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Update app.py
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app.py
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import streamlit as st
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import time
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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def define_model():
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model = AutoModelForCausalLM.from_pretrained("facebook/opt-1.3b", torch_dtype=torch.float16).cuda()
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tokenizer = AutoTokenizer.from_pretrained("facebook/opt-1.3b", use_fast=False)
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return model, tokenizer
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input_ids = tokenizer(prompt, return_tensors="pt").input_ids.cuda()
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generated_ids = model.generate(input_ids, num_return_sequences=num_sequences, max_length=max_length)
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answer = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
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return answer
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prompt= st.text_area('Your prompt here',
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'''Hello, I'm am conscious and''')
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answer = opt_model(prompt, model, tokenizer,)
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#lst = ['ciao come stai sjfsbd dfhsdf fuahfuf feuhfu wefwu ']
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lst = ' '.join(answer)
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import streamlit as st
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import time
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#from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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#@st.cache(allow_output_mutation=True)
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#def define_model():
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# model = AutoModelForCausalLM.from_pretrained("facebook/opt-1.3b", torch_dtype=torch.float16).cuda()
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# tokenizer = AutoTokenizer.from_pretrained("facebook/opt-1.3b", use_fast=False)
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# return model, tokenizer
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from transformers import pipeline
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generator = pipeline('text-generation', model="facebook/opt-1.3b")
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answer = generator("Hello, I'm am conscious and")
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#@st.cache(allow_output_mutation=True)
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#def opt_model(prompt, model, tokenizer, num_sequences = 1, max_length = 50):
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# input_ids = tokenizer(prompt, return_tensors="pt").input_ids.cuda()
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# generated_ids = model.generate(input_ids, num_return_sequences=num_sequences, max_length=max_length)
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# answer = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
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# return answer
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#model, tokenizer = define_model()
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prompt= st.text_area('Your prompt here',
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'''Hello, I'm am conscious and''')
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#answer = opt_model(prompt, model, tokenizer,)
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#lst = ['ciao come stai sjfsbd dfhsdf fuahfuf feuhfu wefwu ']
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lst = ' '.join(answer)
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