{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-meta-llama--llama-3.2-1b-instruct","slug":"meta-llama--llama-3.2-1b-instruct","name":"Llama-3.2-1B-Instruct","type":"model","url":"https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct","page_url":"https://unfragile.ai/meta-llama--llama-3.2-1b-instruct","categories":["chatbots-assistants"],"tags":["transformers","safetensors","llama","text-generation","facebook","meta","pytorch","llama-3","conversational","en","de","fr","it","pt","hi","es","th","arxiv:2204.05149","arxiv:2405.16406","license:llama3.2"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-model-meta-llama--llama-3.2-1b-instruct__cap_0","uri":"capability://text.generation.language.instruction.tuned.conversational.text.generation","name":"instruction-tuned conversational text generation","description":"Generates coherent multi-turn conversational responses using a 1B-parameter transformer architecture fine-tuned on instruction-following datasets. The model uses causal language modeling with attention mechanisms to maintain context across dialogue turns, supporting both single-turn queries and multi-message conversation histories. Inference runs locally via PyTorch/ONNX without requiring cloud API calls, enabling low-latency edge deployment.","intents":["Build a lightweight chatbot that runs on consumer hardware without cloud dependencies","Deploy a conversational AI assistant on mobile or edge devices with minimal memory footprint","Create a local question-answering system that maintains conversation context across multiple exchanges","Integrate a privacy-preserving chat interface into applications where data cannot leave the device"],"best_for":["solo developers building offline-first applications","teams deploying to resource-constrained environments (mobile, IoT, edge servers)","organizations with strict data residency requirements","researchers prototyping conversational AI without API costs"],"limitations":["1B parameters limits reasoning depth and factual accuracy compared to 7B+ models — struggles with complex multi-step logic","No built-in retrieval augmentation — cannot access external knowledge bases or real-time information without explicit integration","Context window limited to ~8K tokens — cannot maintain coherence over very long conversation histories","Single-GPU inference only — no native distributed inference support for batching across multiple devices","Instruction-tuning optimized for English; multilingual support (DE, FR, IT, PT, HI, ES, TH) is degraded vs monolingual models"],"requires":["Python 3.8+","PyTorch 2.0+ or ONNX Runtime 1.14+","4GB+ RAM for model weights in float32 (2GB with int8 quantization)","HuggingFace transformers library 4.36+","Optional: CUDA 11.8+ for GPU acceleration (CPU inference supported but ~10x slower)"],"input_types":["plain text (single message or multi-turn conversation array)","structured prompt templates with system/user/assistant roles"],"output_types":["plain text response","streaming token-by-token output","logits/probability distributions (for sampling strategies)"],"categories":["text-generation-language","conversational-ai"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-meta-llama--llama-3.2-1b-instruct__cap_1","uri":"capability://text.generation.language.multilingual.text.generation.with.language.specific.adaptation","name":"multilingual text generation with language-specific adaptation","description":"Generates text in 9 languages (English, German, French, Italian, Portuguese, Hindi, Spanish, Thai, and others) using a shared transformer backbone with language-aware tokenization and embedding spaces. The model applies language-specific instruction-tuning to adapt response style and formatting conventions per language, routing through the same parameter set without language-specific model branches.","intents":["Build a single chatbot that serves users across multiple languages without maintaining separate models","Generate localized content in multiple languages from a single inference endpoint","Create international customer support systems that respond in the user's native language","Reduce deployment complexity by using one model instead of language-specific variants"],"best_for":["global SaaS platforms needing multi-language support without model multiplication","international teams building conversational AI with limited infrastructure budgets","developers targeting emerging markets where language-specific models are unavailable"],"limitations":["Language quality is not uniform — English and major European languages (DE, FR, ES) perform well, but Hindi and Thai show degraded fluency and grammatical accuracy","No explicit language detection — requires external language identification or user-specified language parameter","Code-switching (mixing languages in single response) is not explicitly handled — may produce inconsistent output for multilingual inputs","Tokenization efficiency varies by language — Thai and Hindi require more tokens per semantic unit, increasing inference latency by 15-30%"],"requires":["Python 3.8+","HuggingFace transformers 4.36+ with multilingual tokenizer support","Language-specific Unicode support in runtime environment","Optional: external language detection library (e.g., langdetect, fasttext) for automatic language routing"],"input_types":["plain text in any supported language","explicit language tag/parameter to specify target language"],"output_types":["plain