{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"llama-3-2-3b","slug":"llama-3-2-3b","name":"Llama 3.2 3B","type":"model","url":"https://ai.meta.com/blog/llama-3-2-connect-2024-vision-edge-mobile-devices/","page_url":"https://unfragile.ai/llama-3-2-3b","categories":["model-training","documentation"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"llama-3-2-3b__cap_0","uri":"capability://text.generation.language.local.on.device.text.generation.with.128k.context.window","name":"local-on-device text generation with 128k context window","description":"Generates coherent text responses using a 3-billion-parameter transformer architecture deployable entirely on edge devices (mobile, laptop, embedded systems) without cloud connectivity. Implements a 128K token context window enabling processing of long documents, conversations, and multi-file code contexts in a single forward pass. Uses quantization-friendly architecture compatible with INT8, INT4, and other compression schemes for sub-gigabyte memory footprints on ARM-based processors.","intents":["Build a local AI assistant that runs offline on a user's laptop without sending data to cloud APIs","Process long documents (research papers, books, codebases) in a single inference pass without chunking","Deploy an AI agent on mobile devices or IoT hardware with minimal latency and no network dependency","Create privacy-preserving applications where user data never leaves the device"],"best_for":["Solo developers building offline-first LLM applications","Teams deploying AI to resource-constrained edge devices (mobile, embedded systems)","Organizations with strict data privacy requirements prohibiting cloud inference","Builders creating local AI assistants for consumer electronics"],"limitations":["No quantitative inference latency benchmarks published — actual tokens-per-second on reference hardware unknown","128K context window is hard limit; documents exceeding this require chunking or summarization preprocessing","Arm/Qualcomm optimization documented but specific hardware compatibility matrix not provided — may require testing on target device","Text-only model; no vision, audio, or multimodal capabilities (vision available only in 11B/90B variants)","Memory footprint in standard and quantized formats not explicitly specified — requires empirical testing on target hardware"],"requires":["PyTorch 2.0+ or compatible inference runtime (torchtune, torchchat, ExecuTorch, Ollama)","ARM-based processor (Qualcomm Snapdragon, MediaTek, Apple Silicon) or x86 CPU for laptop/desktop deployment","Minimum 2-4GB RAM for quantized variants; unknown for full-precision (likely 6-8GB based on 3B parameter count)","Model weights downloaded from Hugging Face or llama.com (approximately 6GB for full precision, 1-2GB quantized)","Python 3.9+ for fine-tuning with torchtune; inference frameworks support multiple languages"],"input_types":["text (raw prompts, documents, code snippets, conversation history up to 128K tokens)"],"output_types":["text (generated responses, completions, summaries, rewrites)"],"categories":["text-generation-language","edge-deployment"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llama-3-2-3b__cap_1","uri":"capability://text.generation.language.instruction.following.and.task.specific.fine.tuning","name":"instruction-following and task-specific fine-tuning","description":"Implements instruction-tuned variant trained to follow natural language directives for specific tasks (summarization, rewriting, Q&A, code generation). Supports parameter-efficient fine-tuning via torchtune framework, enabling developers to adapt the base model to domain-specific tasks without full retraining. Fine-tuned weights can be distributed as LoRA adapters or merged into the base model for deployment.","intents":["Fine-tune the model on proprietary domain data (legal documents, medical records, internal codebases) to improve task-specific accuracy","Create specialized variants for specific use cases (customer support chatbot, code reviewer, technical writer) without training from scratch","Adapt the model to follow custom instruction formats or domain-specific terminology","Distribute fine-tuned models as lightweight LoRA adapters without shipping full model weights"],"best_for":["Teams with domain-specific datasets (100-10K examples) wanting to customize model behavior","Developers building specialized AI assistants (customer support, code review, content generation)","Organizations needing to adapt the model to proprietary instruction formats or terminology","Builders distributing fine-tuned variants as plugins or adapters"],"limitations":["Fine-tuning framework (torchtune) is Python-only; no native support for other languages","No published benchmarks comparing fine-tuned 3B performance to base model or other fine-tuned alternatives","LoRA adapter compatibility with quantized