{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"llama-3-2-1b","slug":"llama-3-2-1b","name":"Llama 3.2 1B","type":"model","url":"https://ai.meta.com/blog/llama-3-2-connect-2024-vision-edge-mobile-devices/","page_url":"https://unfragile.ai/llama-3-2-1b","categories":["deployment-infra"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"llama-3-2-1b__cap_0","uri":"capability://text.generation.language.on.device.text.generation.with.128k.context.window","name":"on-device text generation with 128k context window","description":"Generates coherent text completions and responses on mobile phones, IoT devices, and embedded systems using a 1 billion parameter transformer architecture with 128K token context window. Operates entirely locally without cloud connectivity, using quantized model weights (int8/int4 formats) distributed via PyTorch ExecuTorch runtime, enabling sub-100MB memory footprint on ARM processors from Qualcomm and MediaTek.","intents":["Run a language model directly on a smartphone without sending data to cloud servers","Generate text summaries and responses on IoT devices with <500MB RAM","Build offline-first mobile apps that don't require internet connectivity for inference","Deploy LLM capabilities on edge hardware where latency and privacy are critical"],"best_for":["Mobile app developers building privacy-first features","IoT engineers deploying edge AI on resource-constrained devices","Teams requiring offline-first inference without cloud dependencies","Organizations with strict data residency or privacy requirements"],"limitations":["Inference latency unknown — no published benchmarks for token generation speed on target hardware","Memory footprint varies by quantization format (int8 vs int4) — specific VRAM requirements not documented","128K context window is fixed and non-expandable, limiting long-document processing","Text-only capability — no vision or multimodal understanding","Basic reasoning only — not suitable for complex multi-step problem solving"],"requires":["ARM-based processor (Qualcomm Snapdragon or MediaTek SoC)","PyTorch ExecuTorch runtime for on-device execution","Minimum RAM: unknown (estimated 512MB-2GB for quantized variants)","Model weights downloaded from llama.com or Hugging Face (1B base model)"],"input_types":["text (UTF-8 encoded)","prompts up to 128K tokens"],"output_types":["text (streaming or batch generation)","token sequences with configurable length limits"],"categories":["text-generation-language","edge-deployment"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llama-3-2-1b__cap_1","uri":"capability://text.generation.language.instruction.following.and.task.completion","name":"instruction-following and task completion","description":"Executes natural language instructions for text rewriting, summarization, and basic reasoning tasks through instruction-tuned model variants. The model interprets user intent from prompts and generates task-specific outputs without requiring explicit few-shot examples, leveraging instruction-tuning applied during training to align model behavior with user commands.","intents":["Summarize documents or articles into concise bullet points or paragraphs","Rewrite text in different styles or tones (formal, casual, technical)","Answer questions based on provided context or general knowledge","Follow multi-step instructions for content transformation tasks"],"best_for":["Content creators needing on-device text transformation without cloud APIs","Mobile app developers adding AI-powered text features","Teams building chatbots or Q&A systems with privacy requirements"],"limitations":["Basic reasoning only — cannot handle complex multi-hop logical reasoning or advanced problem solving","No explicit chain-of-thought or step-by-step reasoning output format","Instruction-tuning methodology and training data composition unknown","No published accuracy metrics for instruction-following tasks vs. larger models","Hallucination rates and failure modes not documented"],"requires":["Instruction-tuned model variant (not base pre-trained model)","PyTorch ExecuTorch runtime for execution","Clear, well-formed natural language instructions in input prompt"],"input_types":["text instructions","context documents (up to 128K tokens total)","task specifications in natural language"],"output_types":["text completions","summarized content","rewritten text","answers to questions"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llama-3-2-1b__cap_2","uri":"capability://code.generation.editing.fine.tuning.for.custom.applications.via.torchtune","name":"fine-tuning for custom applications via torchtune","description":"Enables adaptation of the 1B model to custom domains and use cases through torchtune framework, supporting parameter-efficient fine-tuning (LoRA, QLoRA) on consumer hardware. Fine-tuned models can be deployed locally via torchchat or ExecuTorch, allowing developers to specialize the model for domain-specific tasks (customer support, technical documentation, domain-specific Q&A) without retraining from scratch.","intents":["Adapt the model to your company's specific terminology, style, or domain knowledge","Fine-tune on proprietary data without sending it to cloud services","Create specialized versions for customer support, technical documentation, or vertical-specific tasks","Reduce inference costs by using a smaller fine-tuned model instead of larger base models"],"best_for":["Teams with proprietary domain data requiring model customization","Organizations with strict data privacy requirements preventing cloud fine-tuning","Developers building specialized chatbots or Q&A systems for specific industries","Companies optimizing for inference cost and latency on edge devices"],"limitations":["Fine-tuning framework (torchtune) and supported techniques (LoRA, QLoRA) not fully documented in source material","Hardware requirements for fine-tuning unknown — estimated to require GPU with 8GB+ VRAM","Training time, convergence behavior, and optimal hyperparameters not published","No guidance on minimum dataset size or data quality requirements","Fine-tuned model deployment pipeline and versioning not documented"],"requires":["torchtune framework (PyTorch-based fine-tuning library)","GPU with sufficient VRAM for fine-tuning (estimated 8GB+, specific requirements unknown)","Custom training dataset in text format","PyTorch 2.0+ and Python 3.9+"],"input_types":["text training data (JSONL, CSV, or plain text format)","domain-specific examples and use cases"],"output_types":["fine-tuned model weights","adapter weights (LoRA format)","quantized fine-tuned model for deployment"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llama-3-2-1b__cap_3","uri":"capability://automation.workflow.local.deployment.via.ollama.and.executorch","name":"local deployment via ollama and executorch","description":"Distributes quantized model variants through Ollama (single-node inference server) and PyTorch ExecuTorch (on-device runtime), enabling one-command deployment on laptops, servers, and mobile devices. Ollama provides a REST API interface for local inference without cloud connectivity, while ExecuTorch optimizes model execution for ARM processors with minimal binary size and memory overhead.","intents":["Run the model locally on a laptop or server without cloud API costs","Integrate the model into existing applications via REST API (Ollama)","Deploy to mobile apps with optimized binary size and memory usage (ExecuTorch)","Build offline-first applications that don't depend on external API availability"],"best_for":["Developers building local-first AI applications","Teams avoiding cloud API costs and vendor lock-in","Organizations with air-gapped or offline infrastructure","Mobile app developers requiring on-device inference"],"limitations":["Ollama REST API specification and authentication mechanisms not documented","ExecuTorch quantization formats (int8, int4, etc.) and performance trade-offs not detailed","Inference latency benchmarks for Ollama and ExecuTorch not published","Scaling limitations unknown — Ollama designed for single-node, not distributed inference","Model format compatibility and version management not documented"],"requires":["Ollama runtime (for server/laptop deployment) or PyTorch ExecuTorch (for mobile)","Model weights downloaded from llama.com or Hugging Face","For Ollama: Linux, macOS, or Windows with sufficient disk space","For ExecuTorch: ARM processor and PyTorch ExecuTorch SDK"],"input_types":["text prompts via REST API (Ollama)","text input to ExecuTorch runtime (mobile)"],"output_types":["JSON responses with text completions (Ollama)","streaming text output (both platforms)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llama-3-2-1b__cap_4","uri":"capability://tool.use.integration.ecosystem.integration.with.hardware.partners","name":"ecosystem integration with hardware partners","description":"Provides optimized implementations and pre-built integrations with major hardware platforms (Qualcomm, MediaTek, AMD, NVIDIA, Intel) and cloud providers (AWS, Google Cloud, Azure, Oracle Cloud) through Meta's partner ecosystem. Hardware partners enable day-one optimization for their processors, while cloud providers offer managed deployment options, reducing integration friction for developers.","intents":["Deploy the model on Qualcomm or MediaTek mobile processors with native optimization","Run inference on cloud infrastructure (AWS, Google Cloud, Azure) without custom setup","Leverage hardware-specific optimizations for faster inference on target devices","Access managed model serving through cloud provider marketplaces"],"best_for":["Mobile app developers targeting Qualcomm/MediaTek devices","Teams deploying on cloud infrastructure (AWS, GCP, Azure, Oracle)","Organizations seeking hardware-optimized inference without custom optimization","Enterprises using existing cloud provider relationships"],"limitations":["Specific hardware optimizations and performance improvements not documented","Cloud provider pricing, SLAs, and service terms vary by platform","Integration details and API specifications for each partner unknown","No published performance comparisons across hardware partners","Availability and feature parity across different cloud providers not guaranteed"],"requires":["Target hardware platform (Qualcomm Snapdragon, MediaTek SoC, or cloud provider account)","Cloud provider SDK or CLI (AWS CLI, gcloud, Azure CLI, etc.) if using cloud deployment","Appropriate credentials and permissions for hardware/cloud platform"],"input_types":["text prompts","configuration parameters for hardware-specific optimizations"],"output_types":["text completions","inference metrics and performance data"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llama-3-2-1b__cap_5","uri":"capability://data.processing.analysis.quantization.and.memory.optimization.for.resource.constrained.devices","name":"quantization and memory optimization for resource-constrained devices","description":"Provides quantized model variants (int8, int4 formats inferred from 'minimal memory footprint' claims) that compress model weights while maintaining inference quality, enabling deployment on devices with <500MB available RAM. Quantization reduces model size from estimated 4GB (fp32) to <500MB (int4), implemented through PyTorch quantization tools and ExecuTorch's optimization pipeline.","intents":["Run the model on smartphones with limited RAM (1-4GB total)","Reduce model download size for mobile app distribution","Minimize memory usage on IoT devices with severe resource constraints","Balance inference quality against memory footprint for target hardware"],"best_for":["Mobile app developers targeting budget smartphones","IoT engineers deploying on memory-constrained embedded systems","Teams optimizing for download size and installation footprint","Organizations requiring minimal power consumption on edge devices"],"limitations":["Specific quantization formats (int8, int4, etc.) and their trade-offs not documented","Quantization impact on model quality and accuracy not published","Memory requirements for different quantization levels unknown","Inference speed impact of quantization not benchmarked","No guidance on selecting appropriate quantization level for target hardware"],"requires":["PyTorch quantization tools or ExecuTorch quantization pipeline","Target device with ARM processor and <500MB available RAM","Model weights in quantizable format (fp32 or bf16)"],"input_types":["full-precision model weights (fp32/bf16)","quantization