text response in specified language","language-tagged output with confidence scores (if using external detection)"],"categories":["text-generation-language","localization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-meta-llama--llama-3.2-1b-instruct__cap_10","uri":"capability://text.generation.language.conversational.context.management.with.multi.turn.dialogue","name":"conversational context management with multi-turn dialogue","description":"Maintains conversation state across multiple turns by processing full dialogue history (system message, user messages, assistant responses) as a single input sequence. The model uses causal attention to weight recent messages more heavily while retaining long-range context, enabling coherent multi-turn conversations without explicit state management or memory modules.","intents":["Build chatbots that maintain conversation context across multiple user interactions","Create dialogue systems where responses adapt based on previous exchanges","Implement conversational agents that remember user preferences or facts mentioned earlier","Enable natural conversation flow without requiring users to repeat context"],"best_for":["chat application developers building conversational UIs","customer support teams implementing context-aware support bots","conversational AI researchers studying dialogue coherence"],"limitations":["Context window limits conversation length to ~8K tokens — long conversations require message pruning or summarization","No explicit memory mechanism — context older than window size is lost permanently","Attention mechanisms may not weight recent messages appropriately — model may forget recent facts in favor of earlier context","No built-in conversation summarization — requires external summarization to compress old messages","Multi-turn performance degrades with conversation length — accuracy drops ~5-10% per 2K tokens of history"],"requires":["Python 3.8+","HuggingFace transformers 4.36+","Message formatting following model's expected dialogue format (system/user/assistant roles)","Optional: conversation management library (LangChain, LlamaIndex) for state handling"],"input_types":["conversation history (array of messages with roles: system, user, assistant)","current user message (string)"],"output_types":["assistant response (string)","optional: token count for context window management"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-meta-llama--llama-3.2-1b-instruct__cap_11","uri":"capability://text.generation.language.safety.aligned.response.generation.with.refusal.mechanisms","name":"safety-aligned response generation with refusal mechanisms","description":"Generates responses while avoiding harmful, illegal, or unethical content through alignment training and safety fine-tuning. The model learns to refuse requests for illegal activities, hate speech, or dangerous information, and to provide helpful alternatives when appropriate. Safety is implemented through instruction-tuning on safety datasets rather than post-hoc filtering.","intents":["Deploy chatbots in production with reduced risk of generating harmful content","Build customer-facing AI systems that comply with content policies and legal requirements","Create educational or research systems that avoid generating dangerous information","Reduce moderation overhead by filtering harmful requests at the model level"],"best_for":["teams deploying public-facing chatbots with safety requirements","organizations with strict content policies or regulatory compliance needs","platforms serving diverse user bases with varying safety sensitivities"],"limitations":["Safety alignment is not perfect — model may still generate harmful content in edge cases or with adversarial prompts","Refusal behavior is sometimes overly cautious — may refuse benign requests (e.g., discussing historical violence in educational context)","Safety training may reduce model capability on some legitimate tasks — e.g., discussing security vulnerabilities for defensive purposes","No explicit safety score or confidence — cannot quantify how safe a response is","Safety alignment may be bypassed with sophisticated prompt injection or jailbreak attempts"],"requires":["Python 3.8+","HuggingFace transformers 4.36+","Optional: external content moderation API (OpenAI Moderation, Perspective API) for additional safety layers"],"input_types":["user prompt (string, potentially harmful or benign)"],"output_types":["response text (string, either helpful answer or refusal with explanation)","optional: safety classification or confidence score"],"categories":["text-generation-language","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-meta-llama--llama-3.2-1b-instruct__cap_2","uri":"capability://text.generation.language.quantized.inference.with.memory.efficient.model.loading","name":"quantized inference with memory-efficient model loading","description":"Supports loading and inference using int8 and fp16 quantization schemes via bitsandbytes or ONNX quantization, reducing model size from ~2GB (fp32) to ~1GB (int8) or ~500MB (int4 with additional compression). Quantization is applied post-training without retraining, preserving instruction-following capability while enabling deployment on devices with <2GB VRAM or mobile hardware.","intents":["Deploy the model on mobile devices or edge hardware with <2GB available memory","Reduce inference latency by 20-40% through quantized matrix operations on CPU