Llama 2

Llama 2 implements a transformer-based architecture with rotary position embeddings (RoPE) and grouped query attention (GQA) to maintain coherent multi-turn conversations while managing context windows up to 4,096 tokens. The model uses causal self-attention masking to prevent attending to future tokens, enabling sequential token generation with awareness of conversation history. Context is retained in-memory during inference without explicit retrieval mechanisms, allowing natural dialogue flow across multiple exchanges.

Llama 2 was trained using supervised fine-tuning (SFT) on high-quality instruction-response pairs, followed by reinforcement learning from human feedback (RLHF) using a reward model trained on human preference annotations. This two-stage alignment process teaches the model to follow user instructions accurately while avoiding harmful outputs. The model learns to parse structured instructions, understand intent, and generate appropriate responses across diverse task categories without explicit task-specific training.

Llama 2 includes built-in safety mechanisms trained through RLHF to refuse harmful requests and avoid generating dangerous content. The model learned to recognize and decline requests for illegal activities, violence, hate speech, and other harmful outputs. Additionally, Meta provides safety classifiers that can be applied at inference time to detect and filter harmful outputs before they reach users. These mechanisms are probabilistic and imperfect but provide a baseline defense against misuse.

Llama 2 can process multiple requests in parallel through batch inference, where multiple prompts are processed together in a single forward pass. Batching improves GPU utilization and throughput by amortizing computation overhead across multiple requests. Inference frameworks like vLLM implement continuous batching, where new requests are added to batches as they arrive, maximizing throughput without requiring all requests to be available upfront. This enables high-throughput serving on limited hardware.

While Llama 2 is primarily a text model, it can reason about code and technical content by processing them as text. The model can analyze code snippets, generate code, and explain technical concepts by leveraging patterns learned during pre-training on code repositories and technical documentation. This enables integration of code understanding into broader reasoning tasks, though without explicit visual or multi-modal capabilities. The model treats code as structured text and learns to recognize patterns in syntax and semantics.

Llama 2 was trained on diverse code repositories and technical documentation, enabling it to generate syntactically correct code snippets, complete partial implementations, and reason about programming problems. The model uses standard transformer attention to understand code structure and context, generating code in multiple languages (Python, JavaScript, C++, SQL, etc.) with awareness of common patterns and libraries. Code generation leverages the same token prediction mechanism as text generation, with no specialized code-specific architecture.

Llama 2 uses transformer self-attention mechanisms to build rich semantic representations of input text, enabling it to understand relationships between concepts, perform logical reasoning, and answer questions requiring multi-step inference. The model learns to identify entities, relationships, and implicit information through attention patterns developed during pre-training on diverse text. This capability emerges from scale and training data diversity rather than explicit reasoning modules, allowing the model to handle reasoning tasks across scientific, mathematical, legal, and creative domains.

Llama 2 was trained on text in multiple languages (English, Spanish, French, German, Italian, Portuguese, Dutch, Russian, Chinese, Japanese, Korean, and others), enabling it to generate coherent text and understand content across language boundaries. The model uses a shared vocabulary and transformer architecture without language-specific modules, learning to map different languages to shared semantic representations. This enables cross-lingual transfer where understanding of concepts in one language can inform generation in another.

Llama 2 can process documents up to 4,096 tokens in length using its full attention mechanism, enabling it to analyze, summarize, and extract information from longer texts without chunking. The model uses causal self-attention to understand relationships across the entire document, building a unified representation that captures both local details and global structure. Summarization emerges from the model's ability to identify salient information and generate condensed representations in natural language.

Llama 2 can adapt its behavior to new tasks by including examples in the prompt (few-shot learning), without requiring fine-tuning or retraining. The model uses attention mechanisms to recognize patterns in provided examples and apply those patterns to new inputs, effectively learning task-specific behavior from context alone. This capability enables rapid prototyping and task switching without model updates, though performance depends on example quality and task similarity to training data.

Llama 2 can be constrained to generate output in specific formats (JSON, XML, CSV, code blocks, etc.) through prompt engineering and inference-time constraints. While the model has no native structured output mechanism, careful prompting and post-processing can enforce format compliance. Some inference frameworks (vLLM, llama.cpp) support grammar-based constraints that restrict token generation to valid format sequences, enabling reliable structured output without additional models.

Llama 2 supports multiple inference optimization techniques including 8-bit and 4-bit quantization, which reduce model size and memory requirements while maintaining reasonable quality. Quantization maps floating-point weights to lower-precision integers, reducing VRAM usage by 4-8x and enabling deployment on consumer hardware. Inference frameworks like llama.cpp, vLLM, and Ollama implement these optimizations transparently, allowing developers to run large models on limited hardware without code changes.

Llama 2 can be fine-tuned on custom datasets to adapt the model for specific domains, tasks, or styles. Fine-tuning updates model weights using supervised learning on task-specific examples, enabling the model to learn domain-specific patterns and terminology. Techniques like LoRA (Low-Rank Adaptation) enable efficient fine-tuning by training only small adapter modules rather than all model weights, reducing memory requirements and training time. Fine-tuning requires GPU resources and expertise but enables significant quality improvements for specialized applications.