TiexinDing/NPM-Weibull-DATABASE-v9_1

NPM-Weibull DATABASE v9_1

Benchmark database of Weibull (k, λ) fits for 12 transformer model entries spanning 7 architectural families (Pythia, OLMo-1/2, LLaMA-3, Mistral, Qwen2.5, Qwen3) — 70M–14B parameters, GeLU and SwiGLU activations, Pre-LN and QK-Norm placements.

• 📄 Paper: A Two-Parameter Weibull Framework for Diagnosing Transformer Weight Distributions (arXiv:2605.18898)
• 📦 Source repo (primary): github.com/tiexinding/NPM-Weibull-public — directory database_v9_1/
• 🔧 Python library: pip install npm-weibull-py — PyPI

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📌 Source of truth

The primary, authoritative location of this dataset is the GitHub repository above (directory database_v9_1/). This Hugging Face Dataset is a mirror synced from GitHub on each tagged release, provided for load_dataset(...) convenience and Dataset Viewer discoverability.

• Updates are batched per release, not per commit — for the latest state, watch the GitHub repo.
• Issues and pull requests: please file on GitHub Issues rather than HF discussions (faster response).
• License, citation, and documentation are kept consistent across both surfaces.

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Quick start

Option 1 — load_dataset (Hugging Face)

from datasets import load_dataset

ds = load_dataset("TiexinDing/NPM-Weibull-DATABASE-v9_1")
print(ds["train"][0])  # First entry: pythia-70m

Option 2 — pandas

import pandas as pd

df = pd.read_csv("DATABASE_v9_1.csv")
print(df[["entry_id", "k_median_o", "lambda_median_o"]].head())

Option 3 — npm-weibull-py library (recommended for analysis)

from npm_weibull import DATABASE_v9_1, compare_to_benchmark

print(DATABASE_v9_1)  # 12 entries with per-component fits

cmp = compare_to_benchmark({
    "median_k_per_kind": {"q": 1.14, "k": 1.13, "v": 1.19, "o": 1.19}
})
print(cmp["nearest_neighbor"])  # nearest of the 12 benchmark entries

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Dataset structure

12 entries

entry_id	Family	Size	Architecture	QK-Norm	Tokens
pythia-70m	Pythia	70m	MHA-merged	False	300B
pythia-160m	Pythia	160m	MHA-merged	False	300B
pythia-410m	Pythia	410m	MHA-merged	False	300B
pythia-1b	Pythia	1B	MHA-merged	False	300B
pythia-6.9b	Pythia	6.9B	MHA-merged	False	300B
olmo-1-7b	OLMo-1	7B	MHA-separate	False	2.5T
olmo-2-7b	OLMo-2	7B	MHA-separate	True	5T
llama-3-8b	LLaMA-3	8B	GQA-4:1	False	15T
mistral-7b	Mistral	7B	GQA-4:1	False	8T
qwen2.5-7b	Qwen2.5	7B	GQA-7:1	False	18T
qwen2.5-14b	Qwen2.5	14B	GQA-5:1	False	18T
qwen3-8b	Qwen3	8B	GQA-4:1	True	36T

Columns (55 total)

Identifiers: entry_id, family, size, arch

Architecture: n_q, n_kv (head counts for GQA), qk_norm

Training hyperparameters: training_tokens, eta_peak (peak learning rate), lambda_wd (weight decay), T_steps, tau_iter = 1/(η·λ_wd), T_over_tau (Wang-Aitchison 2024 cycle ratio), Physical_State, hp_confidence (explicit / inferred / estimated)

Per-component Weibull fits (median across blocks within each model):

• Pythia entries (GeLU 2-projection FFN with merged W_qkv): populate qkv, o, ffn_in, ffn_out
• Non-Pythia entries (SwiGLU 3-projection FFN with separate Q/K/V): populate q, k, v, o, gate, up, down

Each component has four columns: k_median_*, lambda_median_*, R2_median_*, R2_below_95_* (count of blocks with R² < 0.95).

n_records = total number of per-block Weibull fits aggregated.

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Key findings (paper §3)

1. Transmission Class (FFN + W_o): the median terminal k across components per entry, then aggregated across the 12 entries, falls in [1.186, 1.204] with cross-family CV = 0.51%. Shared across SwiGLU and GeLU activations, Pre-LN and QK-Norm placements, and model sizes 70M → 14B.

2. Selection Class (W_q, W_k): k departs from initialization anchor (k_init ≈ 1.205), severity tracks attention storage architecture — separately-stored MHA (OLMo-1/2) deepest at k ∈ [0.76, 0.99], GQA (LLaMA-3, Mistral, Qwen2.5/3) milder at [1.10, 1.16], Pythia merged W_qkv transitional at [1.05, 1.18].

3. λ scaling: terminal λ ∝ √(η/λ_wd) within Pythia, Pearson r = 0.94 across the three Transmission Class component kinds, directionally consistent with Fan et al. (2025) AdamW steady-state analysis.

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Files in this dataset

File	Purpose
DATABASE_v9_1.csv	Machine-readable dataset (55 columns × 12 entries)
DATABASE_v9_1.md	Human-readable reference table
DATABASE_v9_1_report.md	Per-entry sanity verification
populate_database_v9_1.py	Internal regeneration script (dev-only; requires cascade v3 raw per-block fits not shipped here — see GitHub repo for the cascade pipeline)
LICENSE	CC BY 4.0 license text
README.md	This file

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License

CC BY 4.0 — free for academic and commercial use with attribution.

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Citation

@article{ding2026twoparameterweibull,
  title   = {A Two-Parameter Weibull Framework for Diagnosing Transformer Weight Distributions},
  author  = {Ding, Tiexin},
  journal = {arXiv preprint arXiv:2605.18898},
  year    = {2026},
  doi     = {10.48550/arXiv.2605.18898},
  url     = {https://arxiv.org/abs/2605.18898}
}

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Author: Tiexin Ding · NeuralCAE

Issues / questions: GitHub Issues