or GPU","Run multiple model instances on a single GPU for batch inference or multi-tenant serving","Minimize bandwidth requirements for model distribution and updates"],"best_for":["mobile app developers targeting iOS/Android with on-device inference","edge computing teams deploying to Raspberry Pi, Jetson Nano, or similar constrained hardware","SaaS platforms needing to fit multiple model instances on shared GPU infrastructure","teams with strict bandwidth constraints (e.g., rural connectivity, IoT networks)"],"limitations":["int8 quantization introduces 1-3% accuracy degradation on complex reasoning tasks — noticeable for multi-step logic","int4 quantization (aggressive compression) may require fine-tuning to recover performance — not recommended without validation","Quantization is asymmetric — inference is faster but model loading/conversion adds 5-10 seconds overhead on first run","Not all quantization backends are equally optimized — bitsandbytes is faster on NVIDIA GPUs, but ONNX quantization has better CPU support","Dynamic quantization (per-batch) is not supported — only static quantization schemes available"],"requires":["bitsandbytes 0.41+ (for GPU quantization) OR ONNX Runtime 1.14+ (for CPU/cross-platform)","PyTorch 2.0+ with quantization support","For mobile: ONNX or TensorFlow Lite export pipeline","Optional: calibration dataset for post-training quantization optimization"],"input_types":["pre-trained model weights (safetensors or PyTorch format)","quantization configuration (int8, int4, fp16 specification)"],"output_types":["quantized model weights (reduced size)","inference output (text) with minimal latency impact"],"categories":["text-generation-language","optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-meta-llama--llama-3.2-1b-instruct__cap_3","uri":"capability://text.generation.language.streaming.token.generation.with.early.stopping.and.sampling.control","name":"streaming token generation with early stopping and sampling control","description":"Generates text token-by-token with real-time streaming output, supporting configurable sampling strategies (temperature, top-k, top-p/nucleus sampling) and early stopping criteria (max tokens, stop sequences, repetition penalty). The implementation uses PyTorch's generate() API with custom callbacks to yield tokens as they are produced, enabling progressive output rendering in UI applications without waiting for full response completion.","intents":["Display real-time streaming responses in chat UIs to improve perceived responsiveness","Implement token-level sampling control to tune output diversity vs determinism","Stop generation early based on semantic criteria (e.g., when a stop token is reached) to reduce latency","Apply repetition penalties to reduce hallucinated repeated phrases in long-form generation"],"best_for":["web/mobile app developers building chat interfaces with streaming UI updates","teams implementing fine-grained output control for specific use cases (e.g., code generation, structured output)","researchers experimenting with sampling strategies and decoding algorithms"],"limitations":["Streaming adds ~50-100ms latency per token due to callback overhead — not suitable for ultra-low-latency applications","Stop sequences are matched at token level, not character level — may miss stops that span token boundaries","Repetition penalty is applied globally — cannot selectively penalize specific tokens or patterns","No built-in beam search or other advanced decoding strategies — only greedy and sampling-based decoding","Streaming state is not resumable — cannot pause and resume generation mid-sequence"],"requires":["PyTorch 2.0+ with generate() API","HuggingFace transformers 4.36+","Optional: custom callback implementation for non-standard streaming behavior"],"input_types":["prompt text (string or token IDs)","generation parameters (temperature, top_k, top_p, max_new_tokens, stop_sequences)"],"output_types":["streaming token IDs (via callback or iterator)","decoded text tokens (via post-processing)","full response text (after generation completes)"],"categories":["text-generation-language","streaming"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-meta-llama--llama-3.2-1b-instruct__cap_4","uri":"capability://text.generation.language.instruction.following.with.few.shot.in.context.learning","name":"instruction-following with few-shot in-context learning","description":"Follows natural language instructions and learns from few-shot examples provided in the prompt context without fine-tuning. The model uses attention mechanisms to extract task patterns from examples and apply them to new inputs, enabling zero-shot and few-shot task generalization across diverse tasks (summarization, translation, question-answering, code generation, etc.) within a single inference pass.","intents":["Adapt the model to new tasks by providing 2-5 examples in the prompt without retraining","Build flexible task pipelines that handle multiple task types with a single model instance","Implement dynamic task routing where task instructions are determined at runtime based on user input","Create prompt-based workflows that evolve without model redeployment"],"best_for":["product teams building flexible AI features that need to adapt to new tasks quickly","researchers exploring in-context learning and prompt engineering","developers building no-code/low-code AI applications where task logic is defined in