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https://github.com/rasbt/LLMs-from-scratch.git
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79 lines
2.4 KiB
Python
79 lines
2.4 KiB
Python
# Copyright (c) Sebastian Raschka under Apache License 2.0 (see LICENSE.txt).
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# Source for "Build a Large Language Model From Scratch"
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# - https://www.manning.com/books/build-a-large-language-model-from-scratch
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# Code: https://github.com/rasbt/LLMs-from-scratch
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from pathlib import Path
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import sys
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import tiktoken
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import torch
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import chainlit
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from previous_chapters import (
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classify_review,
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GPTModel
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)
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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def get_model_and_tokenizer():
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"""
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Code to load finetuned GPT-2 model generated in chapter 6.
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This requires that you run the code in chapter 6 first, which generates the necessary model.pth file.
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"""
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GPT_CONFIG_124M = {
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"vocab_size": 50257, # Vocabulary size
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"context_length": 1024, # Context length
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"emb_dim": 768, # Embedding dimension
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"n_heads": 12, # Number of attention heads
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"n_layers": 12, # Number of layers
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"drop_rate": 0.1, # Dropout rate
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"qkv_bias": True # Query-key-value bias
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}
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tokenizer = tiktoken.get_encoding("gpt2")
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model_path = Path("..") / "01_main-chapter-code" / "review_classifier.pth"
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if not model_path.exists():
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print(
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f"Could not find the {model_path} file. Please run the chapter 6 code"
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" (ch06.ipynb) to generate the review_classifier.pth file."
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)
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sys.exit()
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# Instantiate model
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model = GPTModel(GPT_CONFIG_124M)
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# Convert model to classifier as in section 6.5 in ch06.ipynb
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num_classes = 2
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model.out_head = torch.nn.Linear(in_features=GPT_CONFIG_124M["emb_dim"], out_features=num_classes)
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# Then load model weights
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checkpoint = torch.load(model_path, map_location=device, weights_only=True)
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model.load_state_dict(checkpoint)
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model.to(device)
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model.eval()
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return tokenizer, model
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# Obtain the necessary tokenizer and model files for the chainlit function below
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tokenizer, model = get_model_and_tokenizer()
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@chainlit.on_message
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async def main(message: chainlit.Message):
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"""
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The main Chainlit function.
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"""
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user_input = message.content
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label = classify_review(user_input, model, tokenizer, device, max_length=120)
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await chainlit.Message(
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content=f"{label}", # This returns the model response to the interface
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).send()
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