NightmareNet
Autonomous AI Self-Improvement through
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Force neural networks to learn invariant structures — not memorize patterns.
250+
Tests Passing
4
Training Phases
7
API Endpoints
< 50ms
Avg Latency
See It In Action
Watch your text transform through dream and nightmare distortions in 3 steps.
4-Phase Training Pipeline
Each cycle builds robust invariant knowledge that survives even the harshest perturbations.
Wake
Train on clean data to establish baseline competence and anchor model weights.
Dream
Soft perturbations stretch learned representations while preserving core structure.
Nightmare
Aggressive adversarial distortions test the model's invariant knowledge.
Compress
Distill robust representations into a tighter model via knowledge distillation.
Dream Distortion
Synonym swaps, paraphrase, soft noise
Nightmare Distortion
Adversarial attacks, char-swaps, deletions
Compression
Knowledge distillation, magnitude pruning
3 Lines to Robustness
Add NightmareNet to your training pipeline in minutes, not days.
git clone https://github.com/Adit-Jain-srm/NightmareNet.git
cd NightmareNet
pip install -e ".[api]"Wake
Train on clean data
~15 min
Dream
Gentle distortions
~10 min
Nightmare
Adversarial stress test
~8 min
Compress
Prune & distill
~5 min
Distortion Playground
Feed text through the distortion pipeline and watch it transform in real-time.
🌀 Token drops, paraphrasing — moderate restructuring
Resilience Lab
Measure how text degrades under distortion. A resilience score of 85% means your text retains 85% of its structure under adversarial pressure.
Training Lab
Configure training hyperparameters and preview the full phase schedule before launch.
Configure & Preview
Adjust sliders and click preview to see the training schedule.
Train a Hardened Model
Bring your own data → select a model → configure the sleep cycle → get a production-ready model with before/after robustness metrics.
Upload Data
Feed your text data into the distortion pipeline. Supports .txt, .csv, and .json.
Drag & drop or click to upload
.txt, .csv, .json • Max 5MB
Model Explorer
Inspect the transformer architecture that NightmareNet trains, distorts, and compresses.
Training Capabilities
Mixed Precision
AMP + gradient checkpointing for 2× speed
Streaming Data
Process datasets larger than memory
Distributed
Multi-GPU via HuggingFace Accelerate
Multi-Arch
GPT-2, BERT, DistilBERT, and more
Default Model
GPT-2
AutoModelForCausalLM • 124M params • wikitext-2
System Status
Real-time endpoint health, latency monitoring, and API diagnostics.
Checking...
Waiting...
Endpoint Health Matrix
Health
GET /api/v1/health
Dream
POST /api/v1/generate/dream
Nightmare
POST /api/v1/generate/nightmare
Robustness
POST /api/v1/evaluate/robustness
Compare
POST /api/v1/compare
Train Config
POST /api/v1/train/config
Upload
POST /api/v1/upload/text