Downloads · 30 days
0
neurolex-v4-creative-name-diffusion
krystv/neurolex-v4-creative-name-diffusion
neurolex-v4-creative-name-diffusion is a machine learning model from krystv. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
A domain-specific AI architecture that generates truly creative, novel names for brands, YouTube channels, social media handles, and more — using Uniform Discrete Language Diffusion instead of autoregressive LLMs.
Access
Public
Updated May 30, 2026
Repo size
—
Likes
0
Public
Repo makeup
Click a slice to open those files.
.py67.5 KB · 67%
At a glance
- Access
- Public
- Created
- May 30, 2026
- Updated
- May 30, 2026
- SHA
- 4e9d004a
Model card
From the Hugging Face model README
🧠 NeuroLex v4 — Creative Name Diffusion Engine
A domain-specific AI architecture that generates truly creative, novel names for brands, YouTube channels, social media handles, and more — using Uniform Discrete Language Diffusion instead of autoregressive LLMs.
🚀 Quick Start (Colab)
# Clone and setup
!git clone https://huggingface.co/krystv/neurolex-v4-creative-name-diffusion
%cd neurolex-v4-creative-name-diffusion
!python setup.py # ← IMPORTANT: fixes imports
# Train (~25 minutes on free T4)
!python train.py --size base --epochs 30 --batch_size 256
# Generate names
from neurolex_v4_model import *
checkpoint = torch.load('./checkpoints/neurolex_v4_best.pt')
config = NeuroLexConfig(**checkpoint['config'])
model = NeuroLexV4(config).cuda()
model.load_state_dict(checkpoint['state_dict'])
model.eval()
names = model.generate(
domain_id=DOMAIN_TO_ID['tech'],
style_id=STYLE_TO_ID['sharp'],
lang_id=LANG_TO_ID['english'],
target_length=8, batch_size=20,
cfg_scale=2.5, temperature=0.9,
n_steps=80, odd_alpha=8.0, device='cuda'
)
print(names)
🎯 The Problem We Solve
Why do LLMs and current AI name generators suck at creative naming?
| Problem | Root Cause | Example |
|---|---|---|
| Repetition | AR probability feedback loops | Generates "Nexaflow" 50 times |
| Generic outputs | MLE training → common patterns | "TechFlow", "DataStream", "CloudSync" |
| Mode collapse | Small model memorizes modes | Only 47% uniqueness (v3) |
| Can't invent words | Subword tokenizers recombine known pieces | Just concatenation of morphemes |
| Sounds cringe | No phonotactic awareness | "Xyzptlk", "Blorpify" |
| No cultural sense | Ignores language-specific sound patterns | Same output for Japanese vs French vibe |
✨ Our Solution: Discrete Diffusion (NOT Autoregressive)
LLM/GPT approach (BROKEN):
[Start] → P(next|left) → P(next|left) → ... → same output every time
NeuroLex v4 (WORKS):
[Random Noise] ← denoise ← denoise ← ... ← [Novel Name]
(different noise each time = different output each time)
Key Innovations
| Innovation | What It Does | Based On |
|---|---|---|
| UDLM | Uniform noise → iterative denoising | MDLM (NeurIPS 2024) |
| Classifier-Free Guidance | Control generation without mode collapse | Discrete CFG (2024) |
| ODD | Batch samples actively repel each other | ODD (2025) |
| adaLN | Condition modulates every layer | DiT (2023) |
| Cosine schedule | More refinement time at low noise | DDPM/MDLM |
| Character vocab | Generate truly novel sequences | ByT5 principles |
📊 Specifications
| Property | Value |
|---|---|
| Parameters | ~12M (base) |
| Vocabulary | 72 characters (a-z, A-Z, 0-9, specials) |
| Max name length | 24 characters |
| Languages | 25 |
| Domains | 20 |
| Styles | 10 |
| Training time | ~25 min on free Colab T4 |
| GPU memory | <8 GB |
| Target diversity | 90%+ uniqueness |
📁 Repository Structure
├── neurolex_v4_model.py # Core UDLM architecture (DiT + CFG + ODD)
├── neurolex_v4_dataset.py # Built-in dataset (25 languages, 20 domains)
├── train.py # Training script (CLI)
├── generate.py # Interactive generation script
├── test_model.py # Validation tests
├── setup.py # Run first to fix imports
├── NeuroLex_v4_Training.ipynb # Complete Colab notebook
└── README.md # This file
🔬 Research Foundation
- MDLM — NeurIPS 2024 (arxiv:2406.07524)
- Discrete CFG — (arxiv:2412.10193)
- ODD — (arxiv:2603.04893)
- DiT — Peebles & Xie, 2023
- GFlowNet — NeurIPS 2021 (arxiv:2106.04399)
- Sound Symbolism — (arxiv:2310.16781)
- ByT5 — (arxiv:2105.13626)
- SimCTG — NeurIPS 2022 (arxiv:2202.06417)
📝 License
Apache 2.0
<!-- ml-intern-provenance -->Generated by ML Intern
This model repository was generated by ML Intern, an agent for machine learning research and development on the Hugging Face Hub.
- Try ML Intern: https://smolagents-ml-intern.hf.space
- Source code: https://github.com/huggingface/ml-intern
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "krystv/neurolex-v4-creative-name-diffusion"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
For non-causal architectures, replace AutoModelForCausalLM with the appropriate AutoModel class.
- Created
- May 30, 2026
- Updated
- May 30, 2026