95 lines
3.2 KiB
Markdown
95 lines
3.2 KiB
Markdown
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---
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language:
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- en
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tags:
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- gpt2
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- scaling-study
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- benchmarking
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- banterhearts
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pipeline_tag: text-generation
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library_name: transformers
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license: mit
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---
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# GPT-2 50M
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Custom-trained GPT-2 checkpoint with deliberate depth-width configuration for inference benchmarking research.
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Created as part of the [Banterhearts research program](https://github.com/Sahil170595/Banterhearts) investigating benchmarking integrity for local LLM inference.
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| **Architecture** | GPT2LMHeadModel (MHA) |
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| **Parameters** | 50M |
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| **Config** | n_embd=512, n_head=2, n_layer=8, n_inner=2048 |
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| **Context length** | 1,024 tokens |
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| **Precision** | FP32 |
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| **Model size** | 197 MB |
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| **Vocab size** | 50,257 |
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## Purpose
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Mid-scale MHA model for scaling-regime validation.
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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.
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## Source Technical Reports
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Used in: TR117, TR126, TR147
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| TR | Role |
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| TR117 | Original cross-backend benchmark matrix (7 backends, 4 model groups) |
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| TR126 | Linux/Triton compiler validation with phase-separated measurement |
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| TR147 | Second-regime portability validation on RTX 6000 Ada |
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## Design Rationale
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The GPT-2 family (25M, 50M, 100M) uses a 2x3 factorial design:
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| Model | n_embd | n_layer | n_inner | Params | Design role |
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|---|---|---|---|---|---|
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| gpt2-25m | 384 | 3 | 1,536 | 25M | Shallow, narrow |
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| gpt2-50m | 512 | 8 | 2,048 | 50M | Deep, medium width |
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| gpt2-100m | 768 | 8 | 3,072 | 100M | Deep, wide |
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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.
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## Usage
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model = AutoModelForCausalLM.from_pretrained("Crusadersk/gpt2-50m")
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tokenizer = AutoTokenizer.from_pretrained("Crusadersk/gpt2-50m")
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inputs = tokenizer("Hello", return_tensors="pt")
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outputs = model.generate(**inputs, max_new_tokens=32, do_sample=False)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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## Compatibility
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| Framework | Supported |
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| Transformers | Yes |
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| torch.compile (Inductor) | Yes |
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| Ollama | No (not GGUF format) |
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| vLLM | Yes |
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## Citation
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```bibtex
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@misc{banterhearts2026gpt250m,
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title = {Custom GPT-2 Scaling Checkpoint (50M) for Inference Benchmarking Research},
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author = {Kadadekar, Sahil},
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year = {2026},
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url = {https://huggingface.co/Crusadersk/gpt2-50m},
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note = {Part of the Banterhearts research program. NeurIPS 2026 submission.}
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}
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```
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## Acknowledgments
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This work is part of a 40-TR research program on consumer LLM deployment safety, conducted independently as pre-doctoral research. Full program details at [github.com/Sahil170595/Banterhearts](https://github.com/Sahil170595/Banterhearts).
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