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README.md

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+---
+license: cc-by-4.0
+task_categories:
+  - feature-extraction
+language:
+  - en
+tags:
+  - transformer
+  - attention
+  - rope
+  - power-law
+  - scaling-laws
+  - interpretability
+  - llm
+  - benchmark
+pretty_name: TAF Attention-Decay Measurements
+size_categories:
+  - n<1K
+configs:
+  - config_name: default
+    data_files:
+      - split: train
+        path: taf-attention-decay.jsonl
+---
+
+# TAF Attention-Decay Measurements
+
+> **First public dataset of attention-decay exponent γ measurements
+> across transformer LLMs.**
+> Companion to the paper *Predicting How Transformers Attend*
+> (Marín 2026, [Zenodo DOI 10.5281/zenodo.19826343](https://zenodo.org/records/19826343)).
+
+## What it is
+
+Each record is one γ measurement on one (model, corpus, precision) tuple.
+γ is the exponent of the power-law decay of attention weights at distance d:
+
+```
+A(d) ∝ d^(-γ)
+```
+
+predicted from RoPE geometry by the closed-form Padé formula
+
+```
+γ_padé = (2θ - T√2) / (2θ + T√2)
+```
+
+where θ is the RoPE base frequency and T is the evaluation context length.
+
+## Coverage
+
+- **32 models** across 12 families (Pythia, Qwen, Llama, Mistral, Gemma, Phi, OLMo, OLMoE, DeepSeek, StarCoder2, CodeLlama, GPT-J, SmolLM2, Falcon)
+- **58 records** total
+- **2 corpora**: real text (`real_text`, MongoDB English episodes) + random tokens (`random_tokens`)
+- **2 precisions**: 4-bit NF4 (BitsAndBytes) + bfloat16
+- **Includes random-init controls** (E2 falsifier on Pythia 70M/410M/1B with random Gaussian init, no pretraining) — establishes that the slope ν = ∂γ/∂log₁₀(P) ≈ −1/(2π) is genuinely a *training imprint*, not architecture artifact.
+
+## Schema
+
+Each JSONL row:
+
+```json
+{
+  "model_id": "EleutherAI/pythia-2.8b",
+  "revision": "main",
+  "arch": {
+    "d_model": 2560, "n_heads": 32, "n_layers": 32, "d_head": 80,
+    "n_kv_heads": 32, "n_params_M": 2800, "rope_theta": 10000,
+    "T_train": 2048, "family": "pythia",
+    "is_instruct": false, "is_moe": false
+  },
+  "measurement": {
+    "gamma": 0.674,
+    "gamma_ci95_lo": 0.65, "gamma_ci95_hi": 0.70,
+    "method": "pade_d_alias_T",
+    "fit": {"log_A": -3.21, "R2": 0.987, "n_points": 9, "delta_R2_power_minus_exp": 0.42},
+    "T_eval": 2048,
+    "corpus": "real_text",
+    "n_prompts_per_distance": 150,
+    "seeds": [42, 123, 7],
+    "distances": [10, 20, 30, 50, 100, 200, 500, 1000, 2000],
+    "precision": "4-bit-NF4"
+  },
+  "predictions": {
+    "gamma_pade": 0.747,
+    "gamma_random_pred": null,
+    "imprint_constant_nu": -0.1592
+  },
+  "decision": "MED gamma=0.674 (R²=0.987)",
+  "provenance": {
+    "taf_version": "0.4",
+    "paper_doi": "10.5281/zenodo.19826343",
+    "source_file": "EleutherAI--pythia-2.8b_mongo.json",
+    "tool": "tafagent/cli/diagnose_model.py + e4_extended_gamma.py",
+    "license_data": "CC-BY-4.0",
+    "license_code": "Apache-2.0"
+  }
+}
+```
+
+## Usage
+
+```python
+from datasets import load_dataset
+ds = load_dataset("karlexmarin/taf-attention-decay")
+print(ds["train"][0])
+```
+
+```python
+import pandas as pd
+df = pd.read_json("taf-attention-decay.jsonl", lines=True)
+df_text = df[df["measurement"].apply(lambda m: m["corpus"] == "real_text")]
+df_text["gamma"] = df_text["measurement"].apply(lambda m: m["gamma"])
+print(df_text.groupby("arch")["gamma"].describe())
+```
+
+## Why this dataset exists
+
+The attention-decay exponent γ is a single-number diagnostic of how
+"locally" or "globally" a transformer attends. It connects RoPE geometry
+to long-context behavior, KV-cache compression, NIAH retrieval, and
+hallucination rates — see the companion paper for details.
+
+Until now, no public dataset of γ measurements existed across LLMs.
+This release closes that gap.
+
+## What's NOT in this dataset
+
+- **Raw attention tensors** (TB-scale, redundant with model weights)
+- **Per-layer per-head γ-fields** (separate dataset planned)
+- **Training-trajectory γ over checkpoints** (separate, from Pythia ckpts)
+- **Downstream task scores** (use RULER, LongBench-v2, HELM separately)
+
+## License
+
+- **Data (this dataset)**: CC-BY-4.0
+- **Measurement code**: Apache-2.0 ([github.com/karlesmarin/tafagent](https://github.com/karlesmarin/tafagent))
+- **Underlying model weights**: respective HuggingFace licenses (consult each model's card)
+
+## Citation
+
+```bibtex
+@dataset{marin2026taf_attention_decay,
+  author    = {Mar{\'\i}n, Carles},
+  title     = {TAF Attention-Decay Measurements},
+  year      = {2026},
+  publisher = {HuggingFace},
+  url       = {https://huggingface.co/datasets/karlexmarin/taf-attention-decay},
+  license   = {CC-BY-4.0}
+}
+
+@article{marin2026predicting,
+  author    = {Mar{\'\i}n, Carles},
+  title     = {Predicting How Transformers Attend: Analytic Power-Law Theory,
+               Phase Transitions, and Practical Compression Tools},
+  year      = {2026},
+  doi       = {10.5281/zenodo.19826343},
+  url       = {https://zenodo.org/records/19826343}
+}
+```
+
+## Acknowledgements
+
+This dataset would not exist without:
+
+- **EleutherAI** for the Pythia panel (8 sizes from 14M to 2.8B), the
+  primary scientific anchor of the framework.
+- **AI2** for OLMo / OLMoE.
+- **Meta**, **Mistral AI**, **Qwen team / Alibaba**, **Google DeepMind**,
+  **Microsoft**, **HuggingFace SmolLM team**, **DeepSeek-AI**, **TII**
+  (Falcon), and **BigScience** (BLOOM) for releasing weights publicly.
+- The **HuggingFace Hub** for free hosting that made the measurements possible.
+
+## Reproducibility
+
+The measurement protocol is fully open:
+- Tool: [github.com/karlesmarin/tafagent](https://github.com/karlesmarin/tafagent), `cli/diagnose_model.py`
+- Browser tool: [karlesmarin.github.io/tafagent](https://karlesmarin.github.io/tafagent)
+
+Each row in this dataset can be reproduced from the original model weights
+via the open tool. If you find a discrepancy, please open an issue at the
+GitHub repo — refutations are welcome.
+
+## Updates
+
+- 2026-04-29: Initial release (58 records, 32 models, 2 corpora, 2 precisions)
+- Future: training-trajectory data (Pythia checkpoint γ-flow), per-layer γ-fields,
+  fp16 anchor measurements (DeepSeek-chat verification, Llama-3-8B cross-paper anchor)