{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-ibm-granite-granite-4.0-h-micro","slug":"ibm-granite-granite-4.0-h-micro","name":"IBM: Granite 4.0 Micro","type":"model","url":"https://openrouter.ai/models/ibm-granite~granite-4.0-h-micro","page_url":"https://unfragile.ai/ibm-granite-granite-4.0-h-micro","categories":["chatbots-assistants"],"tags":["ibm-granite","api-access","text"],"pricing":{"model":"paid","free":false,"starting_price":"$1.70e-8 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-ibm-granite-granite-4.0-h-micro__cap_0","uri":"capability://text.generation.language.lightweight.text.generation.with.long.context","name":"lightweight-text-generation-with-long-context","description":"Generates coherent text responses using a 3B parameter transformer architecture optimized for inference efficiency on resource-constrained environments. The model employs standard causal language modeling with attention mechanisms fine-tuned to handle extended context windows, enabling multi-turn conversations and document-aware responses without requiring GPU acceleration for deployment.","intents":["I need a small language model that can run on edge devices or embedded systems without high computational overhead","I want to build a chatbot that maintains conversation history across multiple turns without token limits becoming prohibitive","I need to process and respond to long documents or code files within a single inference pass"],"best_for":["embedded systems and IoT developers building on-device AI","teams deploying models in resource-constrained cloud environments to reduce inference costs","organizations requiring model deployment without GPU infrastructure"],"limitations":["3B parameter size limits reasoning depth and factual accuracy compared to 7B+ models; may struggle with complex multi-step logical tasks","Fine-tuning specifics for long-context handling are proprietary; exact context window length not publicly documented","Inference latency on CPU-only systems will be significantly higher than quantized smaller models or GPU-accelerated inference","No built-in retrieval-augmented generation (RAG) integration; requires external vector database and retrieval pipeline for knowledge grounding"],"requires":["API key for OpenRouter or direct IBM Granite API access","HTTP/REST client library for API calls","Minimum 2GB RAM for local deployment if self-hosted","Network connectivity for cloud-based inference via OpenRouter"],"input_types":["text (plain text, markdown, code snippets)","multi-turn conversation history as concatenated text"],"output_types":["text (natural language responses)","code snippets (if prompted with code context)","structured text (JSON, YAML if explicitly formatted in prompt)"],"categories":["text-generation-language","edge-deployment"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-ibm-granite-granite-4.0-h-micro__cap_1","uri":"capability://text.generation.language.multi.turn.conversation.state.management","name":"multi-turn-conversation-state-management","description":"Maintains coherent dialogue across multiple exchanges by processing concatenated conversation history as context in each inference call. The model uses standard transformer attention to track speaker roles, intent shifts, and contextual references across turns, enabling stateless conversation management where the full history is resubmitted with each new user message.","intents":["I want to build a chatbot that remembers previous messages and maintains conversation context without external state storage","I need to implement a conversational AI that can reference earlier parts of the dialogue and correct misunderstandings","I want to create a multi-turn Q&A system where follow-up questions are answered in context of prior exchanges"],"best_for":["developers building stateless chatbot APIs where conversation history is managed client-side","teams implementing conversational interfaces with simple context requirements (5-20 turn conversations)","prototyping conversational AI without implementing external session/memory databases"],"limitations":["Stateless design requires resubmitting full conversation history with each turn, increasing token consumption and latency linearly with conversation length","No built-in conversation summarization; conversations longer than the context window will lose early context without explicit summarization logic","Attention mechanism may dilute focus on recent messages when conversation history exceeds ~4000 tokens; no recency bias optimization documented","No native support for multi-user conversations or conversation branching; requires application-level logic to manage parallel dialogue threads"],"requires":["API key for OpenRouter","Client-side conversation history management (array of {role, content} objects)","Token counting library to track cumulative conversation length against context window","HTTP client for API calls with support for streaming or polling"],"input_types":["text (user messages)","conversation history (array of turn objects with role and content)"],"output_types":["text (assistant response)","streaming text chunks (if streaming API is used)"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-ibm-granite-granite-4.0-h-micro__cap_2","uri":"capability://code.generation.editing.code.understanding.and.generation","name":"code-understanding-and-generation","description":"Generates and analyzes code across multiple programming languages by leveraging transformer attention over tokenized source code, with fine-tuning on technical documentation and code repositories. The model can complete code snippets, explain code logic, and generate code from natural language descriptions, using standard causal language modeling without specialized AST parsing or syntax-aware tokenization.","intents":["I want to generate boilerplate code or code snippets from natural language descriptions","I need a lightweight code assistant that can explain code logic and suggest improvements without GPU requirements","I want to build a code completion tool for embedded development or IoT projects where model size is constrained"],"best_for":["developers building code generation features in resource-constrained environments","teams needing lightweight code assistance