Nous: Hermes 4 70B

Dynamically switches between fast-inference and extended-reasoning modes during generation, allowing the model to allocate computational budget based on query complexity. The model learns to route simple queries through direct generation paths while complex reasoning tasks trigger iterative chain-of-thought processing, implemented via a learned gating mechanism that predicts reasoning necessity before token generation begins.

Generates multi-step reasoning chains with explicit intermediate steps, leveraging the 70B parameter scale to maintain coherence across long reasoning sequences. When activated, the model produces verbose step-by-step explanations with intermediate conclusions, implemented via training on synthetic reasoning datasets and reinforced through process-reward modeling to prefer logically sound intermediate steps.

Answers factual and reasoning-based questions by retrieving relevant knowledge and applying logical deduction. The model combines pattern matching from training data with reasoning chains to synthesize answers, particularly effective when questions require multi-step inference or combining information from multiple domains.

Analyzes sentiment and extracts opinions from text, classifying emotional tone and identifying specific viewpoints or attitudes. The model recognizes sentiment markers (words, phrases, context) and generates structured sentiment labels (positive/negative/neutral) with confidence scores and supporting evidence.

Identifies and extracts named entities (people, organizations, locations, dates, etc.) from text, classifying them into semantic categories. The model recognizes entity boundaries and types through learned patterns from training data, generating structured output with entity spans and classifications.

Identifies potentially harmful, inappropriate, or policy-violating content including hate speech, violence, adult content, and misinformation. The model applies learned safety patterns to classify content risk levels and flag problematic material, implemented through instruction-tuning on safety datasets and reinforcement learning from human feedback on safety preferences.

Executes complex multi-part instructions with precise output formatting, using instruction-tuning techniques to reliably parse structured prompts and generate outputs matching specified schemas. The model was trained on diverse instruction datasets with explicit format specifications, enabling it to follow JSON schemas, XML structures, markdown formatting, and code block requirements with high consistency.

Generates syntactically correct code across 20+ programming languages and performs refactoring tasks like optimization, style conversion, and bug fixing. Built on Llama 3.1's code training, enhanced with instruction-tuning for code-specific tasks, the model maintains language-specific idioms and best practices through learned patterns from diverse codebases.

Solves mathematical problems ranging from algebra to calculus by generating step-by-step solutions with intermediate calculations. The model uses symbolic reasoning patterns learned from mathematical datasets, showing work through explicit equation manipulation and logical deduction steps rather than direct answer generation.

Maintains coherent multi-turn conversations by tracking conversation history and building context across exchanges. The model uses standard transformer attention mechanisms to weight recent messages more heavily while retaining key facts from earlier turns, implemented through careful prompt formatting that preserves conversation structure within the context window.

Generates structured function calls in JSON format to invoke external tools and APIs, parsing natural language requests into executable tool invocations. The model learns to map user intents to appropriate functions by recognizing function signatures provided in the prompt, generating valid JSON that downstream systems can parse and execute.

Condenses long documents, articles, or conversations into concise summaries while preserving key information. The model learns to identify salient facts and main ideas through training on summarization datasets, generating summaries at configurable length (bullet points, paragraphs, or single-sentence abstracts) while maintaining factual accuracy.

Translates text between 50+ languages and generates content in non-English languages with cultural and linguistic appropriateness. Built on Llama 3.1's multilingual training, the model maintains semantic meaning across language boundaries and adapts tone/formality to target language conventions.

Generates original creative content including stories, poetry, marketing copy, and dialogue with stylistic consistency and narrative coherence. The model learns creative writing patterns from diverse text corpora, generating content that maintains tone, voice, and thematic consistency across extended passages.