---
pretty_name: llmfp Fingerprint Library
license: other
license_name: cisco-proprietary
license_link: https://github.com/marctjones/llmfp/blob/main/LICENSE
tags:
  - model-provenance
  - model-fingerprinting
  - lineage-detection
  - supply-chain-security
  - llmfp
  - jaccard-position-similarity
  - static-weight-analysis
size_categories:
  - n<1K
task_categories:
  - feature-extraction
configs:
  - config_name: default
    data_files:
      - split: train
        path: fingerprints/*.json
---

# llmfp Fingerprint Library

A library of pre-computed **weight-extrema fingerprints** for ~360 LLMs, generated by [`llmfp`](https://github.com/marctjones/llmfp) — a forensic analysis tool that detects AI model lineage by reading weight files (SafeTensors, GGUF, PyTorch, ONNX) **without executing the model or requiring a GPU**.

This dataset is the canonical store for the fingerprint `.json` files. Local copies in the `llmfp` repo are working copies, not committed to git (50–100 MB each). The registry metadata (parent/modification/notes for every entry) lives at `index.yaml` here and at `fingerprints_index.yaml` in the `llmfp` repo.

## What's in a fingerprint

A fingerprint is a deterministic, GPU-free summary of a model's weight tensors. Each `*.json` file contains, per tensor:

- **Extrema samples** — positions and values of the largest-magnitude weights (the lineage-bearing signal; positions are preserved through fine-tuning but random for independently trained models)
- **Grid samples** — uniform position grid for cross-validation of similarity metrics
- **Topographical samples** — multi-scale "peak count" structure for shape analysis
- **Per-tensor randomness statistics** — kurtosis, Shannon entropy, Gini coefficient, sparsity at three thresholds, gzip compression ratio
- **Tensor metadata** — shape, dtype, parameter count, semantic category (attention / mlp / norm / embedding / lm_head)

Plus per-fingerprint metadata: source model identifier, sampling mode + sample sizes, `model_config` snapshot (architecture, vocab_size, etc.), `total_parameters`, `tensor_count`, and detected modifications (quantization config, sharding, etc.).

## Sampling modes

| mode | extrema | grid | topographical | use case |
|---|---|---|---|---|
| `fast` | 512 | – | – | exploratory checks; lower accuracy |
| **`balanced`** (default) | **2048** | **2048** | 384 (16/64/192 scales × 128) | **canonical mode for all entries here** |
| `full` | 8192 | 8192 | 384 | research / ground-truth reference |

**All current 359 entries in this dataset are at `balanced` (2048 extrema samples)**, so cross-comparison is well-defined. See "Compatibility caveats" below.

## Compatibility caveats

**Cross-comparison requires matching sample sizes.** If fingerprint A was generated with 2048 samples and fingerprint B with 3000 samples, the comparator's value-correlation metrics (cosine, mean-diff, etc.) become meaningless because the stored sample positions diverge — even bit-identical model weights can produce `cosine ≈ 0` because the cross-position alignment is undefined.

As of llmfp **v3.7.5**, the comparator detects this and refuses with `IncompatibleSampleSizeError` (exit code 4). The `--force-mismatched-samples` CLI flag bypasses it for exploratory use.

The library was migrated to a uniform 2048-sample baseline in v3.7.5–v3.7.6. Two outliers remain (Qwen3-14B and Mistral-Nemo-Instruct-2407 at the pre-Dec-2025 3000-sample default — too large to resample on the v3.7.5 development box).

## Naming convention

Files follow `<org>_<model>.json`, where the HF model identifier has `/` replaced with `_`:

- `meta-llama_Llama-2-7b-hf.json` ← `meta-llama/Llama-2-7b-hf`
- `microsoft_phi-2.json` ← `microsoft/phi-2`
- `TheBloke_Llama-2-13B-GPTQ.json` ← `TheBloke/Llama-2-13B-GPTQ`

To recover the HF model id from a filename, replace the first `_` with `/`.

A few entries use variant disambiguators (`<org>_<model>-<variant>.json`) when multiple fingerprints share the same HF repo id (e.g., quantization variants).

