Llama 3.1 405B

Generates coherent multi-turn conversations and long-form content up to 128K tokens using a transformer architecture with extended positional embeddings. Processes entire documents, codebases, or conversation histories in a single forward pass without sliding-window truncation, enabling context-aware responses that reference information from the beginning of the input sequence. Implements rotary position embeddings (RoPE) or similar mechanism to handle the expanded context window while maintaining computational efficiency.

Implements native tool-use capability allowing the model to invoke external functions, APIs, and tools through structured function-calling schemas. The model learns to recognize when a task requires external tool invocation, generates properly-formatted function calls with arguments, and integrates tool outputs into subsequent reasoning steps. Supports schema-based function registry compatible with OpenAI and Anthropic function-calling formats, enabling seamless integration with existing tool ecosystems without custom prompt engineering.

Detects and flags prompt injection attacks using Prompt Guard, a specialized detection model that identifies attempts to override instructions or manipulate model behavior. Analyzes user inputs for suspicious patterns (instruction override attempts, jailbreak techniques, etc.) and flags concerning inputs before processing by the main model. Enables secure deployment by preventing adversarial prompts from reaching the model.

Translates text between supported languages while preserving context, formatting, and technical terminology through transformer-based translation without external translation APIs. The model learns language-specific patterns and maintains semantic equivalence across languages, enabling code-switching and cross-lingual reasoning within single inference pass. Supports translation of code, technical documentation, and domain-specific content with implicit understanding of context.

Distributes 405B model weights openly through Hugging Face and llama.meta.com, enabling on-premises deployment without cloud provider lock-in or API dependencies. Model weights are available in standard formats (safetensors, GGUF quantizations) compatible with multiple inference frameworks. Supports self-hosted inference on private infrastructure, enabling data privacy, cost control, and customization without reliance on external APIs.

Generates fluent, contextually-appropriate text across 8 supported languages using a shared transformer backbone trained on multilingual corpora. The model learns language-specific tokenization, grammar, and cultural context through mixed-language training data, enabling code-switching and cross-lingual reasoning. Language selection is implicit from input context (detected from prompt language) or explicit via system prompts, with no separate language-specific model variants required.

Generates syntactically correct, functionally sound code across multiple programming languages using transformer-based code understanding trained on large code corpora. The model learns language-specific patterns, standard library APIs, and common algorithms, enabling both single-function generation and multi-file code completion. Achieves 89% pass rate on HumanEval benchmark (solving programming problems with correct implementations), indicating strong capability for algorithmic reasoning and API usage.

Solves multi-step mathematical problems and word problems using chain-of-thought reasoning patterns learned during training. The model breaks down complex problems into intermediate steps, performs arithmetic operations, and validates results through logical reasoning. Achieves 96.8% accuracy on GSM8K benchmark (grade-school math word problems), indicating strong capability for arithmetic, algebra, and problem decomposition without external calculators.

Answers factual questions across diverse domains (science, history, law, medicine, etc.) using knowledge learned during pretraining on 15+ trillion tokens. The model retrieves relevant knowledge from its parameters and generates contextually appropriate answers without external knowledge bases. Achieves 88.6% accuracy on MMLU benchmark (multiple-choice questions across 57 subjects), indicating broad knowledge coverage and strong performance on knowledge-intensive tasks.

Generates high-quality synthetic training data for fine-tuning smaller models, data augmentation, and model distillation workflows. The 405B model produces diverse, contextually-appropriate examples across domains, enabling creation of task-specific datasets without manual annotation. Supports generation of instruction-response pairs, code examples, mathematical problems, and domain-specific content at scale, facilitating training of smaller, more efficient models that inherit capabilities from the larger teacher model.

Executes 405B parameter model inference across multiple GPUs using tensor parallelism and pipeline parallelism to distribute computation and memory. Implements KV cache optimization to reduce memory footprint during long sequences, enabling efficient inference despite massive model size. Requires specialized inference frameworks (vLLM, TensorRT-LLM, or similar) that handle GPU communication, load balancing, and memory management automatically.

Follows natural language instructions and adapts behavior based on prompt context without fine-tuning, using learned instruction-following patterns from training. The model interprets system prompts, role definitions, and task specifications to modify output style, format, and content. Supports few-shot learning (learning from examples in context) and zero-shot task adaptation, enabling flexible use across diverse applications without model retraining.

Filters unsafe content and enforces safety policies using Llama Guard 3, a specialized safety classifier model released alongside 405B. Detects harmful content categories (violence, illegal activity, sexual content, etc.) in both user inputs and model outputs, enabling content moderation workflows. Integrates with 405B inference pipeline to block unsafe generations or flag concerning inputs before processing.