GPT vs BERT

This is the code-heavy post, and the cover sets the expectation: enough theory, run both models in one notebook and feel the difference. The promise is a complete loop — generate with GPT-2, fill a blank with BERT, fine-tune BERT for classification, and inspect GPT's raw next-token probabilities.

The goal of this post is muscle memory. Reading that GPT generates and BERT understands is one thing; watching GPT stream tokens and BERT recover a masked word in your own terminal is what makes the split permanent.

The setup slide keeps dependencies minimal. Hugging Face transformers is the standard library for both model families, and it exposes a consistent API across them. The single pip install pulls transformers, torch, and datasets, and the imports bring in the four AutoModel classes the post uses.

Importing all four AutoModelFor* classes up front previews the post's central lesson: the same backbone serves many jobs, and you select the job by choosing the right class. AutoModelForCausalLM is for GPT-style generation, AutoModelForMaskedLM for BERT's fill-blank, and AutoModelForSequenceClassification for fine-tuned classification.

This slide demonstrates GPT-2 generation end to end. It loads the tokenizer and the causal LM model, encodes a prompt, and calls model.generate with sampling parameters — max_new_tokens caps the output length, do_sample with top_k introduces controlled randomness so the output isn't deterministic and dull.

The key observation is that generate produces new tokens left-to-right, exactly mirroring the autoregressive training objective from the mechanics post. The decoded output is fresh text continuing the prompt. This is GPT's whole identity — give it a beginning, get a continuation — made concrete in six lines.

This slide demonstrates BERT's fill-mask capability at a lower level than the earlier pipeline, so the reader sees the mechanics. It loads BERT's tokenizer and masked LM model, encodes a sentence containing [MASK], runs a forward pass to get logits, locates the masked position, and reads off the top predictions.

The explicit steps — finding mask_token_id, indexing the logits at the mask position, taking topk — reveal what the fill-mask pipeline does internally. BERT looks at the whole sentence, including the words after the blank, and predicts the most likely fillers. Seeing it predict 'capital' for 'Paris is the [MASK] of France' demonstrates bidirectional understanding directly.

This is the transfer-learning payoff slide: fine-tuning BERT for classification. It loads AutoModelForSequenceClassification with num_labels=2, which attaches a small classification head on top of the pretrained BERT backbone. It tokenizes a tiny batch with padding, supplies labels, computes the loss, and calls backward for one gradient step.

The comment notes that a real training loop wraps this with an optimizer and multiple epochs, but the essential mechanic is visible: BERT already understands language from pretraining, so you only teach a small head to map its representations to your labels. This is why BERT fine-tuning needs little data and few epochs, which the later slide expands on.

This slide pauses the code to drive home the post's organizing insight: all three model loads share the same kind of transformer backbone, and the difference is the head bolted on top. CausalLM adds a next-token prediction head; MaskedLM adds a fill-blank head; SequenceClassification adds a small classifier.

This reframes the entire AutoModelFor* family. You're not loading fundamentally different networks — you're loading the same pretrained backbone with a task-specific head. Choosing the right AutoModel class is literally how you choose the job, which makes the library's design intuitive once you see it this way.

This slide goes one level deeper than the generate call, reading GPT's raw next-token probabilities directly. It runs a forward pass, takes the logits at the last position, applies softmax to turn them into a probability distribution over the vocabulary, and prints the top-5 most likely next tokens.

This exposes what generation actually is under the hood: at each step the model produces a probability distribution over the next token, and the generation strategy (greedy, sampling, top-k) just picks from it. Seeing ' Paris' rank highly after 'The capital of France is' demonstrates that GPT has absorbed real factual associations purely from next-token prediction. It also bridges to the mistakes post's point that these are statistics, not guaranteed facts.

The stack diagram visualizes the post's central theme: same backbone, different heads. At the bottom sit the token and position embeddings (the input layer), in the middle the shared transformer blocks (the backbone), and on top the task head — causal, masked, or classifier depending on the job.

This layered picture is the takeaway the reader should retain. The expensive, reusable knowledge lives in the backbone; the cheap, swappable task adaptation lives in the head. It explains both why fine-tuning is efficient and why the AutoModelFor* classes are organized the way they are.

This slide explains why the BERT classification step in slide 3 looked almost too easy. The backbone already understands language from massive pretraining, so fine-tuning only needs to teach a small head to map those representations onto your specific labels. That requires little labeled data and just a few epochs.

This is transfer learning made concrete. You're not teaching BERT English from scratch; you're teaching it that, given its existing understanding, this region of representation space means 'positive' and that region means 'negative'. The promise of pretraining — learn once, adapt cheaply — is exactly what makes this work, and it's the core reason BERT was so impactful.

The production tips translate the demo into real practice. Use the AutoModelFor* classes to select the task. Prefer DistilBERT when you need cheap, fast classification — it's about 40% smaller and nearly as accurate. Tune do_sample, top_k, and temperature to control GPT's output quality and creativity. Batch and pad inputs for throughput. And use the Trainer API for real fine-tuning rather than hand-rolling the loop.

These tips bridge from the minimal teaching snippets to production-grade code. The single-step loss.backward() in the demo is fine for illustration, but real fine-tuning needs an optimizer, a learning-rate schedule, evaluation, and checkpointing — all of which the Trainer API handles cleanly.

The CTA closes the hands-on post and sets up the final angle: failure modes. Now that the reader can run both models, the responsible next step is learning where they go wrong in practice.

The teaser lists the traps the last post covers — wrong-model choices, [CLS] embedding mistakes, and the bidirectional gotcha — the exact issues that turn a working demo into a broken production feature if ignored.