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| import torch | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| def extract_responses(text): | |
| """ | |
| Extracts and returns the responses from the text, excluding the parts | |
| between and including the [INST] tags. | |
| Args: | |
| text (str): The input text containing responses and [INST] tags. | |
| Returns: | |
| str: The extracted responses. | |
| """ | |
| import re | |
| # Split the text by [INST] tags and accumulate non-tag parts | |
| parts = re.split(r'\[INST\].*?\[/INST\]', text, flags=re.DOTALL) | |
| cleaned_text = "".join(parts) | |
| # Return the cleaned and trimmed text | |
| return cleaned_text.strip() | |
| def generate_html(): | |
| return( | |
| ''' | |
| <!DOCTYPE html> | |
| <html lang="en"> | |
| <head> | |
| <meta charset="UTF-8"> | |
| <meta name="viewport" content="width=device-width, initial-scale=1.0"> | |
| <title>Your Gradio App</title> | |
| <style> | |
| @import url('https://fonts.googleapis.com/css2?family=Montserrat:wght@300;400&display=swap'); | |
| body, html { | |
| margin: 0; | |
| padding: 0; | |
| font-family: 'Montserrat', sans-serif; | |
| background: #f9f9f9; | |
| } | |
| header { | |
| background-color: #e8f0fe; | |
| color: #333; | |
| text-align: center; | |
| padding: 40px 20px; | |
| border-radius: 0 0 25px 25px; | |
| background-image: linear-gradient(to right, #a7c7e7, #c0d8f0); | |
| box-shadow: 0 8px 16px 0 rgba(0,0,0,0.2); | |
| position: relative; | |
| overflow: hidden; | |
| } | |
| .background-shapes { | |
| position: absolute; | |
| top: 0; | |
| left: 0; | |
| right: 0; | |
| bottom: 0; | |
| background-image: linear-gradient(120deg, #a7c7e7 0%, #c0d8f0 100%); | |
| opacity: 0.6; | |
| animation: pulse 5s ease-in-out infinite alternate; | |
| } | |
| .header-content h1 { | |
| font-size: 2.8em; | |
| margin: 0; | |
| } | |
| .header-content p { | |
| font-size: 1.3em; | |
| margin-top: 20px; | |
| } | |
| @keyframes pulse { | |
| from { background-size: 100% 100%; } | |
| to { background-size: 110% 110%; } | |
| } | |
| </style> | |
| </head> | |
| <body> | |
| <header> | |
| <div class="background-shapes"></div> | |
| <div class="header-content"> | |
| <h1>AI Assistant</h1> | |
| <p>This interactive app leverages the power of a fine-tuned Phi 2 AI model to provide insightful responses. Type your query below and witness AI in action.</p> | |
| </div> | |
| </header> | |
| <!-- Rest of your Gradio app goes here --> | |
| </body> | |
| </html> | |
| ''') | |
| def generate_footer(): | |
| return( | |
| ''' | |
| <!DOCTYPE html> | |
| <html lang="en"> | |
| <head> | |
| <meta charset="UTF-8"> | |
| <meta name="viewport" content="width=device-width, initial-scale=1.0"> | |
| <title>Your Gradio App</title> | |
| <style> | |
| @import url('https://fonts.googleapis.com/css2?family=Roboto+Slab:wght@400;700&display=swap'); | |
| body, html { | |
| margin: 0; | |
| padding: 0; | |
| font-family: 'Roboto Slab', serif; | |
| background: #f9f9f9; | |
| } | |
| header, footer { | |
| color: #333; | |
| text-align: center; | |
| padding: 40px 20px; | |
| border-radius: 25px; | |
| background: linear-gradient(120deg, #a7c7e7 0%, #c0d8f0 100%); | |
| background-size: 200% 200%; | |
| animation: gradientShift 8s ease-in-out infinite; | |
| position: relative; | |
| overflow: hidden; | |
| } | |
| .header-content, .footer-content { | |
| position: relative; | |
| z-index: 1; | |
| } | |
| .header-content h1, .footer-content p { | |
| font-size: 2.8em; | |
| margin: 0; | |
| } | |
| .header-content p, .footer-content p { | |
| font-size: 1.3em; | |
| margin-top: 20px; | |
| } | |
| @keyframes gradientShift { | |
| 0% { background-position: 0% 50%; } | |
| 50% { background-position: 100% 50%; } | |
| 100% { background-position: 0% 50%; } | |
| } | |
| footer { | |
| margin-top: 40px; | |
| border-radius: 25px 25px 0 0; | |
| } | |
| </style> | |
| </head> | |
| <body> | |
| <footer> | |
| <div class="footer-content"> | |
| <p>This model was fine-tuned on a subset of the OpenAssistant dataset.</p> | |
| </div> | |
| </footer> | |
| </body> | |
| </html> | |
| ''') | |
| model = AutoModelForCausalLM.from_pretrained( | |
| "microsoft/phi-2", | |
| torch_dtype=torch.float32, | |
| device_map="cpu", | |
| trust_remote_code=True | |
| ) | |
| model.load_adapter('checkpoint-780') | |
| tokenizer = AutoTokenizer.from_pretrained('checkpoint-780', trust_remote_code=True) | |
| tokenizer.pad_token = tokenizer.eos_token |