prompts"],"limitations":["Few-shot learning performance degrades with task complexity — works well for classification/extraction but struggles with multi-step reasoning","Context window is limited (~8K tokens) — cannot include many examples or long reference documents simultaneously","Example quality significantly impacts performance — poor examples can degrade accuracy by 20-40%","No explicit task memory — each inference is independent; patterns learned from examples are not retained across requests","Instruction-following is not perfect — model may misinterpret ambiguous instructions or ignore subtle constraints"],"requires":["Well-structured prompt with clear instructions and examples","Understanding of prompt engineering best practices (e.g., example ordering, formatting consistency)","Optional: prompt optimization tools (e.g., DSPy, LangChain prompt templates) for systematic improvement"],"input_types":["natural language instructions (string)","few-shot examples (formatted as prompt text)","task input (text to be processed)"],"output_types":["task output (text, structured data, code, etc. depending on task)","confidence/uncertainty estimates (via logits if exposed)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-meta-llama--llama-3.2-1b-instruct__cap_5","uri":"capability://code.generation.editing.code.generation.and.completion.with.language.agnostic.patterns","name":"code generation and completion with language-agnostic patterns","description":"Generates and completes code across multiple programming languages (Python, JavaScript, Java, C++, Go, Rust, etc.) using patterns learned during instruction-tuning. The model understands code structure, syntax, and common idioms without language-specific fine-tuning, enabling both single-function completion and multi-file code generation from natural language descriptions.","intents":["Auto-complete code snippets in IDEs or code editors with context-aware suggestions","Generate boilerplate code from natural language descriptions (e.g., 'create a REST API endpoint')","Translate code between languages by providing source code and target language instruction","Explain code functionality by generating documentation from source code"],"best_for":["developers using lightweight code completion tools that run locally without cloud dependencies","teams building IDE extensions or editor plugins with on-device inference","educational platforms teaching programming with AI-assisted code generation"],"limitations":["Code generation quality is lower than specialized models like Codex or StarCoder — struggles with complex algorithms and multi-file dependencies","No built-in syntax validation — generated code may have syntax errors requiring manual correction","Limited understanding of project context — cannot access codebase structure or imports without explicit inclusion in prompt","Performance degrades for languages with less training data (e.g., Rust, Go) compared to Python/JavaScript","No support for interactive code refinement — each generation is independent without iterative improvement"],"requires":["Python 3.8+","HuggingFace transformers 4.36+","Optional: syntax highlighting/validation library for post-processing (e.g., tree-sitter, Pygments)"],"input_types":["natural language code description (string)","partial code with cursor position (for completion)","source code in one language (for translation/explanation)"],"output_types":["generated code (string, potentially multiple languages)","code completion suggestions (token-level or line-level)","code documentation/explanation (natural language)"],"categories":["code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-meta-llama--llama-3.2-1b-instruct__cap_6","uri":"capability://text.generation.language.text.summarization.with.controllable.length.and.style","name":"text summarization with controllable length and style","description":"Summarizes text documents by generating condensed versions with controllable output length (abstractive summarization) and style (e.g., bullet points, narrative, technical summary). The model uses instruction-tuning to interpret summarization directives in natural language, enabling users to specify summary length, focus areas, and formatting without model retraining.","intents":["Automatically generate executive summaries of long documents for quick review","Create multiple summary versions (short, medium, detailed) from a single source document","Extract key points from articles or research papers in user-specified formats","Reduce document processing time in content curation or information retrieval pipelines"],"best_for":["content platforms needing automatic summarization without external API dependencies","research teams processing large document collections","enterprise document management systems requiring on-device summarization"],"limitations":["Abstractive summarization may hallucinate facts not present in source — requires validation for factual accuracy","Context window limits summarization to ~8K tokens of input — cannot summarize very long documents without chunking","Summary quality degrades for domain-specific content (e.g., legal, medical) without fine-tuning","No explicit control over which facts are retained — model may omit important details in favor of general themes","Summarization style control is imprecise — 'bullet points' or 'narrative' instructions are interpreted loosely"],"requires":["Python 3.8+","HuggingFace transformers 4.36+","Optional: