The Tool Desk
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What “from scratch” means for a learning project
For this guide, “from scratch” means implementing the main parts of a GPT-style model and training a small version so you can follow the process end to end. It does not mean assembling a competitive foundation model with the data, compute, evaluation, and post-training resources used by leading AI labs. Sebastian Raschka’s official LLMs-from-scratch repository explicitly takes an educational, step-by-step PyTorch approach to developing, pretraining, and fine-tuning a GPT-like model.
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The learning goal is to understand the pipeline: raw text becomes token IDs; sequences of IDs become input and target examples; a decoder predicts a distribution over the next token; and training adjusts its parameters to improve those predictions. A working toy model is valuable even if its output is limited to the style and patterns present in its small training corpus.
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Prerequisites and environment
- Be comfortable with Python, functions, arrays or tensors, and basic debugging.
- Know the basics of neural networks: parameters, forward passes, loss, gradients, and optimization. You can learn these alongside the project, but they make the model code easier to reason about.
- Set up a working PyTorch environment and verify that you can create tensors and run a small model. PyTorch’s original paper describes the framework’s imperative deep-learning approach: PyTorch: An Imperative Style, High-Performance Deep Learning Library.
A small model can teach the mechanics on modest data and hardware; larger models, more training data, and longer runs require more compute. Begin with a scale that lets you iterate and inspect failures rather than treating hardware as a status symbol.
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Turn text into next-token examples
A model does not consume words as meanings. A tokenizer maps text into a vocabulary of token units—often whole words, word pieces, punctuation, or other symbols—and assigns each token an integer ID. The particular segmentation and vocabulary are part of the model’s input design; token IDs are labels for learned representations, not meanings by themselves.
Build a sequence and shift the target
Suppose a tokenized passage is [A, B, C, D, E]. With a context length of four, one training example can use [A, B, C, D] as input and [B, C, D, E] as the target. Each position is trained to predict the next token from the preceding context. Longer texts are split into overlapping or adjacent windows according to the training setup; the important invariant is that a target token must not be available to the model as future context for predicting itself.
Keep a validation portion separate from the text used to update model weights. If passages or near-duplicates cross the split, validation can look better than genuine performance because the model has effectively seen the answer during training.
Understand the components of a GPT-style decoder
The Transformer was introduced as a sequence architecture based on attention rather than recurrent processing. Vaswani and coauthors describe it as “a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely” in Attention Is All You Need (2017). A GPT-style language model uses a decoder-only form of the Transformer for autoregressive next-token prediction.
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- Use scikit-learn to track an example ML project end to end
- Explore several models, including support vector machines, decision trees, random forests, and ensemble methods
- Exploit unsupervised learning techniques such as dimensionality reduction, clustering, and anomaly detection
- Dive into neural net architectures, including convolutional nets, recurrent nets, generative adversarial networks, autoencoders, diffusion models, and transformers
- Use TensorFlow and Keras to build and train neural nets for computer vision, natural language processing, generative models, and deep reinforcement learning
Token and position representations
An embedding layer maps each token ID to a trainable vector. Because the same token can occur at different places in a sequence, the model also needs position information. A GPT-style implementation combines token representations with positional representations before sending them through the decoder blocks. The resulting vectors are learned features, not explicit definitions of the words.
Causal self-attention: queries, keys, and values
Self-attention lets each position gather information from other positions in its sequence. Linear projections form queries, keys, and values: queries are compared with keys to determine which available positions are relevant, and the resulting weights mix the value vectors. Multiple attention heads perform this operation in parallel with different learned projections.
For next-token training, attention must be causal: position t may use positions at or before t, but not later positions. A causal mask blocks attention to future positions. Without it, training could expose the answer token to the model, while generation at inference time would not have that future information.
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After attention, each Transformer block applies a position-wise feed-forward network, which transforms each position’s features independently. Residual connections provide paths that carry earlier representations forward, and normalization helps stabilize the computation. Implementations differ in details such as where normalization is placed, so follow one clearly specified design consistently rather than mixing variants by accident.
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Stack blocks and produce logits
A GPT-style decoder stacks Transformer blocks, then maps the final representation at each position to scores across the vocabulary. These scores are called logits. A softmax converts them into a probability distribution; the model is trained to assign high probability to the actual next token. The output layer is therefore a next-token predictor, not a direct text-to-answer lookup.
Train the model and generate text
Training and inference use the same learned network but serve different purposes. During training, batches contain input sequences and their shifted targets; the model computes logits for all target positions, and a loss measures how poorly those logits predict the correct tokens. An optimizer uses gradients of that loss to update parameters. During generation, the model receives a context, predicts a distribution for the next token, selects or samples one token, appends it to the context, and repeats.
