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---
tags:
- ml-intern
---
# 🧠 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)

```python
# 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

1. **MDLM** — NeurIPS 2024 ([arxiv:2406.07524](https://arxiv.org/abs/2406.07524))
2. **Discrete CFG** — ([arxiv:2412.10193](https://arxiv.org/abs/2412.10193))
3. **ODD** — ([arxiv:2603.04893](https://arxiv.org/abs/2603.04893))
4. **DiT** — Peebles & Xie, 2023
5. **GFlowNet** — NeurIPS 2021 ([arxiv:2106.04399](https://arxiv.org/abs/2106.04399))
6. **Sound Symbolism** — ([arxiv:2310.16781](https://arxiv.org/abs/2310.16781))
7. **ByT5** — ([arxiv:2105.13626](https://arxiv.org/abs/2105.13626))
8. **SimCTG** — NeurIPS 2022 ([arxiv:2202.06417](https://arxiv.org/abs/2202.06417))

## 📝 License

Apache 2.0

<!-- ml-intern-provenance -->
## Generated by ML Intern

This model repository was generated by [ML Intern](https://github.com/huggingface/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

```python
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.