models not explicitly documented","Requires GPU or TPU for practical fine-tuning; CPU-only fine-tuning would be prohibitively slow","No built-in evaluation metrics or automated hyperparameter tuning — requires manual experimentation"],"requires":["Python 3.9+","torchtune framework (PyTorch-based, requires PyTorch 2.0+)","GPU with 16GB+ VRAM for efficient fine-tuning (A100, H100, or consumer RTX 4090)","Training dataset in text format (JSONL, CSV, or custom loaders)","Base model weights from Hugging Face or llama.com"],"input_types":["text (instruction-response pairs, domain-specific documents, task examples)"],"output_types":["fine-tuned model weights (full or LoRA adapter format)","text (task-specific outputs from fine-tuned variant)"],"categories":["text-generation-language","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llama-3-2-3b__cap_10","uri":"capability://data.processing.analysis.structured.data.extraction.and.information.retrieval.from.unstructured.text","name":"structured data extraction and information retrieval from unstructured text","description":"Extracts structured information (entities, relationships, key-value pairs) from unstructured text using instruction-tuning and prompt engineering. Supports extraction of specific fields (names, dates, amounts, categories) with optional JSON or CSV output formatting. Works on documents up to 128K tokens enabling batch extraction from long documents without chunking.","intents":["Extract structured data (invoice amounts, dates, vendor names) from unstructured documents","Parse natural language descriptions into structured formats (JSON, CSV) for downstream processing","Identify and extract entities (people, organizations, locations) from long documents","Convert free-form text (meeting notes, customer feedback) into structured data for analysis"],"best_for":["Teams processing unstructured documents (invoices, contracts, emails) into structured databases","Developers building data extraction pipelines without external NER or structured extraction tools","Organizations automating document processing workflows","Builders creating data ingestion systems for knowledge bases or databases"],"limitations":["No published extraction accuracy benchmarks (precision, recall, F1) vs specialized NER tools or larger models","Extraction quality depends on instruction clarity and field definition; ambiguous requirements produce inconsistent results","No built-in validation or error handling; extracted data may be incomplete or malformed","JSON/CSV formatting accuracy not guaranteed — may produce invalid syntax requiring post-processing","No built-in deduplication or entity linking; duplicate entities not automatically merged","Inference latency unknown — may be slow for batch extraction of many documents"],"requires":["Inference runtime: torchtune, torchchat, Ollama, or ExecuTorch","Device with 2-4GB RAM (quantized) or 6-8GB (full precision estimated)","Unstructured text documents (emails, PDFs, web pages, etc.)","Optional: document preprocessing tools for format conversion","Optional: JSON schema or extraction template definition"],"input_types":["text (unstructured documents, extraction instructions, up to 128K tokens)"],"output_types":["text (structured data in JSON, CSV, or custom format)"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llama-3-2-3b__cap_11","uri":"capability://planning.reasoning.lightweight.reasoning.and.step.by.step.problem.solving","name":"lightweight reasoning and step-by-step problem solving","description":"Performs lightweight reasoning tasks (problem decomposition, step-by-step solutions, logical inference) suitable for edge deployment. Instruction-tuned to follow chain-of-thought prompts, enabling multi-step reasoning without external reasoning frameworks. Suitable for simple math problems, logic puzzles, and algorithmic thinking on resource-constrained devices.","intents":["Solve math problems with step-by-step reasoning on edge devices without cloud APIs","Decompose complex problems into sub-tasks for planning and execution","Perform logical inference and deduction for decision-making tasks","Generate explanations for complex concepts with intermediate reasoning steps"],"best_for":["Developers building reasoning-based applications on edge devices","Teams creating educational tools with step-by-step problem solving","Organizations needing privacy-preserving reasoning (no cloud reasoning APIs)","Builders creating planning agents or decision-support systems"],"limitations":["No published reasoning benchmarks (GSM8K, MATH, ARC scores) — actual performance vs larger models unknown","Reasoning quality limited by 3B parameter count; complex multi-step problems likely produce errors","No built-in verification or validation of reasoning steps; incorrect intermediate steps may propagate","Inference latency for long reasoning chains unknown — may be slow on mobile devices","No external tool integration for verification (calculator, code execution, fact-checking)"],"requires":["Inference runtime: torchtune, torchchat, Ollama, or ExecuTorch","Device with 2-4GB RAM (quantized) or 6-8GB (full precision estimated)","Problem descriptions in natural language or structured format","Optional: chain-of-thought prompt templates"],"input_types":["text (problem descriptions, reasoning prompts, up to 128K tokens)"],"output_types":["text (step-by-step reasoning, solutions, explanations)"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llama-3-2-3b__cap_12","uri":"capability://text.generation.language.meta.ai.assistant.integration.for.interactive.testing.and.exploration","name":"meta-ai-assistant integration for interactive testing and exploration","description":"Available via Meta AI smart assistant for interactive testing and exploration without local setup. Provides web-based interface for prompt experimentation, document upload, and conversation without requiring model download or inference infrastructure. Suitable for evaluating model capability before local deployment or for users without technical setup.","intents":["Test model capability interactively before committing to local deployment","Experiment with prompts and instructions without setting up inference infrastructure","Evaluate model performance on specific tasks (summarization, Q&A, coding) before integration","Share model outputs with non-technical stakeholders via web interface"],"best_for":["Product managers and non-technical stakeholders evaluating model capability","Developers prototyping applications before local integration","Teams benchmarking model performance on specific tasks","Builders exploring model behavior without infrastructure setup"],"limitations":["Web interface limitations unknown — may not support all model features (long context, fine-tuning, quantization)","No API access documented for Meta AI assistant — integration requires manual interaction","Rate limiting and usage quotas not documented","No conversation history export or API access for programmatic use","No fine-tuning or customization via web interface"],"requires":["Meta account (Facebook, Instagram, or standalone)","Web browser with internet connectivity","No local infrastructure required"],"input_types":["text (prompts, documents via upload)"],"output_types":["text (model responses via web interface)"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llama-3-2-3b__cap_2","uri":"capability://text.generation.language.document.summarization.and.long.form.text.analysis","name":"document summarization and long-form text analysis","description":"Processes documents up to 128K tokens (approximately 100K words or 400+ pages) in a single inference pass, enabling direct summarization, Q&A, and analysis without chunking or retrieval-augmented generation. Instruction-tuned variant trained on summarization tasks, allowing natural language directives like 'summarize this in 3 bullet points' or 'extract key technical details'. Suitable for legal documents, research papers, codebases, and meeting transcripts.","intents":["Summarize long documents (research papers, contracts, reports) in a single API call without chunking","Answer questions about multi-file codebases or entire documentation sets without external retrieval systems","Extract key information from long-form text (meeting transcripts, legal documents) with context-aware understanding","Analyze document relationships and cross-references within a single context window"],"best_for":["Developers building document analysis tools for legal, medical, or technical domains","Teams processing research papers, technical documentation, or large codebases","Organizations needing privacy-preserving document analysis (no cloud upload required)","Builders creating offline-first productivity tools (note-taking, research assistants)"],"limitations":["128K token limit requires preprocessing for documents exceeding ~100K words; no automatic chunking or summarization pipeline provided","No published benchmarks for summarization quality (ROUGE scores, human evaluation) vs larger models or specialized summarization tools","Summarization quality depends on instruction quality and model capability — may produce less coherent summaries than fine-tuned 7B+ models","No built-in document parsing; requires external tools to convert PDFs, Word docs, or images to text","Inference latency on edge devices unknown — may be slow for real-time interactive analysis on mobile"],"requires":["Document in text format (plain text, markdown, or extracted via OCR/PDF parser)","Inference runtime: torchtune, torchchat, Ollama, or PyTorch ExecuTorch","Device with sufficient RAM (2-4GB for quantized, 6-8GB for full precision estimated)","Optional: document preprocessing tools (pypdf, pdfplumber, or similar