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NPM-Weibull-DATABASE-v9_1

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NPM-Weibull-DATABASE-v9_1 / README.md
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Initial release: DATABASE_v9_1 mirror from GitHub source-of-truth
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metadata 
license: cc-by-4.0
task_categories:
  - other
language:
  - en
size_categories:
  - n<1K
tags:
  - weibull
  - transformer
  - weight-statistics
  - model-diagnostics
  - npm-weibull
pretty_name: NPM-Weibull DATABASE v9_1
configs:
  - config_name: default
    data_files: DATABASE_v9_1.csv

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.

📌 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.

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

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.

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.

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

License

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

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}
}

Author: Tiexin Ding · NeuralCAE

Issues / questions: GitHub Issues