Gradio
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title = "Shisa 7B"
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description = "Test out Shisa 7B in either English or Japanese."
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placeholder = "Type Here / γγγ«ε
₯εγγ¦γγ γγ"
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examples = [
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"Hello, how are you?",
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"γγγ«γ‘γ―γε
ζ°γ§γγοΌ",
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"γγ£γγε
ζ°οΌ",
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"γγγ«γ‘γ―γγγγγιγγγ§γγοΌ",
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]
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tokenizer = AutoTokenizer.from_pretrained(model)
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model = AutoModelForCausalLM.from_pretrained(model)
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def chat(input, history=[]):
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input_ids = tokenizer.encode(input + tokenizer.eos_token, return_tensors="pt")
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history = model.generate(
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input_ids, max_length=4000, pad_token_id=tokenizer.eos_token_id
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).tolist()
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# convert the tokens to text, and then split the responses into lines
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response = tokenizer.decode(history[0]).split("<|endoftext|>")
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'''
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# tokenize the new input sentence
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new_user_input_ids = tokenizer.encode(
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input + tokenizer.eos_token, return_tensors="pt"
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)
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# append the new user input tokens to the chat history
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bot_input_ids = torch.cat([torch.LongTensor(history), new_user_input_ids], dim=-1)
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# generate a response
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history = model.generate(
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bot_input_ids, max_length=4000, pad_token_id=tokenizer.eos_token_id
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).tolist()
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# convert the tokens to text, and then split the responses into lines
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response = tokenizer.decode(history[0]).split("<|endoftext|>")
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# print('decoded_response-->>'+str(response))
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response = [
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(response[i], response[i + 1]) for i in range(0, len(response) - 1, 2)
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] # convert to tuples of list
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# print('response-->>'+str(response))
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'''
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return response, history
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gr.ChatInterface(
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chat,
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chatbot=gr.Chatbot(height=400),
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textbox=gr.Textbox(placeholder=placeholder, container=False, scale=7),
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title=title,
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description=description,
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theme="soft",
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examples=examples,
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cache_examples=False,
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undo_btn="Delete Previous",
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clear_btn="Clear",
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).queue().launch()
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