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- [07_gpt_to_llama](07_gpt_to_llama) contains a step-by-step guide for converting a GPT architecture implementation to Llama 3.2 and loads pretrained weights from Meta AI
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- [08_memory_efficient_weight_loading](08_memory_efficient_weight_loading) contains a bonus notebook showing how to load model weights via PyTorch's `load_state_dict` method more efficiently
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- [09_extending-tokenizers](09_extending-tokenizers) contains a from-scratch implementation of the GPT-2 BPE tokenizer
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- [10_llm-training-speed](10_llm-training-speed) shows PyTorch performance tips to improve the LLM training speed
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- [10_llm-training-speed](10_llm-training-speed) shows PyTorch performance tips to improve the LLM training speed
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<br>
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<br>
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[](https://www.youtube.com/watch?v=Zar2TJv-sE0)
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# `llms-from-scratch` PyPI Package
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This optional PyPI package lets you conveniently import code from various chapters of the *Build a Large Language Model From Scratch* book.
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## Installation
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### From PyPI
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Install the `llms-from-scratch` package from the official [Python Package Index](https://pypi.org/project/llms-from-scratch/) (PyPI):
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```bash
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pip install llms-from-scratch
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```
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> **Note:** If you're using [`uv`](https://github.com/astral-sh/uv), replace `pip` with `uv pip` or use `uv add`:
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```bash
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uv add llms-from-scratch
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```
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### Editable Install from GitHub
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If you'd like to modify the code and have those changes reflected during development:
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```bash
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git clone https://github.com/rasbt/LLMs-from-scratch.git
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cd LLMs-from-scratch
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pip install -e .
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```
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> **Note:** With `uv`, use:
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```bash
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uv add --editable . --dev
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```
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## Using the Package
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Once installed, you can import code from any chapter using:
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```python
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from llms_from_scratch.ch02 import GPTDatasetV1, create_dataloader_v1
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from llms_from_scratch.ch03 import (
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MultiHeadAttention,
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SelfAttention_v1,
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SelfAttention_v2,
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CausalAttention,
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MultiHeadAttentionWrapper
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)
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from llms_from_scratch.ch04 import (
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LayerNorm,
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GELU,
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FeedForward,
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TransformerBlock,
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GPTModel,
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generate_text_simple
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)
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from llms_from_scratch.ch05 import (
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generate,
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train_model_simple,
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evaluate_model,
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generate_and_print_sample,
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assign,
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load_weights_into_gpt,
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text_to_token_ids,
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token_ids_to_text,
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calc_loss_batch,
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calc_loss_loader,
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plot_losses
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)
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from llms_from_scratch.ch06 import (
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download_and_unzip_spam_data,
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create_balanced_dataset,
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random_split,
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SpamDataset,
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calc_accuracy_loader,
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evaluate_model,
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train_classifier_simple,
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plot_values,
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classify_review
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)
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from llms_from_scratch.ch07 import (
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download_and_load_file,
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format_input,
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InstructionDataset,
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custom_collate_fn,
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check_if_running,
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query_model,
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generate_model_scores
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)
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from llms_from_scratch.appendix_a import NeuralNetwork, ToyDataset
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from llms_from_scratch.appendix_d import find_highest_gradient, train_model
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```
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