Technical Specifications
Performance
*Performance metrics measured on small prompts (<2K tokens). Larger contexts increase latency proportionally.
Training & Architecture
Training Infrastructure
Trained on a cluster of 512 NVIDIA A100 GPUs over 6 weeks. Mixed-precision training with BF16 and FP32 accumulation. Global batch size of 4M tokens optimized for efficiency.
Data Composition
2.5 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 consumer GPUs with INT8 quantization.
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.
Inference Optimization
Optimized for RTX 4060 Ti (16GB) and RTX 3060 (12GB) with INT8 quantization. Flash Attention 2 for speed. KV cache optimization for memory efficiency. Runs on mid-range consumer GPUs without CPU offloading.
Core Capabilities
Designed for real-world tasks without artificial limitations
Language Understanding
- 8K token context window with full attention
- Multi-turn dialogue with conversation history
- Intent classification and entity extraction
- Sentiment analysis across 15 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 15 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 8K Context
Full attention over 8K tokens without sliding windows or truncation tricks. Validated on needle-in-haystack benchmarks with >95% retrieval accuracy. Optimized for speed and memory efficiency on consumer hardware with INT8 quantization.
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.
Runs on Your Hardware
Designed for RTX 4060 Ti (16GB) and RTX 3060 (12GB) with INT8 quantization. Complete model weights available for download. True ownership - runs entirely on your machine, no cloud dependency. No data leaves your system.