for PDF extraction)"],"input_types":["text (documents, code, transcripts, up to 128K tokens)"],"output_types":["text (summaries, extracted information, answers, analysis)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llama-3-2-3b__cap_3","uri":"capability://code.generation.editing.lightweight.code.generation.and.reasoning.for.edge.deployment","name":"lightweight code generation and reasoning for edge deployment","description":"Generates code snippets, explains code logic, and performs lightweight reasoning tasks (problem decomposition, step-by-step solutions) with 3B parameters optimized for edge devices. Outperforms 1B variant on coding tasks but trades off against 11B/90B variants for maximum capability. Suitable for code completion, bug explanation, and simple algorithm generation on resource-constrained devices without cloud API calls.","intents":["Generate code snippets or complete functions locally on a developer's machine without sending code to cloud APIs","Explain error messages and suggest fixes for bugs in codebases without external tools","Perform lightweight reasoning tasks (algorithm design, problem decomposition) on edge devices","Build IDE plugins or code editors with local code intelligence without cloud dependency"],"best_for":["Solo developers building offline-first code editors or IDE plugins","Teams deploying AI-assisted coding tools to resource-constrained environments","Organizations with strict code confidentiality requirements (no cloud code sharing)","Builders creating lightweight coding assistants for embedded systems or IoT devices"],"limitations":["No published benchmarks for code generation quality (HumanEval, MBPP scores) — actual performance vs Copilot, Claude, or GPT-4 unknown","Outperforms 1B variant but no comparison to 7B/13B models; likely produces less sophisticated code for complex algorithms","No multi-language code generation benchmarks; primary training likely English-focused","Inference latency on mobile devices not documented — may be slow for interactive code completion","No built-in code execution, testing, or validation; generated code requires manual review"],"requires":["Inference runtime: torchtune, torchchat, Ollama, or PyTorch ExecuTorch","Device with 2-4GB RAM (quantized) or 6-8GB (full precision estimated)","Code in text format (source files, error messages, problem descriptions)","Optional: syntax highlighting or IDE integration framework"],"input_types":["text (code snippets, error messages, natural language problem descriptions, up to 128K tokens)"],"output_types":["text (generated code, explanations, debugging suggestions, reasoning steps)"],"categories":["code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llama-3-2-3b__cap_4","uri":"capability://automation.workflow.multi.format.model.distribution.and.quantization","name":"multi-format model distribution and quantization","description":"Available in multiple formats (full precision, INT8, INT4, GGUF, and other quantization schemes) enabling deployment across diverse hardware with memory-capability trade-offs. Distributed via Hugging Face and llama.com with pre-quantized variants ready for immediate deployment. Supports quantization-aware inference frameworks (Ollama, ExecuTorch, torchtune) enabling automatic format selection based on target hardware.","intents":["Deploy the model on devices with varying memory constraints (2GB mobile to 8GB laptop) by selecting appropriate quantization","Download pre-quantized weights without running quantization pipelines locally","Switch between quantization formats (INT8 for speed, INT4 for memory) without retraining","Distribute models across heterogeneous hardware (mobile, desktop, server) with format-specific optimizations"],"best_for":["Developers deploying to multiple device types with different memory/compute profiles","Teams distributing models to end users without requiring quantization expertise","Builders creating cross-platform AI applications (mobile app, web, desktop)","Organizations optimizing for cost (lower quantization = cheaper inference hardware)"],"limitations":["Specific quantization formats (INT8, INT4, GGUF, GPTQ, AWQ) not explicitly documented — requires checking Hugging Face model card","No published quality degradation metrics for each quantization level (perplexity, benchmark score loss)","Quantization-inference latency trade-offs not benchmarked — actual speed gains from INT4 vs INT8 unknown","No automatic quantization tool provided; requires external tools (llama.cpp, GPTQ, bitsandbytes) for custom quantization","Quantized model compatibility with fine-tuning (LoRA) not explicitly documented"],"requires":["Model weights from Hugging Face or llama.com (pre-quantized variants available)","Inference runtime supporting target quantization format (Ollama for GGUF, ExecuTorch for INT8, etc.)","Optional: quantization tools (llama.cpp, GPTQ, bitsandbytes) for custom quantization","Target device with sufficient storage for model weights (1-2GB quantized, 6GB full precision)"],"input_types":["model weights (full precision or quantized formats)"],"output_types":["quantized model weights in target format","text (inference output from quantized model)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llama-3-2-3b__cap_5","uri":"capability://tool.use.integration.cross.platform.inference.via.partner.ecosystem.and.deployment.frameworks","name":"cross-platform inference via partner ecosystem and deployment frameworks","description":"Deployed across 15+ partner platforms (AWS, Google Cloud, Azure, Databricks, Together AI, Fireworks, etc.) and inference frameworks (Ollama, ExecuTorch, torchtune, torchchat) enabling single-model deployment to cloud, edge, and mobile without framework-specific rewrites. Partners provide optimized inference stacks, serving infrastructure, and managed fine-tuning. Llama Stack distributions abstract framework differences, enabling portable inference code.","intents":["Deploy the same model to AWS, Google Cloud, and Azure without rewriting inference code","Use managed inference services (Together AI, Fireworks) for scalable cloud deployment without managing infrastructure","Run the model locally via Ollama for development, then deploy to cloud via partner platforms for production","Access optimized inference on specialized hardware (NVIDIA, AMD, Intel) via partner platforms"],"best_for":["Teams deploying to multiple cloud providers and wanting vendor lock-in avoidance","Developers using Llama Stack abstraction for portable inference code","Organizations leveraging managed inference services (Together AI, Fireworks) for cost optimization","Builders creating multi-platform applications (local + cloud hybrid deployment)"],"limitations":["Llama Stack abstraction details not documented — actual portability and API consistency across partners unknown","Partner-specific optimizations and latency characteristics not published — performance varies by provider","No unified pricing comparison across partners; cost optimization requires manual benchmarking","Partner availability and feature parity not guaranteed — some partners may not support all quantization formats or fine-tuning","Managed service lock-in risk — switching providers requires code changes if using provider-specific APIs"],"requires":["Cloud account (AWS, Google Cloud, Azure, Databricks, etc.) or local inference runtime (Ollama, ExecuTorch)","API credentials for chosen platform","Llama Stack SDK (if using abstraction layer) or platform-specific SDK","Network connectivity for cloud deployment; local deployment requires no external connectivity"],"input_types":["text (prompts, documents, code, up to 128K tokens)"],"output_types":["text (generated responses, completions)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llama-3-2-3b__cap_6","uri":"capability://automation.workflow.mobile.and.embedded.device.optimization.with.hardware.acceleration","name":"mobile and embedded device optimization with hardware acceleration","description":"Optimized for ARM-based processors (Qualcomm Snapdragon, MediaTek, Apple Silicon) with native hardware acceleration enabled on day one. Deployed via PyTorch ExecuTorch for on-device inference with quantization and operator fusion for sub-second latency on mobile. Supports both Android and iOS deployment with framework-specific optimizations (XNNPACK for CPU, Metal for iOS GPU).","intents":["Deploy AI assistants to iOS and Android apps with sub-second inference latency","Build on-device features (text completion, summarization, Q&A) without cloud API calls","Optimize inference for specific mobile hardware (Snapdragon 8 Gen 3, MediaTek Dimensity) with hardware-specific kernels","Create privacy-preserving mobile apps where user data never leaves the device"],"best_for":["Mobile app developers building offline-first AI features","Teams deploying AI to consumer electronics (smartwatches, IoT devices)","Organizations with strict privacy requirements (healthcare, finance, government)","Builders creating cross-platform mobile apps (iOS + Android) with unified AI backend"],"limitations":["Specific mobile hardware compatibility matrix not published — requires testing on target devices","Inference latency on mobile devices not benchmarked — actual tokens-per-second on iPhone 15, Pixel 8 unknown","Memory footprint on mobile not documented — may exceed available RAM on older devices","ExecuTorch deployment requires native code compilation; no high-level Python API for mobile","iOS Metal GPU acceleration details not documented; may fall back to CPU on unsupported devices","No built-in mobile UI framework; requires custom integration with app UI"],"requires":["iOS 14+ (Apple Silicon) or Android 10+ (Snapdragon/MediaTek)","PyTorch ExecuTorch framework