## Repository layout

```
marcjon/llmfp-fingerprints/
├── README.md                ← this file
├── index.yaml               ← per-entry metadata: parent, modification, notes, model_card url, ...
└── fingerprints/
    ├── meta-llama_Llama-2-7b-hf.json
    ├── meta-llama_Llama-2-7b-chat-hf.json
    └── ... (~360 entries total)
```

## How to consume

### With the llmfp library

```bash
# Pull a snapshot of this dataset to local disk
python scripts/hf_library_sync.py pull --repo marcjon/llmfp-fingerprints

# Now point llmfp at any fingerprint
llmfp compare fingerprints/meta-llama_Llama-2-7b-hf.json \
              fingerprints/meta-llama_Llama-2-7b-chat-hf.json
```

### Without llmfp

Each `.json` file is a self-contained, human-readable record:

```python
import json
fp = json.load(open("meta-llama_Llama-2-7b-hf.json"))
print(fp["tensor_count"], fp["total_parameters"])
print(fp["sampling_methods"], fp["sampling_mode"])
for tname, t in list(fp["tensors"].items())[:3]:
    extrema = t["fingerprints"]["extrema"]
    print(tname, "extrema samples:", len(extrema["samples"]))
```

The compressed variants the llmfp generator can emit (`.json.zst` ≈ 7× smaller, `.parquet` ≈ 10× smaller) are bit-identical on round-trip; llmfp loaders auto-detect by magic bytes. This dataset currently stores the plain `.json` form for transparency.

## Coverage and quality

As of 2026-05-28:

- **359 fingerprints**, **350 indexed entries** in `index.yaml` (9 variant-disambiguated entries share an HF id)
- **Sampling mode**: 356/359 at `balanced` (2048 extrema + 2048 grid + 384 topographical); 2 outliers at the pre-Dec-2025 3000-sample default (Qwen3-14B, Mistral-Nemo-Instruct-2407 — too large for in-session resample); 1 at `full` mode (fdtn-ai/Foundation-Sec-8B-Instruct at 8192 samples)
- **Randomness statistics** (kurtosis, entropy, Gini, sparsity at 3 thresholds, compression ratio): present in 354/359 (98.6%). 5 older fingerprints predate the `compute_randomness_stats` default-on switch
- **`model_config` snapshot**: present in 359/359 (100%)
- **Embedding tensors** captured in 343/359 (96%). The 16 absent are mostly encoder-decoder (T5 / FLAN-T5 / mT5 / Cohere Aya) and GGUF/ONNX variants whose tokenizer layer is named differently or moved out of the main weight list; the comparator handles their alignment separately
- Coverage spans LLaMA, Mistral, Qwen, Phi, Gemma, BERT, T5, ViT, CLIP, Whisper, Wav2Vec2, ResNet, EfficientNet, GPT-2 families, plus MoE families (DeepSeek-V2-Lite, OLMoE, LLaMA-MoE, Qwen1.5-MoE, Qwen3-Coder-30B) and quantization variants (GPTQ, AWQ, GGUF k-quant, FP8, ONNX)
- Cisco MPK benchmark roster: 136/137 models present at balanced=2048 (the Qwen3-0.6B base/instruct pair was resampled in v3.7.6 closing the previous 134/137 gap)

## Update workflow

The local llmfp working copy and this HF dataset are kept in sync by `scripts/hf_library_sync.py`:

```bash
# Push the entire local library to HF (additive — won't reap retired entries)
python scripts/hf_library_sync.py push --repo marcjon/llmfp-fingerprints

# Push only the registry (fast metadata sync)
python scripts/hf_library_sync.py push-index --repo marcjon/llmfp-fingerprints

# Push only this README
python scripts/hf_library_sync.py push-readme --repo marcjon/llmfp-fingerprints

# Delete specific files from the remote
python scripts/hf_library_sync.py delete fingerprints/foo.json

# Pull a clean copy down
python scripts/hf_library_sync.py pull --repo marcjon/llmfp-fingerprints
```

Authentication is via the `huggingface_hub` SDK (`hf auth login` or `HF_TOKEN` env var). The dataset is private; access requires a token authorized by the owner.

## Sister datasets / projects

- [`llmfp`](https://github.com/marctjones/llmfp) — the tool that produces and consumes these fingerprints
- [`marcjon/ckasketch-sketches`](https://huggingface.co/datasets/marcjon/ckasketch-sketches) — sister dataset for `ckasketch` representational-geometry sketches (different format, complementary use case)
- [`lineagebench`](https://github.com/marctjones/lineagebench) — benchmark harness that consumes both datasets

## License

Cisco Proprietary. See the [LICENSE](https://github.com/marctjones/llmfp/blob/main/LICENSE) file in the `llmfp` repository.

The fingerprints themselves are derived static summaries of public model weights — they contain no model parameters or generative capacity. Each entry's `model_card` URL in `index.yaml` points at the upstream model's HF page and license; consult that for the upstream license of the model being fingerprinted.

## Citation

If this dataset informs published work, please cite the `llmfp` repository and tag the dataset version (HF dataset revisions are immutable Git refs):

```bibtex
@misc{llmfp_fingerprints_2026,
  author = {Jones, Marc},
  title  = {llmfp Fingerprint Library},
  year   = {2026},
  publisher = {Hugging Face Hub},
  url    = {https://huggingface.co/datasets/marcjon/llmfp-fingerprints},
}
```