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lln_v16_blend_100k
cufa64/lln_v16_blend_100k
lln_v16_blend_100k is a machine learning model from cufa64. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as mit.
Zero-parameter language generation from pure graph topology.
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Public
Updated Apr 8, 2026
Repo size
2.1 GB
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.mdb2.1 GB · 100%
At a glance
- License
- mit
- Access
- Public
- Created
- Apr 8, 2026
- Updated
- Apr 8, 2026
- SHA
- bec35d24
Model card
From the Hugging Face model README
LLN v16 — Living Language Network (3-Corpus Blend)
Zero-parameter language generation from pure graph topology.
No neural networks. No gradient descent. No learned weights.
Model Details
| Property | Value |
|---|---|
| Vocabulary | 100,000 tokens |
| Forward edges | 117.5M directed |
| PMI edges | 34.5M |
| Trigram pairs | 11.9M |
| Total bigrams | 6.42 billion |
| Corpus | FineWeb-Edu (10GB) + Gutenberg (12GB) + OpenWebText (10GB) |
| Build time | ~3 hours (CPU) |
| Model size | 2.1 GB |
| Format | LMDB (data.mdb) |
Usage
pip install numpy lmdb huggingface_hub
git clone https://github.com/andycufari/lln
cd lln
python generate.py --prompt "The fire burned"
# The model auto-downloads on first run
Examples
| Prompt | Output |
|---|---|
| The king | a more powerful voice heard my lord hath commanded thee thy life . The most famous letter addressed a |
| She opened the door | opened fire on the door swung open . She paused abruptly closed the " " She laughed softly closed doors |
| The fire burned | alive , and the public safety training camp with his eyes flashed brightly glowing cheeks |
| The ship sailed | northward along the right to go straight to him so forth a big leagues farther inland navigation channel the whole |
| Scientists discovered | they would not believe that he began studying all I hope you don't expect to think you find you want |
Architecture
Generation uses a 5-phase pipeline:
- Frequency-penalized PMI activation — identifies semantic targets, suppresses rare-word hallucination
- Flow-aware target selection — avoids topological dead ends using local push/receive mass
- Beam search grammar walk — 5 competing paths find bridges across low-weight gaps
- Target depletion — reached targets are zeroed, activation landscape shifts
- Natural halting — generation stops when semantic energy is exhausted
See the whitepaper for full details.
License
MIT — Andy Cufari, 2026
- License
- mit
- Languages
- en
- Created
- Apr 8, 2026
- Updated
- Apr 8, 2026