Nous: Hermes 3 70B Instruct

Hermes 3 70B maintains semantic coherence across extended multi-turn conversations through optimized attention mechanisms and training on long-context datasets, enabling it to track conversation state, reference earlier turns accurately, and resolve pronouns/references across 10+ exchanges without context collapse. The model uses Llama 3.1's grouped-query attention (GQA) architecture to reduce KV cache memory while preserving long-range dependencies, allowing it to handle conversations that would cause context drift in smaller models.

Hermes 3 70B is trained to generate structured function calls in response to tool-use prompts, enabling it to invoke external APIs, execute code, or trigger workflows by outputting properly-formatted JSON or XML function signatures. The model learns to reason about which tools to invoke, in what order, and with what parameters through instruction-tuning on synthetic agentic datasets, allowing it to decompose complex tasks into tool-calling sequences without requiring explicit prompt engineering for each tool.

Hermes 3 70B can be used as a semantic understanding layer to rank the relevance of documents or passages to a query by understanding semantic similarity and contextual relevance, enabling it to identify the most relevant information from a knowledge base without requiring explicit vector embeddings. The model learns to understand query intent and match it against document content based on meaning rather than keyword matching, enabling more intelligent search and retrieval.

Hermes 3 70B maintains consistent character personas, voice, and behavioral patterns across extended interactions through instruction-tuning on roleplay datasets and character-consistency examples. The model learns to internalize character traits, speech patterns, and knowledge domains, allowing it to stay in-character while responding contextually to user inputs without breaking character or contradicting established persona attributes.

Hermes 3 70B is trained to generate explicit reasoning chains where it breaks down complex problems into intermediate steps, showing its work before arriving at conclusions. The model learns to use natural language reasoning tokens (e.g., 'Let me think through this step by step...') and structured formats to decompose problems, enabling more reliable multi-step reasoning and making its decision-making process interpretable to users and downstream systems.

Hermes 3 70B generates syntactically correct code across 40+ programming languages (Python, JavaScript, Java, C++, Go, Rust, etc.) through training on diverse code repositories and instruction-tuning on code-generation tasks. The model understands language-specific idioms, libraries, and best practices, allowing it to generate production-ready code snippets, complete partial implementations, and suggest refactorings with language-aware context awareness.

Hermes 3 70B is trained to follow detailed, multi-part instructions with high fidelity, parsing complex task specifications and executing them accurately even when instructions contain multiple constraints, conditional logic, or nested requirements. The model learns to clarify ambiguous instructions, ask for missing information, and decompose complex tasks into sub-steps, enabling it to handle real-world task specifications that aren't perfectly formatted.

Hermes 3 70B synthesizes information from multiple sources or long documents into coherent summaries while preserving key context, nuance, and important details. The model learns to identify salient information, abstract away redundancy, and maintain semantic relationships between concepts, enabling it to create summaries at various granularities (bullet points, paragraphs, abstracts) without losing critical information.

Hermes 3 70B generates original creative content (stories, poetry, marketing copy, dialogue) while maintaining consistent tone, style, and voice through instruction-tuning on diverse writing datasets. The model learns to adapt its writing style to match specified genres, audiences, or tones (formal, casual, humorous, etc.), enabling it to generate contextually appropriate content that aligns with user intent and brand voice.

Hermes 3 70B answers questions based on provided context or its training knowledge while optionally attributing answers to specific sources and expressing uncertainty about answers it's less confident in. The model learns to distinguish between high-confidence factual answers and speculative responses, enabling it to provide nuanced answers that acknowledge knowledge gaps or ambiguity rather than hallucinating confident but incorrect answers.

Hermes 3 70B translates text between 50+ languages while preserving meaning, tone, and cultural context through training on multilingual corpora and instruction-tuning on translation tasks. The model understands language-specific idioms, grammar structures, and cultural references, enabling it to produce natural translations rather than literal word-for-word conversions, and can also answer questions or perform tasks in non-English languages.