A practical training sequence
- Prepare and inspect the corpus. Choose text you have the right to use, normalize it consistently, tokenize it, and inspect several decoded sequences to catch encoding or preprocessing mistakes.
- Create train and validation splits. Keep validation examples out of gradient updates. Split at a sensible boundary, such as documents or passages, when possible to reduce leakage between nearly identical text segments.
- Batch context windows. Sample batches of input windows and one-token-shifted targets. Confirm their lengths and inspect a few input-target pairs before training.
- Run the forward pass and loss. Feed the batch through the decoder, compute next-token logits and a cross-entropy loss against target IDs, then backpropagate gradients.
- Update parameters and monitor progress. Use an optimizer to apply parameter updates. Track training and validation loss at intervals; save checkpoints so you can resume or compare runs.
- Generate samples at checkpoints. Give the model a prompt drawn from the intended domain and decode generated token IDs back to text. Compare samples over training rather than relying on a single output.
Loss is useful for checking whether the model is learning to predict held-out tokens, but it does not establish that generated text is useful, factual, coherent over long spans, or safe. A model can reduce validation loss while still producing repetition, broken syntax, or plausible-sounding errors.
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Evaluate more than the training loss
Use held-out validation loss as a basic quantitative check, then inspect generated text for specific failure modes. Keep prompts and sampling settings consistent when comparing checkpoints; otherwise, changes in the prompt or decoding can be mistaken for changes in the model.
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- Repetition: Does the model loop on phrases or tokens?
- Local coherence: Do adjacent sentences follow one another, or does the passage lose track of its subject?
- Corpus imitation: Is the model learning broad patterns, or reproducing memorized passages? Inspect data and outputs, especially if the corpus is small.
- Domain fit: Does it handle examples similar to the intended use, and fail predictably outside them?
- Data leakage: Are validation results inflated because related or duplicate text appeared in training?
These checks are diagnostic, not a substitute for a systematic evaluation when a model will be used by others. The appropriate evaluation depends on the intended task, users, and risks.
Pretraining from random initialization is not fine-tuning
Pretraining starts with randomly initialized parameters and learns general statistical patterns from a large token corpus. Supervised fine-tuning starts with an already pretrained model and trains it further on labeled examples for a more specific behavior. Adapting existing pretrained weights is often a more practical project than pretraining a foundation model from scratch, but it does not teach exactly the same parts of the process: it relies on the capabilities and representations learned earlier.
Raschka’s companion repository covers both a from-scratch GPT-like implementation and fine-tuning, while framing the implementation as educational rather than as a recipe for frontier-scale pretraining: official code repository.
Why scaling changes the problem
Increasing parameter count alone does not guarantee a better model. Model size and the amount of training data interact with the compute budget: how much training can be completed, and how effectively that compute is used, matter alongside architecture. Hoffmann and coauthors analyze this relationship in Training Compute-Optimal Large Language Models (2022). Their work is a reason to treat size, token quantity, and compute as a joint planning problem, not to infer a universal recipe from one parameter count.
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The original Transformer paper reported 41.8 BLEU on WMT 2014 English-to-French for a single Transformer model trained for 3.5 days on eight GPUs. That is a specific historical machine-translation result from Vaswani and coauthors’ 2017 paper, not a present-day benchmark for chat models or an estimate of the hardware needed for current LLM training.
A tutorial model helps you understand tokenization, causal masking, optimization, and generation. A frontier-scale foundation model requires substantially different data and compute resources, as well as broad evaluation and post-training work. The mechanisms you learn transfer; the scale and operational demands do not.
Choose a structured learning resource
If you want a guided sequence rather than assembling each exercise yourself, two publisher-described books offer relevant paths. Their advertised scopes are not independent evaluations of teaching quality or results.
| Resource | Publisher-described scope | Best fit by stated goal |
|---|---|---|
| Build a Large Language Model (From Scratch) by Sebastian Raschka | Simon & Schuster lists chapter coverage that includes pretraining on unlabeled data: publisher listing. The official repository provides code for developing, pretraining, and fine-tuning a GPT-like model: GitHub repository. | A stepwise PyTorch implementation with runnable code and both pretraining and fine-tuning topics. |
| Building Large Language Models from Scratch: Design, Train, and Deploy LLMs with PyTorch by Dilyan Grigorov | Springer Nature / Apress advertises coverage from tokenization through modern components, training, and deployment, and lists a softcover format: publisher listing. | A resource to consider if the advertised design, training, and deployment scope matches your learning plan. |
For either book, check the live publisher listing for the edition, format, and regional availability that apply to you. A book’s educational implementation is not a turnkey plan for training a frontier-scale system.
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