Upload GPT-2 25M for TR147 portability validation

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 25M
+
+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** | 25M |
+| **Config** | n_embd=384, n_head=2, n_layer=3, n_inner=1536 |
+| **Context length** | 1,024 tokens |
+| **Precision** | FP32 |
+| **Model size** | 96 MB |
+| **Vocab size** | 50,257 |
+
+## Purpose
+
+Small-scale MHA baseline for depth-width trade-off analysis.
+
+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/gpt2-25m")
+tokenizer = AutoTokenizer.from_pretrained("Crusadersk/gpt2-25m")
+
+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{banterhearts2026gpt225m,
+  title = {Custom GPT-2 Scaling Checkpoint (25M) for Inference Benchmarking Research},
+  author = {Kadadekar, Sahil},
+  year = {2026},
+  url = {https://huggingface.co/Crusadersk/gpt2-25m},
+  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).

config.json

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+{
+  "activation_function": "gelu_new",
+  "architectures": [
+    "GPT2LMHeadModel"
+  ],
+  "attn_pdrop": 0.0,
+  "bos_token_id": 50256,
+  "dtype": "float32",
+  "embd_pdrop": 0.0,
+  "eos_token_id": 50256,
+  "initializer_range": 0.02,
+  "layer_norm_epsilon": 1e-05,
+  "model_type": "gpt2",
+  "n_ctx": 1024,
+  "n_embd": 384,
+  "n_head": 2,
+  "n_inner": 1536,
+  "n_layer": 3,
+  "n_positions": 1024,
+  "reorder_and_upcast_attn": false,
+  "resid_pdrop": 0.0,
+  "scale_attn_by_inverse_layer_idx": false,
+  "scale_attn_weights": true,
+  "summary_activation": null,
+  "summary_first_dropout": 0.1,
+  "summary_proj_to_labels": true,
+  "summary_type": "cls_index",
+  "summary_use_proj": true,
+  "transformers_version": "4.57.0",
+  "use_cache": true,
+  "vocab_size": 50257
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 50256,
+  "eos_token_id": 50256,
+  "transformers_version": "4.57.0"
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:da3b2fbc76d1fa1122cfd00415dfacc7d6302016646608a8396f3244854280d9
+size 100068208

special_tokens_map.json

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+{
+  "bos_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": "<|endoftext|>",
+  "unk_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

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+{
+  "add_bos_token": false,
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "50256": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<|endoftext|>",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|endoftext|>",
+  "errors": "replace",
+  "extra_special_tokens": {},
+  "model_max_length": 1024,
+  "pad_token": "<|endoftext|>",
+  "tokenizer_class": "GPT2Tokenizer",
+  "unk_token": "<|endoftext|>"
+}