Llama (Large Language Model Meta AI)
The premier open-weight ecosystem for sovereign, scalable AI development.
Meta's foundational open-weights model for high-performance generative AI and fine-tuning.
LLaMA 2, developed by Meta AI, is a generation of large language models ranging from 7 billion to 70 billion parameters. Built upon the transformer architecture, it introduced significant improvements over its predecessor, including Grouped-Query Attention (GQA) for the 70B model to enhance inference efficiency and a doubling of the context length to 4,096 tokens. In the 2026 landscape, LLaMA 2 remains a critical benchmark for enterprise-grade, self-hosted deployments where data privacy is paramount. It was pre-trained on 2 trillion tokens and fine-tuned using Reinforcement Learning from Human Feedback (RLHF), specifically optimized for dialogue through its 'Llama-2-chat' variants. While newer iterations like Llama 3 and 4 have since been released, LLaMA 2 maintains a significant market share due to its stability, extensive documentation, and lighter computational footprint for edge-computing applications. It serves as the primary backbone for businesses requiring local LLM orchestration without the latency or privacy risks associated with proprietary cloud APIs. Its licensing model remains a standard-bearer for 'open-ish' AI, permitting commercial use for entities with fewer than 700 million monthly active users, making it the preferred choice for startups and mid-market enterprises looking to escape vendor lock-in.
A method to accelerate inference by sharing key and value projections across multiple heads.
The premier open-weight ecosystem for sovereign, scalable AI development.
Smarter, Faster, and Cost-Efficient Reasoning Models for the Global AI Frontier.
The first truly open-source LLM stack for reproducible AI research and enterprise transparency.
Open-access large language models designed for transparent and reproducible AI research.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
A fine-tuning technique that helps the model remember system-level instructions across long conversations.
Implementation of relative position encoding using a rotation matrix.
Fine-tuned using 1 million binary comparisons by human annotators.
Replacement of standard ReLU with SwiGLU for the feed-forward network layers.
Root Mean Square Layer Normalization applied before each transformer sub-layer.
Native compatibility with quantization frameworks like AutoGPTQ and bitsandbytes.
Securely querying sensitive internal documents without sending data to third-party cloud providers.
Registry Updated:2/7/2026
Automating 24/7 chat support while maintaining a specific brand voice.
Extracting clinical insights from long-form doctor notes with high privacy standards.