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https://github.com/rasbt/LLMs-from-scratch.git
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77 lines
2.3 KiB
Python
77 lines
2.3 KiB
Python
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# 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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generate,
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GPTModel,
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text_to_token_ids,
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token_ids_to_text,
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)
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def get_model_and_tokenizer():
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"""
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Code to loads a GPT-2 model with pretrained weights generated in chapter 5.
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This requires that you run the code in chapter 5 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": 256, # Shortened context length (orig: 1024)
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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": False # Query-key-value bias
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}
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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tokenizer = tiktoken.get_encoding("gpt2")
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model_path = Path("..") / "01_main-chapter-code" / "model.pth"
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if not model_path.exists():
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print(f"Could not find the {model_path} file. Please run the chapter 5 code (ch05.ipynb) to generate the model.pth file.")
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sys.exit()
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checkpoint = torch.load("model.pth", weights_only=True)
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model = GPTModel(GPT_CONFIG_124M)
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model.load_state_dict(checkpoint)
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model.to(device)
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return tokenizer, model, GPT_CONFIG_124M
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# Obtain the necessary tokenizer and model files for the chainlit function below
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tokenizer, model, model_config = 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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token_ids = generate(
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model=model,
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idx=text_to_token_ids(message.content, tokenizer), # The user text is provided via as `message.content`
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max_new_tokens=50,
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context_size=model_config["context_length"],
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top_k=1,
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temperature=0.0
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)
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text = token_ids_to_text(token_ids, tokenizer)
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await chainlit.Message(
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content=f"{text}", # This returns the model response to the interface
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).send()
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