
Architecture	GPT2LMHeadModel (MHA)
Parameters	4.6M
Config	n_embd=2, n_head=2, n_layer=2
Context length	1,024 tokens
Precision	FP32
Model size	2.4 MB
Vocab size	50,257

Purpose

Environment validation and weight parity checks.

These checkpoints are not general-purpose language models. They are deliberately sized scaling-study artifacts designed to isolate the effect of model depth vs width on GPU inference latency. The key finding: in the small-model GPU regime, layer depth (not parameter count) dominates latency, producing inversions where a 5M-parameter model can be 3.6x slower than a 25M-parameter model.

Source Technical Reports

Used in: TR126, TR147

TR	Role
TR117	Original cross-backend benchmark matrix (7 backends, 4 model groups)
TR126	Linux/Triton compiler validation with phase-separated measurement
TR147	Second-regime portability validation on RTX 6000 Ada

Design Rationale

The GPT-2 family (25M, 50M, 100M) uses a 2x3 factorial design:

Model	n_embd	n_layer	n_inner	Params	Design role
gpt2-25m	384	3	1,536	25M	Shallow, narrow
gpt2-50m	512	8	2,048	50M	Deep, medium width
gpt2-100m	768	8	3,072	100M	Deep, wide

All models use 2 attention heads (MHA, not GQA) to isolate architecture effects from attention-group structure. Dropout is set to 0.0 for deterministic inference measurement.

Usage

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("Crusadersk/tiny-gpt2")
tokenizer = AutoTokenizer.from_pretrained("Crusadersk/tiny-gpt2")

inputs = tokenizer("Hello", return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=32, do_sample=False)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Compatibility

Framework	Supported
Transformers	Yes
torch.compile (Inductor)	Yes
Ollama	No (not GGUF format)
vLLM	Yes

Citation

bibtex

@misc{banterhearts2026tinygpt2,
  title = {Custom GPT-2 Scaling Checkpoint (4.6M) for Inference Benchmarking Research},
  author = {Kadadekar, Sahil},
  year = {2026},
  url = {https://huggingface.co/Crusadersk/tiny-gpt2},
  note = {Part of the Banterhearts research program. NeurIPS 2026 submission.}
}

Acknowledgments

This work is part of the Chimera/Banterhearts technical-report program on deployment-time LLM behavior, quantization, refusal robustness, batching effects, and inference-stack reliability. Canonical public archive: Chimeraforge Reports; source context: github.com/Sahil170595/Banterhearts.