--- 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. - 📄 **Paper**: [A Two-Parameter Weibull Framework for Diagnosing Transformer Weight Distributions](https://arxiv.org/abs/2605.18898) (arXiv:2605.18898) - 📦 **Source repo (primary)**: [github.com/tiexinding/NPM-Weibull-public](https://github.com/tiexinding/NPM-Weibull-public) — directory `database_v9_1/` - 🔧 **Python library**: `pip install npm-weibull-py` — [PyPI](https://pypi.org/project/npm-weibull-py/) --- ## 📌 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](https://github.com/tiexinding/NPM-Weibull-public/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) ```python 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 ```python 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) ```python 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](LICENSE) — free for academic and commercial use with attribution. --- ## Citation ```bibtex @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](https://github.com/tiexinding/NPM-Weibull-public/issues)