Meta Llama 3.1

Llama 3.1 405B Instruct is an instruction-tuned dense transformer large language model designed for advanced reasoning, multilingual dialogue, and large-scale AI applications. Built on a 405B parameter architecture, it features 126 transformer layers, 128 attention heads, and a 16,384 hidden size, utilizing Grouped Query Attention (GQA) for efficient large-scale inference. The model supports up to a 128K token context window with RoPE scaling for long-context understanding. Fine-tuned using supervised learning and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.

Llama 3.1 405B is a large-scale multilingual dense transformer language model designed for advanced reasoning, large-context processing, and enterprise AI workloads. The model features a 405B parameter architecture with 126 transformer layers, 128 attention heads, and a 16,384 hidden size, leveraging Grouped Query Attention (GQA) for efficient large-scale inference. It supports up to a 128K token context window with RoPE scaling for extended context understanding. Optimized for multilingual text and code generation, it is designed for research, large-scale AI systems, and high-performance LLM deployments.

Llama 3.1 70B Instruct is an instruction-tuned dense transformer large language model optimized for multilingual dialogue, reasoning, and assistant-style AI applications. Built on a 70B parameter architecture, it features 80 transformer layers, 64 attention heads, and an 8,192 hidden size, leveraging Grouped Query Attention (GQA) for efficient large-scale inference. The model supports up to a 128K token context window with RoPE scaling for long-context understanding. Fine-tuned using supervised learning and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.

Llama 3.1 70B is a multilingual dense transformer large language model designed for advanced text generation, reasoning, and large-scale AI applications. The model features a 70B parameter architecture with 80 transformer layers, 64 attention heads, and an 8,192 hidden size, utilizing Grouped Query Attention (GQA) for scalable and efficient inference. It supports up to a 128K token context window with RoPE scaling for long-context understanding. Optimized for multilingual text and code generation, it is widely used for research, conversational AI, and enterprise-grade LLM deployments.

Llama 3.1 8B Instruct is an instruction-tuned dense transformer large language model optimized for multilingual conversational AI and assistant-style applications. Built on an 8B parameter architecture, it features 32 transformer layers, 32 attention heads, and a 4,096 hidden size, leveraging Grouped Query Attention (GQA) for efficient inference. The model supports up to a 128K token context window with advanced RoPE scaling for long-context processing. Fine-tuned using supervised learning and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.

Llama 3.1 8B is a multilingual dense transformer large language model designed for scalable text generation, reasoning, and dialogue applications. The model features an 8B parameter architecture with 32 transformer layers, 32 attention heads, and a 4,096 hidden size, utilizing Grouped Query Attention (GQA) for efficient inference. It supports up to a 128K token context window with advanced RoPE scaling for long-context processing. Optimized for multilingual text and code generation, it is widely used for conversational AI, developer tools, and production-grade LLM deployments.