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Model: Crusadersk/gpt2-50m
Source: Original Platform

README.md

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+---
+language:
+- en
+tags:
+- gpt2
+- scaling-study
+- benchmarking
+- banterhearts
+pipeline_tag: text-generation
+library_name: transformers
+license: mit
+---
+
+# GPT-2 50M
+
+Custom-trained GPT-2 checkpoint with deliberate depth-width configuration for inference benchmarking research.
+
+Created as part of the [Banterhearts research program](https://github.com/Sahil170595/Banterhearts) investigating benchmarking integrity for local LLM inference.
+
+| | |
+|---|---|
+| **Architecture** | GPT2LMHeadModel (MHA) |
+| **Parameters** | 50M |
+| **Config** | n_embd=512, n_head=2, n_layer=8, n_inner=2048 |
+| **Context length** | 1,024 tokens |
+| **Precision** | FP32 |
+| **Model size** | 197 MB |
+| **Vocab size** | 50,257 |
+
+## Purpose
+
+Mid-scale MHA model for scaling-regime validation.
+
+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: TR117, 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/gpt2-50m")
+tokenizer = AutoTokenizer.from_pretrained("Crusadersk/gpt2-50m")
+
+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{banterhearts2026gpt250m,
+  title = {Custom GPT-2 Scaling Checkpoint (50M) for Inference Benchmarking Research},
+  author = {Kadadekar, Sahil},
+  year = {2026},
+  url = {https://huggingface.co/Crusadersk/gpt2-50m},
+  note = {Part of the Banterhearts research program. NeurIPS 2026 submission.}
+}
+```
+
+## Acknowledgments
+
+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).