for documentation generation or code review","embedded systems developers requiring on-device code completion without cloud dependency"],"limitations":["3B parameter size limits ability to understand complex multi-file codebases or deeply nested logic; struggles with context-dependent refactoring across files","No built-in syntax validation or compilation checking; generated code may have syntax errors requiring post-processing","No specialized code tokenization (e.g., tree-sitter AST parsing); treats code as plain text, reducing structural awareness compared to code-specific models","Limited to languages present in training data; performance degrades for niche or domain-specific languages","No IDE integration or real-time completion; API-based inference introduces latency unsuitable for keystroke-level completions"],"requires":["API key for OpenRouter","Code context as text input (file contents, snippets, or function definitions)","Language specification in prompt or context for better code generation","Optional: syntax validator or linter for post-processing generated code"],"input_types":["text (natural language code requests)","code (source code snippets for analysis or completion)","mixed (code with inline comments or documentation)"],"output_types":["code (generated source code)","text (code explanations or documentation)","structured text (code comments or docstrings)"],"categories":["code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-ibm-granite-granite-4.0-h-micro__cap_3","uri":"capability://text.generation.language.instruction.following.with.system.prompts","name":"instruction-following-with-system-prompts","description":"Executes user instructions by conditioning generation on system prompts that define behavior, tone, and task constraints. The model uses standard prompt engineering patterns where system instructions are prepended to user input, allowing dynamic role-playing, task specialization, and output format control through text-based configuration without model fine-tuning.","intents":["I want to create a specialized chatbot persona (e.g., technical support agent, creative writer) by defining system instructions","I need to enforce output format constraints (JSON, markdown, code blocks) through prompting rather than post-processing","I want to build a multi-purpose assistant that adapts behavior based on user-provided instructions or context"],"best_for":["developers building prompt-based AI applications without fine-tuning infrastructure","teams prototyping specialized assistants with different personas or behaviors","builders creating flexible AI tools where behavior is configured via prompts rather than model weights"],"limitations":["Instruction-following quality degrades with complex or conflicting instructions; no built-in conflict resolution or instruction prioritization","System prompts increase token consumption, reducing effective context window for user input and conversation history","No guarantee of instruction adherence; model may ignore or partially follow instructions, especially for edge cases or adversarial prompts","Prompt injection vulnerabilities: user input can override system instructions if not properly escaped or validated","Fine-tuning for instruction-following is limited compared to models specifically trained on instruction datasets; performance varies by instruction complexity"],"requires":["API key for OpenRouter","Well-crafted system prompt defining desired behavior and constraints","Input validation or prompt escaping to prevent instruction injection","Optional: prompt testing framework to validate instruction adherence across test cases"],"input_types":["text (system prompt defining behavior)","text (user instruction or query)"],"output_types":["text (response following system prompt constraints)","structured text (JSON, YAML, markdown if specified in system prompt)","code (if system prompt requests code generation)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-ibm-granite-granite-4.0-h-micro__cap_4","uri":"capability://tool.use.integration.api.based.inference.with.streaming","name":"api-based-inference-with-streaming","description":"Provides text generation through OpenRouter's REST API with support for streaming responses via server-sent events (SSE) or polling. Requests are formatted as JSON payloads containing model parameters (temperature, max_tokens, top_p) and conversation history, with responses streamed token-by-token or returned in full, enabling real-time user feedback and progressive output rendering.","intents":["I want to integrate a language model into my application without managing infrastructure or GPU resources","I need streaming responses to display text generation in real-time as it's produced","I want to use a language model API with flexible pricing and provider switching via OpenRouter"],"best_for":["web and mobile developers building AI features without backend ML infrastructure","teams using OpenRouter for provider abstraction and cost optimization","builders prototyping AI applications quickly without model deployment complexity"],"limitations":["API latency adds 100-500ms overhead per request compared to local inference; unsuitable for sub-100ms response requirements","Streaming responses require persistent HTTP connections; some network environments (proxies, firewalls) may block or timeout long-lived connections","Rate limiting and quota enforcement by OpenRouter; high-volume applications may hit rate limits or require premium tier","No local caching of model weights; every request incurs full inference cost, no amortization for repeated queries","Dependency on OpenRouter availability and uptime; no fallback to local inference if API is unavailable"],"requires":["OpenRouter API key (free tier available with usage limits)","HTTP client library supporting streaming (fetch API, axios, requests, etc.)","Network connectivity to OpenRouter endpoints","JSON serialization/deserialization for request/response handling"],"input_types":["JSON (API request with model parameters, messages, system prompt)"],"output_types":["JSON (full response with usage statistics)","streaming text (SSE format with token-by-token