medelharchaoui/gpt2-large-squad

GPT-2-large — full fine-tune on SQuAD (decoder-only baseline)

gpt2-large (774M) fully fine-tuned for extractive QA on SQuAD. This is the decoder-only baseline in an encoder–decoder vs decoder-only paradigm study, included so the comparison is at near-perfect parameter parity (774M vs T5-large's 737M).

TL;DR: even with all 774M parameters trained, the decoder-only model reaches only F1 0.5041 / EM 0.3516 — and is beaten by a T5-large encoder–decoder that trains just its cross-attention bridge (100.7M) by +0.31 F1 (1.61×), and by a 2.4M-param LoRA adapter by a similar margin. The gap is architectural, not a matter of parameter count.

Results (SQuAD validation)

Model	Architecture	Trainable / Total	EM	Token F1
GPT-2-large (this model)	decoder-only	774M / 774M	0.3516	0.5041
T5-small XA-only	enc-dec	6.3M / 60.5M	0.5293	0.7075
T5-large XA-only	enc-dec	100.7M / 737M	0.6406	0.8128
T5-large LoRA r=8	enc-dec	2.4M / 740M	0.6445	0.8152
T5-large full fine-tune	enc-dec	737M / 737M	0.6602	0.8162

Note that even T5-small (60M) — 13× smaller — outperforms this fully-tuned 774M decoder-only model on the same task.

Eval note: 512-example SQuAD-validation generation subset, 4-beam search.

How to use

Trained with prompt answer question: {question: ... context: ...}\nanswer: and the model generates the answer continuation. Match this format exactly:

from transformers import GPT2LMHeadModel, AutoTokenizer

repo = "medelharchaoui/gpt2-large-squad"
tok = AutoTokenizer.from_pretrained(repo)
model = GPT2LMHeadModel.from_pretrained(repo)
tok.pad_token = tok.eos_token

question = "What culture do 'bairn' and 'hyem' originate from?"
context = ("'bairn' and 'hyem' are geordie words with origins in scandinavia; barn and hjem "
           "are the corresponding modern norwegian and danish words.")
prompt = f"answer question: question: {question} context: {context}\nanswer: "
ids = tok(prompt, return_tensors="pt", truncation=True, max_length=384).input_ids
out = model.generate(ids, num_beams=4, max_new_tokens=16, pad_token_id=tok.eos_token_id)
print(tok.decode(out[0][ids.shape[1]:], skip_special_tokens=True))

Training

Setting	Value
Base model	gpt2-large (774M)
Fine-tuning	full (all params)
Dataset	rajpurkar/squad, 30,000 train examples
Precision	bf16, gradient checkpointing
Optimizer steps	3,000 (batch 2 × grad-accum 16 = eff. batch 32)
LR / warmup	5e-5, 300 warmup, weight decay 0.01
Source / target max len	384 / 32
Seed	37
Hardware	1× NVIDIA RTX 3060 (12 GB), local

Checkpoint is saved as pytorch_model.bin (streamed tensor-by-tensor to fit constrained RAM); from_pretrained() loads it automatically.

Limitations

• Provided primarily as a baseline for architecture comparison, not as a recommended QA model — the encoder–decoder checkpoints in this collection are substantially stronger.
• English SQuAD-style extractive QA only; evaluated on a validation subset, not the official SQuAD test server.

Citation

Part of an encoder–decoder vs decoder-only paradigm study (OptimiAI, 2026).