configuration parameters"],"output_types":["quantized model weights (int8/int4 format)","quantization statistics and quality metrics"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llama-3-2-1b__cap_6","uri":"capability://text.generation.language.meta.ai.assistant.integration.for.development.and.testing","name":"meta ai assistant integration for development and testing","description":"Provides immediate access to Llama 3.2 1B through Meta's AI assistant interface for prompt testing, evaluation, and development without local setup. Developers can experiment with model behavior, test instruction-following capability, and validate use cases before deploying locally, reducing iteration time during development.","intents":["Test model behavior and instruction-following capability before local deployment","Evaluate summarization and text rewriting quality on sample data","Prototype chatbot or Q&A system interactions quickly","Validate model fit for specific use cases without infrastructure setup"],"best_for":["Developers evaluating model fit before committing to deployment","Teams prototyping AI features without local infrastructure","Non-technical stakeholders testing model capabilities","Rapid iteration and experimentation during development phase"],"limitations":["Meta AI assistant API specification and rate limits not documented","Cloud-based inference may not reflect on-device performance characteristics","No published SLA or availability guarantees","Data privacy and retention policies for assistant interactions unknown","Limited to text-only interaction — no programmatic API access documented"],"requires":["Meta account or access to Meta AI assistant","Internet connectivity","No local infrastructure or model downloads required"],"input_types":["text prompts","natural language instructions"],"output_types":["text responses","model behavior observations"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llama-3-2-1b__cap_7","uri":"capability://text.generation.language.128k.token.context.window.for.long.document.processing","name":"128k token context window for long-document processing","description":"Supports processing and generating text with up to 128K token context window, enabling summarization and analysis of long documents (approximately 100K words or 400+ pages) in a single inference pass. The 128K context is fixed and non-expandable, implemented through standard transformer attention mechanisms without specialized long-context techniques.","intents":["Summarize long documents, research papers, or books in a single request","Analyze multi-page contracts or legal documents for key terms","Process entire codebases or documentation for code understanding tasks","Maintain conversation history in chatbot applications without truncation"],"best_for":["Document analysis and summarization applications","Long-form content processing on edge devices","Chatbot applications requiring extended conversation history","Code analysis and documentation understanding tasks"],"limitations":["128K context is fixed and non-expandable — cannot process documents larger than ~100K words","Inference latency scales with context length — full 128K context likely requires seconds per request","Memory usage increases with context length — may exceed device limits on smaller devices","Attention mechanism computational complexity is O(n²) — very long contexts may be impractical","No published benchmarks for context utilization quality or retrieval accuracy"],"requires":["Sufficient device memory to hold full context in VRAM (estimated 2-4GB for 128K tokens)","Text input tokenized to 128K tokens or fewer","Reasonable inference latency tolerance (likely 5-30 seconds for full context)"],"input_types":["text documents up to 128K tokens","concatenated conversation history","code or documentation files"],"output_types":["text summaries","analysis results","generated continuations"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llama-3-2-1b__cap_8","uri":"capability://text.generation.language.text.only.inference.without.vision.capability","name":"text-only inference without vision capability","description":"Processes text-only inputs and generates text-only outputs, with no image understanding, vision processing, or multimodal capability. This is explicitly the text-only variant of Llama 3.2 family (distinct from 11B and 90B vision variants), optimized for pure language tasks and reducing model size/complexity for edge deployment.","intents":["Build text-only chatbots and Q&A systems without vision overhead","Deploy language models on devices without camera or image processing capability","Reduce model size and memory footprint by excluding vision components","Focus inference optimization on language understanding and generation"],"best_for":["Text-only applications (chatbots, summarization, translation)","Devices without camera or image input capability","Teams optimizing for minimal model size and memory","Applications where vision capability is not required"],"limitations":["Cannot process, analyze, or understand images or visual content","No OCR or document image analysis capability","Cannot describe images, charts, or diagrams","Not suitable for multimodal applications requiring vision understanding","No fallback to vision capability — separate vision model required if needed"],"requires":["Text-only input data","No image processing libraries or vision models needed"],"input_types":["text (UTF-8 encoded)","prompts and instructions"],"output_types":["text completions","generated text responses"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llama-3-2-1b__headline","uri":"capability://deployment.infra.lightweight.ai.model.for.edge.and.mobile.deployment","name":"lightweight ai model for edge and mobile deployment","description":"Llama 3.2 1B is an ultra-lightweight AI model optimized for on-device and edge deployments, making it ideal for mobile phones and IoT devices with limited computational resources.","intents":["best lightweight AI model for mobile","AI model for edge computing","top models for IoT devices","best text-only model for smartphones","AI model with low memory footprint"],"best_for":["mobile devices","IoT applications","embedded 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