output)"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-ibm-granite-granite-4.0-h-micro__cap_5","uri":"capability://text.generation.language.temperature.and.sampling.parameter.control","name":"temperature-and-sampling-parameter-control","description":"Modulates output randomness and diversity through temperature, top_p (nucleus sampling), and top_k parameters passed to the API. Lower temperatures (0.1-0.3) produce deterministic, focused outputs suitable for factual tasks; higher temperatures (0.7-1.0) increase creativity and diversity for generative tasks. The model applies these parameters during token sampling, affecting probability distribution over vocabulary without retraining.","intents":["I want to generate deterministic, consistent responses for factual queries or code generation","I need to increase output diversity for creative writing or brainstorming tasks","I want to fine-tune the randomness of model outputs to match my application's requirements"],"best_for":["developers building applications requiring tunable output characteristics (chatbots, creative tools, technical assistants)","teams experimenting with model behavior without fine-tuning or retraining","builders optimizing for specific use cases (deterministic for Q&A, creative for content generation)"],"limitations":["Parameter tuning is empirical and task-dependent; no principled method to select optimal values without testing","Temperature values below 0.1 may produce repetitive or degenerate outputs; values above 1.0 increase incoherence","Nucleus sampling (top_p) and temperature interact in complex ways; simultaneous tuning of both requires careful experimentation","No built-in validation or warnings for parameter combinations that may produce poor outputs","Parameter effects vary by prompt and task; settings optimized for one task may not transfer to others"],"requires":["OpenRouter API key","Understanding of temperature and sampling parameters (documentation or ML background)","Ability to test and evaluate outputs for quality and coherence","Optional: A/B testing framework to compare outputs across parameter settings"],"input_types":["numeric parameters (temperature: 0.0-2.0, top_p: 0.0-1.0, top_k: integer)"],"output_types":["text (generated response with tuned randomness)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-ibm-granite-granite-4.0-h-micro__cap_6","uri":"capability://text.generation.language.token.limited.response.generation","name":"token-limited-response-generation","description":"Constrains output length by specifying max_tokens parameter, which limits the number of tokens generated before stopping. The model stops generation when the token limit is reached, even if the response is incomplete, enabling cost control and predictable output sizes. Token counting is approximate (1 token ≈ 4 characters for English text) and handled server-side by OpenRouter.","intents":["I want to control inference costs by limiting output length per request","I need to generate summaries or snippets with predictable token consumption","I want to prevent runaway generation or infinite loops in conversational applications"],"best_for":["cost-conscious teams building high-volume AI applications","developers generating summaries, snippets, or structured outputs with fixed length requirements","builders implementing safety guardrails to prevent excessive token consumption"],"limitations":["max_tokens constraint may truncate responses mid-sentence or mid-thought, producing incomplete outputs","Token counting is approximate and language-dependent; actual token count may vary from estimates","No built-in logic to detect incomplete responses or signal truncation; application must handle partial outputs","Setting max_tokens too low may prevent model from completing valid responses; requires empirical tuning per use case","No support for dynamic token limits based on input length or context; fixed per-request configuration only"],"requires":["OpenRouter API key","Estimation of required tokens for expected output (documentation or testing)","Handling logic for truncated or incomplete responses","Optional: token counting library for client-side estimation"],"input_types":["numeric parameter (max_tokens: integer, typically 1-4096)"],"output_types":["text (response truncated to max_tokens limit)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":23,"verified":false,"data_access_risk":"high","permissions":["API key for OpenRouter or direct IBM Granite API access","HTTP/REST client library for API calls","Minimum 2GB RAM for local deployment if self-hosted","Network connectivity for cloud-based inference via OpenRouter","API key for OpenRouter","Client-side conversation history management (array of {role, content} objects)","Token counting library to track cumulative conversation length against context window","HTTP client for API calls with support for streaming or polling","Code context as text input (file contents, snippets, or function definitions)","Language specification in prompt or context for better code generation"],"failure_modes":["3B parameter size limits reasoning depth and factual accuracy compared to 7B+ models; may struggle with complex multi-step logical tasks","Fine-tuning specifics for long-context handling are proprietary; exact context window length not publicly documented","Inference latency on CPU-only systems will be significantly higher than quantized smaller models or GPU-accelerated inference","No built-in retrieval-augmented generation (RAG) integration; requires external vector database and retrieval pipeline for knowledge grounding","Stateless design requires resubmitting full conversation history with each turn, increasing token consumption and latency linearly with conversation length","No built-in conversation summarization; conversations longer than the context window will lose early context without explicit summarization logic","Attention mechanism may dilute focus on recent messages when conversation history exceeds ~4000 tokens; no recency bias optimization documented","No native support for multi-user conversations or conversation branching; requires application-level logic to manage parallel dialogue threads","3B parameter size limits ability to understand complex multi-file codebases or deeply nested logic; 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