Technical Specifications
Performance
*Performance metrics measured on server infrastructure. Latency includes network round-trip and varies by location/connection quality. Benchmark scores are projected estimates based on model architecture and training approach.
Training & Architecture
Training Infrastructure
Trained on distributed GPU clusters over several months. Mixed-precision training with BF16 and FP32 accumulation. Global batch size of 4M tokens optimized for efficiency.
Data Composition
2.1 trillion tokens from diverse sources: web text (45%), books (15%), academic papers (12%), code repositories (18%), and curated instruction data (10%). All data filtered for quality and deduplicated.
Novel Architecture
Grouped Query Attention (GQA) with 8 KV heads for efficient inference. Rotary Position Embeddings (RoPE) for extended context. SwiGLU activation and RMSNorm for stability. Optimized for server deployment with mixed-precision training.
Optimization
AdamW optimizer with β₁=0.9, β₂=0.95. Cosine learning rate schedule from 3e-4 to 3e-5. Weight decay 0.1. Gradient clipping at 1.0. Flash Attention 2 for training efficiency.
Zero Alignment Tax
Unlike traditional models, NL 1.0 has minimal post-training alignment. No extensive RLHF or constitutional AI training. No heavy refusal training. Focus on raw capability with lightweight instruction tuning for coherence.
Privacy Architecture
Zero data retention by design. RAM-only processing, immediate auto-deletion, cryptographic memory wiping. Read-only filesystems prevent logging even if compromised. Network routing data never logged or stored. Your queries are ephemeral - forensically unrecoverable.
Server Infrastructure
Deployed on NVIDIA A100/H100 GPU clusters. High-availability architecture with load balancing. Consistent performance for all users regardless of local hardware. Automated server restarts every 6 hours to guarantee memory wiping.
Core Capabilities
Designed for real-world tasks without artificial limitations
Language Understanding
- 16K token context window with full attention
- Multi-turn dialogue with conversation history
- Intent classification and entity extraction
- Sentiment analysis across major languages
Code & Development
- Code generation in 40+ programming languages
- Real-time debugging and error correction
- API documentation and technical writing
Mathematical Reasoning
- Step-by-step problem solving
- Statistical analysis
- Algorithm optimization
Content Generation
- Long-form content with consistent tone
- Technical documentation and whitepapers
- Structured data transformation (JSON, XML, CSV)
Analysis & Research
- Document analysis
- Scientific paper summarization
- Hypothesis generation
Multilingual
- Translation across major language pairs
- Cross-lingual information retrieval
- Cultural context adaptation
What Makes NL 1.0 Different
Zero Content Filtering
No refusal training. No hardcoded blocklists. No moral grandstanding. The model responds to all prompts based on capability, not arbitrary restrictions. You decide what's appropriate for your use case.
Efficient 16K Context
Full attention over 16K tokens without sliding windows or truncation tricks. Validated on needle-in-haystack benchmarks with >95% retrieval accuracy. Optimized server infrastructure ensures fast processing and consistent performance.
Reproducible Outputs
Deterministic generation with temperature=0. Seed-based randomness for controlled variation. No hidden prompt augmentation or behind-the-scenes modifications. What you request is exactly what you get.
Client-Server Architecture
Lightweight desktop client (~250MB) connects to high-performance server infrastructure. Zero data retention with RAM-only processing. Automated server restarts every 6 hours ensure cryptographic memory wiping. Privacy-first design with no logs, no data storage.