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102 lines
8.3 KiB
Python
102 lines
8.3 KiB
Python
# Approach 1
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from transformers import GPTNeoForCausalLM, GPT2Tokenizer
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model_name = 'EleutherAI/gpt-neo-1.3B'
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tokenizer = GPT2Tokenizer.from_pretrained(model_name)
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model = GPTNeoForCausalLM.from_pretrained(model_name)
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conversation = """
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Summarize the following conversation in 3 key sentences:
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We 're joined next by Thomas Curian , CEO of Google Cloud , and Alexander Wang , CEO and founder of Scale AI .
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Thomas joined Google in November 2018 as the CEO of Google Cloud . Prior to Google , Thomas spent 22 years at Oracle , where most recently he was president of product development .
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Before that , Thomas worked at McKinsey as a business analyst and engagement manager . His nearly 30 years of experience have given him a deep knowledge of engineering enterprise relationships and leadership of large organizations .
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Thomas 's degrees include an MBA in administration and management from Stanford University , as an RJ Miller scholar and a BSEE in electrical engineering and computer science from Princeton University , where he graduated suma cum laude .
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Thomas serves as a member of the Stanford graduate School of Business Advisory Council and Princeton University School of Engineering Advisory Council .
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Please welcome to the stage , Thomas Curian and Alexander Wang . This is a super exciting conversation . Thanks for being here , Thomas .
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"""
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input_ids = tokenizer.encode(conversation, return_tensors='pt')
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output = model.generate(input_ids,
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max_length=30,
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num_return_sequences=1)
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caption = tokenizer.decode(output[0], skip_special_tokens=True)
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print("Caption:", caption[len(input_ids):])
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# Approach 2
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import torch
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from transformers import GPT2LMHeadModel, GPT2Tokenizer
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model_name = "gpt2"
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tokenizer = GPT2Tokenizer.from_pretrained(model_name)
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model = GPT2LMHeadModel.from_pretrained(model_name)
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model.eval()
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text = """
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You all just came off of your incredible Google Cloud next conference where you released a wide variety of functionality and features and new products across artisan television and also across the entire sort of cloud ecosystem . You want to just first by walking through , first start by walking through all the innovations that you sort of released and what you 're excited about when you come to Google Cloud ? Now our vision is super simple . If you look at what smartphones did for a consumer , you know they took a computer and internet browser , a communication device , and a camera , and made it so that it 's in everybody 's pocket , so it really brought computation to every person . We feel that , you know , our , what we 're trying to do is take all the technological innovation that Google 's doing , but make it super simple so that everyone can consume it . And so that includes our global data center footprint , all the new types of hardware and large-scale systems we work on , the software that we 're making available for people to do high-scale computation , tools for data processing , tools for cybersecurity , processing , tools for cyber security , tools for machine learning , but make it so simple that everyone can use it . And every step that we do to simplify things for people , we think adoption can grow . And so that 's a lot of what we 've done these last three , four years , and we made a number of announcements that next in machine learning and AI in particular , you know , we look at our work as four elements , how we take our large-scale compute systems that were building for AI and how we make that available to everybody . Second , what we 're doing with the software stacks and top of it , things like jacks and other things and how we 're making those available to everybody . Third is advances because different people have different levels of expertise . Some people say I need the hardware to build my own large language model or algorithm . Other people say , look , I really need to use a building block . You guys give me . So , 30s we 've done a lot with AutoML and we announce new capability for image , video , and translation to make it available to everybody . And then lastly , we 're also building completely packaged solutions for some areas and we announce some new stuff . "
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"""
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tokenizer.pad_token = tokenizer.eos_token
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input_ids = tokenizer.encode(text,
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max_length=100,
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truncation=True,
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return_tensors="pt")
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attention_mask = torch.ones(input_ids.shape, dtype=torch.long)
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output = model.generate(input_ids,
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max_new_tokens=20,
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num_return_sequences=1,
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num_beams=2,
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attention_mask=attention_mask)
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chapter_titles = [tokenizer.decode(output[i], skip_special_tokens=True) for i in range(output.shape[0])]
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for i, title in enumerate(chapter_titles):
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print("Caption: ", title)
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# Approach 3
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import torch
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from transformers import GPT2LMHeadModel, GPT2Tokenizer
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def generate_response(conversation, max_length=100):
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input_text = ""
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for entry in conversation:
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role = entry["role"]
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content = entry["content"]
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input_text += f"{role}: {content}\n"
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# Tokenize the entire conversation
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input_ids = tokenizer.encode(input_text, return_tensors="pt")
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# Generate text based on the entire conversation
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with torch.no_grad():
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output = model.generate(input_ids, pad_token_id=tokenizer.eos_token_id)
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# Decode the generated text and return it
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response = tokenizer.decode(output[0], skip_special_tokens=True)
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return response
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if __name__ == "__main__":
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# Call appropriate approach from the main while experimenting
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model_name = "gpt2"
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model = GPT2LMHeadModel.from_pretrained(model_name)
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tokenizer = GPT2Tokenizer.from_pretrained(model_name)
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sample_chunks = [
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"You all just came off of your incredible Google Cloud next conference where you released a wide variety of functionality and features and new products across artisan television and also across the entire sort of cloud ecosystem . You want to just first by walking through , first start by walking through all the innovations that you sort of released and what you 're excited about when you come to Google Cloud ? Now our vision is super simple . If you look at what smartphones did for a consumer , you know they took a computer and internet browser , a communication device , and a camera , and made it so that it 's in everybody 's pocket , so it really brought computation to every person . We feel that , you know , our , what we 're trying to do is take all the technological innovation that Google 's doing , but make it super simple so that everyone can consume it . And so that includes our global data center footprint , all the new types of hardware and large-scale systems we work on , the software that we 're making available for people to do high-scale computation , tools for data processing , tools for cybersecurity , processing , tools for cyber security , tools for machine learning , but make it so simple that everyone can use it . And every step that we do to simplify things for people , we think adoption can grow . And so that 's a lot of what we 've done these last three , four years , and we made a number of announcements that next in machine learning and AI in particular , you know , we look at our work as four elements , how we take our large-scale compute systems that were building for AI and how we make that available to everybody . Second , what we 're doing with the software stacks and top of it , things like jacks and other things and how we 're making those available to everybody . Third is advances because different people have different levels of expertise . Some people say I need the hardware to build my own large language model or algorithm . Other people say , look , I really need to use a building block . You guys give me . So , 30s we 've done a lot with AutoML and we announce new capability for image , video , and translation to make it available to everybody . And then lastly , we 're also building completely packaged solutions for some areas and we announce some new stuff . "
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]
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conversation = [
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{"role": "system", "content": "Summarize this text"},
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{"role": "user", "content": " text : " + sample_chunks[0]},
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]
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response = generate_response(conversation)
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print("Response:", response)
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