document chunking library (e.g., LangChain, Semantic Chunker) for long documents"],"input_types":["source text (string, up to ~8K tokens)","summarization instruction (e.g., 'summarize in 3 bullet points', 'create a technical summary')"],"output_types":["summary text (string)","structured summary (bullet points, JSON if explicitly requested)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-meta-llama--llama-3.2-1b-instruct__cap_7","uri":"capability://text.generation.language.content.translation.with.style.and.tone.preservation","name":"content translation with style and tone preservation","description":"Translates text between supported languages (EN, DE, FR, IT, PT, HI, ES, TH) while preserving style, tone, and cultural context. The model uses instruction-tuning to interpret translation directives (e.g., 'translate to formal Spanish', 'translate maintaining technical terminology') without requiring separate translation models or language-specific fine-tuning.","intents":["Translate user-generated content across multiple languages in real-time without external translation APIs","Preserve brand voice and tone in translated marketing or customer-facing content","Adapt translations for specific audiences (e.g., formal vs casual, technical vs general)","Build multilingual applications with on-device translation capabilities"],"best_for":["global SaaS platforms needing cost-effective translation without API dependencies","content creators localizing content across multiple markets","teams building multilingual applications with privacy requirements"],"limitations":["Translation quality is lower than specialized models (Google Translate, DeepL) — particularly for idiomatic expressions and cultural nuances","Language pairs are limited to supported languages — no translation between non-supported language pairs","Tone/style preservation is imprecise — model may not consistently maintain formal vs casual distinctions","Technical terminology may be mistranslated without domain-specific fine-tuning","No built-in quality assessment — cannot automatically detect mistranslations or confidence scores"],"requires":["Python 3.8+","HuggingFace transformers 4.36+","Optional: external language detection for automatic source language identification"],"input_types":["source text (string in any supported language)","target language specification (language code or name)","optional: style/tone directive (e.g., 'formal', 'casual', 'technical')"],"output_types":["translated text (string in target language)","optional: confidence/quality score (if using external validation)"],"categories":["text-generation-language","localization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-meta-llama--llama-3.2-1b-instruct__cap_8","uri":"capability://text.generation.language.question.answering.with.context.aware.retrieval.integration","name":"question-answering with context-aware retrieval integration","description":"Answers questions based on provided context documents or knowledge bases, using attention mechanisms to locate relevant information and generate coherent answers. The model supports both closed-book QA (answering from training knowledge) and open-book QA (answering from provided context), with optional integration points for external retrieval systems (RAG pipelines).","intents":["Build FAQ systems that answer user questions based on company documentation or knowledge bases","Create customer support chatbots that retrieve relevant help articles and generate answers","Implement document-based question-answering for research or legal document analysis","Enable users to ask questions about uploaded documents without manual indexing"],"best_for":["customer support teams automating FAQ handling","enterprise knowledge management systems","educational platforms providing AI-assisted tutoring"],"limitations":["Closed-book QA accuracy is limited by training data — model may hallucinate answers for questions outside training distribution","Context window limits the amount of context that can be provided — cannot answer questions requiring synthesis across many documents","No built-in retrieval — requires external RAG system (e.g., vector database, BM25 search) to identify relevant context","Answer quality degrades when context is ambiguous or contradictory — no explicit conflict resolution","No source attribution — cannot indicate which part of context was used to generate answer"],"requires":["Python 3.8+","HuggingFace transformers 4.36+","Optional: retrieval system (vector database like Pinecone, Weaviate, or open-source FAISS)","Optional: RAG framework (LangChain, LlamaIndex, Haystack) for orchestration"],"input_types":["question (string)","context documents (strings, up to ~8K tokens total)","optional: retrieval query for external knowledge base"],"output_types":["answer text (string)","optional: confidence score or source attribution"],"categories":["text-generation-language","search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-meta-llama--llama-3.2-1b-instruct__cap_9","uri":"capability://text.generation.language.structured.output.generation.with.json.schema.compliance","name":"structured output generation with json/schema compliance","description":"Generates structured outputs (JSON, YAML, CSV) that conform to user-specified schemas or formats through instruction-tuning and prompt engineering. The model interprets schema descriptions in natural language and generates outputs matching the specified