for on-device inference","Native development environment (Xcode for iOS, Android Studio for Android)","Model weights in ExecuTorch format (quantized, typically 1-2GB)","Device with 2-4GB available RAM for inference"],"input_types":["text (prompts, documents, up to 128K tokens)"],"output_types":["text (generated responses, completions)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llama-3-2-3b__cap_7","uri":"capability://text.generation.language.conversational.ai.and.multi.turn.dialogue.with.long.context","name":"conversational ai and multi-turn dialogue with long context","description":"Instruction-tuned for conversational tasks with 128K context window enabling multi-turn conversations with full history retention without context truncation. Maintains conversation state across dozens of turns without losing earlier context, suitable for chatbots, virtual assistants, and interactive applications. Supports system prompts and role-based instructions for specialized conversational behaviors.","intents":["Build chatbots that maintain coherent multi-turn conversations without losing context","Create virtual assistants that remember earlier parts of long conversations","Implement specialized conversational agents (customer support, technical support, tutoring) with role-based instructions","Develop interactive applications where users can reference earlier conversation turns without re-explaining context"],"best_for":["Teams building chatbot applications with long conversation histories","Developers creating customer support or technical support assistants","Builders developing interactive tutoring or educational applications","Organizations needing privacy-preserving conversational AI (no cloud conversation logging)"],"limitations":["No published conversation quality benchmarks (coherence, consistency, factuality) vs larger models or specialized dialogue models","Conversation state management (persistence, multi-user handling) not provided — requires external database","No built-in conversation safety or moderation; requires external content filtering","Inference latency for long conversations not benchmarked — may be slow on edge devices with 50+ turn history","No conversation analytics or logging framework provided"],"requires":["Inference runtime: torchtune, torchchat, Ollama, or ExecuTorch","Device with 2-4GB RAM (quantized) or 6-8GB (full precision estimated)","Optional: conversation management framework (LangChain, LlamaIndex) for state persistence","Optional: content moderation tools for safety"],"input_types":["text (user messages, system prompts, conversation history up to 128K tokens)"],"output_types":["text (assistant responses, conversational outputs)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llama-3-2-3b__cap_8","uri":"capability://text.generation.language.text.rewriting.and.style.transformation","name":"text rewriting and style transformation","description":"Instruction-tuned for text rewriting tasks (paraphrasing, tone adjustment, formality changes, grammar correction) with 128K context enabling rewriting of long documents in single pass. Supports natural language directives like 'rewrite this in a more formal tone' or 'simplify this technical explanation for a general audience'. Suitable for content editing, accessibility improvement, and style normalization.","intents":["Rewrite long documents (articles, reports, documentation) in different styles or tones without chunking","Simplify technical content for non-expert audiences or vice versa","Improve grammar and clarity of user-generated content (emails, essays, documentation)","Adapt content for different contexts (formal report, casual blog post, technical documentation)"],"best_for":["Content creators and writers using AI for editing and style improvement","Teams improving documentation clarity and accessibility","Developers building writing assistance tools (grammar checkers, style guides)","Organizations normalizing content style across large document sets"],"limitations":["No published benchmarks for rewriting quality (BLEU, human evaluation) vs specialized rewriting tools or larger models","Rewriting accuracy depends on instruction clarity; ambiguous directives may produce inconsistent results","No built-in fact-checking; rewrites may introduce factual errors or hallucinations","Inference latency on edge devices unknown — may be slow for real-time editing","No built-in plagiarism detection or originality verification"],"requires":["Inference runtime: torchtune, torchchat, Ollama, or ExecuTorch","Device with 2-4GB RAM (quantized) or 6-8GB (full precision estimated)","Text in any format (plain text, markdown, code comments)","Optional: text preprocessing tools for format conversion"],"input_types":["text (documents, articles, code comments, up to 128K tokens)"],"output_types":["text (rewritten content in target style/tone)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llama-3-2-3b__cap_9","uri":"capability://text.generation.language.question.answering.over.long.documents.and.knowledge.bases","name":"question-answering over long documents and knowledge bases","description":"Answers questions about documents up to 128K tokens (entire books, codebases, knowledge bases) in single inference pass without retrieval-augmented generation. Instruction-tuned for Q&A tasks with ability to cite source locations and provide multi-step reasoning. Supports both factual retrieval ('What is X?') and reasoning questions ('Why would X cause Y?').","intents":["Answer questions about entire codebases or documentation without external search/retrieval systems","Build Q&A systems over long documents (research papers, legal contracts, technical manuals) without chunking","Create knowledge base assistants that answer questions with source citations","Implement interactive documentation systems where users ask questions about entire product documentation"],"best_for":["Developers building Q&A systems over technical documentation or codebases","Teams creating knowledge base assistants for internal documentation","Organizations deploying Q&A systems with privacy requirements (no cloud upload)","Builders creating interactive documentation or help systems"],"limitations":["No published Q&A accuracy benchmarks (F1, EM scores) vs RAG systems or larger models","Answer quality depends on document clarity and question specificity; ambiguous questions may produce generic answers","No built-in fact verification; answers may contain hallucinations or unsupported claims","Source citation accuracy not benchmarked — may cite irrelevant or incorrect passages","Inference latency on edge devices unknown — may be slow for real-time Q&A","No built-in confidence scoring; no way to distinguish high-confidence from speculative answers"],"requires":["Inference runtime: torchtune, torchchat, Ollama, or ExecuTorch","Device with 2-4GB RAM (quantized) or 6-8GB (full precision estimated)","Document in text format (plain text, markdown, or extracted via OCR/PDF parser)","Optional: document preprocessing tools for format conversion and cleaning"],"input_types":["text (documents, questions, up to 128K tokens total)"],"output_types":["text (answers with optional source citations and reasoning)"],"categories":["text-generation-language","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llama-3-2-3b__headline","uri":"capability://text.generation.language.lightweight.text.model.for.mobile.and.edge.deployment","name":"lightweight text model for mobile and edge deployment","description":"Llama 3.2 3B is a compact text model designed for mobile and edge devices, balancing high performance in reasoning and coding tasks with deployment flexibility for local AI assistants and document analysis.","intents":["best lightweight text model","text model for mobile devices","AI assistant model for local deployment","3 billion parameter text model for edge computing","best model for document analysis"],"best_for":["mobile deployment","local AI assistants","document analysis"],"limitations":["limited to 128K context window"],"requires":["compatible hardware for deployment"],"input_types":[],"output_types":[],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":58,"verified":false,"data_access_risk":"high","permissions":["PyTorch 2.0+ or compatible inference runtime (torchtune, torchchat, ExecuTorch, Ollama)","ARM-based processor (Qualcomm Snapdragon, MediaTek, Apple Silicon) or x86 CPU for laptop/desktop deployment","Minimum 2-4GB RAM for quantized variants; unknown for full-precision (likely 6-8GB based on 3B parameter count)","Model weights downloaded from Hugging Face or llama.com (approximately 6GB for full precision, 1-2GB quantized)","Python 3.9+ for fine-tuning with torchtune; inference frameworks support multiple languages","Python 3.9+","torchtune framework (PyTorch-based, requires PyTorch 2.0+)","GPU with 16GB+ VRAM for efficient fine-tuning (A100, H100, or consumer RTX 4090)","Training dataset in text format (JSONL, CSV, or custom loaders)","Base model weights from Hugging Face or llama.com"],"failure_modes":["No quantitative inference latency benchmarks published — actual tokens-per-second on reference hardware unknown","128K context window is hard limit; documents exceeding this require chunking or summarization preprocessing","Arm/Qualcomm optimization documented but specific hardware compatibility matrix not provided — may require testing on target device","Text-only model; no vision, audio, or multimodal capabilities (vision available only in 11B/90B variants)","Memory footprint in standard and quantized formats not explicitly specified — requires empirical testing on target hardware","Fine-tuning framework (torchtune) is Python-only; 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