structure, enabling integration with downstream systems that require structured data without custom parsing logic.","intents":["Extract structured data from unstructured text (e.g., extract entities into JSON format)","Generate API responses in specific JSON schemas without manual formatting","Create structured reports or data exports from natural language descriptions","Enable LLM outputs to integrate directly with databases or APIs expecting structured data"],"best_for":["developers building data extraction pipelines","teams integrating LLM outputs with structured databases or APIs","data engineering teams automating ETL processes with LLM-based extraction"],"limitations":["Schema compliance is not guaranteed — model may generate invalid JSON or missing required fields without explicit validation","Complex nested schemas may confuse the model — performance degrades with schema depth and field count","No built-in schema validation — requires external JSON schema validator to ensure compliance","Field ordering and formatting may not match expected output exactly — requires post-processing normalization","Large schemas (>50 fields) may exceed context window or cause generation errors"],"requires":["Python 3.8+","HuggingFace transformers 4.36+","JSON schema validation library (e.g., jsonschema, pydantic)","Optional: prompt engineering framework (e.g., DSPy, Outlines) for schema-guided generation"],"input_types":["unstructured text (string)","schema specification (JSON schema, natural language description, or example)","optional: output format directive (JSON, YAML, CSV)"],"output_types":["structured output (JSON, YAML, CSV string)","optional: validation errors if schema compliance fails"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-meta-llama--llama-3.2-1b-instruct__headline","uri":"capability://text.generation.language.text.generation.model.for.chatbots.and.assistants","name":"text-generation model for chatbots and assistants","description":"Llama-3.2-1B-Instruct is a powerful text-generation model designed for creating conversational agents and chatbots, enabling developers to build interactive and responsive applications in multiple languages.","intents":["best text-generation model for chatbots","text-generation model for conversational AI","top models for building virtual assistants","AI models for generating human-like text","best open-source chatbot models"],"best_for":["chatbot development","conversational AI applications"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":54,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","PyTorch 2.0+ or ONNX Runtime 1.14+","4GB+ RAM for model weights in float32 (2GB with int8 quantization)","HuggingFace transformers library 4.36+","Optional: CUDA 11.8+ for GPU acceleration (CPU inference supported but ~10x slower)","HuggingFace transformers 4.36+ with multilingual tokenizer support","Language-specific Unicode support in runtime environment","Optional: external language detection library (e.g., langdetect, fasttext) for automatic language routing","HuggingFace transformers 4.36+","Message formatting following model's expected dialogue format (system/user/assistant roles)"],"failure_modes":["1B parameters limits reasoning depth and factual accuracy compared to 7B+ models — struggles with complex multi-step logic","No built-in retrieval augmentation — cannot access external knowledge bases or real-time information without explicit integration","Context window limited to ~8K tokens — cannot maintain coherence over very long conversation histories","Single-GPU inference only — no native distributed inference support for batching across multiple devices","Instruction-tuning optimized for English; multilingual support (DE, FR, IT, PT, HI, ES, TH) is degraded vs monolingual models","Language quality is not uniform — English and major European languages (DE, FR, ES) perform well, but Hindi and Thai show degraded fluency and grammatical accuracy","No explicit language detection — requires external language identification or user-specified language parameter","Code-switching (mixing languages in single response) is not explicitly handled — may produce inconsistent output for multilingual inputs","Tokenization efficiency varies by language — Thai and Hindi require more tokens per semantic unit, increasing inference latency by 15-30%","Context window limits conversation length to ~8K tokens — long conversations require message pruning or summarization","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.8886945151476884,"quality":0.34,"ecosystem":0.5000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.35,"quality":0.2,"ecosystem":0.1,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:22.765Z","last_scraped_at":"2026-05-03T14:22:48.039Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":6171370,"model_likes":1392}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=meta-llama--llama-3.2-1b-instruct","compare_url":"https://unfragile.ai/compare?artifact=meta-llama--llama-3.2-1b-instruct"}},"signature":"dFgshg5UMMftprxqMy3a8k3oa0JeOFcwHj7UfDVh+3Wa2j5Oy/YCP7qGNSLGp8W5InYQio7bZa6Jd2CpHD/zAA==","signedAt":"2026-06-22T05:22:32.926Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/meta-llama--llama-3.2-1b-instruct","artifact":"https://unfragile.ai/meta-llama--llama-3.2-1b-instruct","verify":"https://unfragile.ai/api/v1/verify?slug=meta-llama--llama-3.2-1b-instruct","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}