{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-meta-llama--llama-3.2-3b-instruct","slug":"meta-llama--llama-3.2-3b-instruct","name":"Llama-3.2-3B-Instruct","type":"model","url":"https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct","page_url":"https://unfragile.ai/meta-llama--llama-3.2-3b-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-3.2-3b-instruct__cap_0","uri":"capability://text.generation.language.instruction.following.text.generation.with.multi.turn.conversation.support","name":"instruction-following text generation with multi-turn conversation support","description":"Generates coherent text responses to natural language instructions using a transformer-based decoder architecture trained on instruction-following data. The model uses causal language modeling with attention masking to maintain conversation context across multiple turns, enabling stateful dialogue without explicit memory management. Implements grouped-query attention (GQA) for efficient inference on resource-constrained hardware while maintaining output quality comparable to larger models.","intents":["Build a conversational chatbot that understands multi-turn dialogue without losing context","Deploy a lightweight language model on edge devices or CPU-only environments","Create a task-specific assistant that follows detailed instructions and maintains coherent responses"],"best_for":["Solo developers building local-first LLM applications without cloud dependencies","Teams deploying inference on edge devices, mobile, or resource-constrained servers","Researchers prototyping instruction-tuned model behavior with full model transparency"],"limitations":["Context window limited to 8,192 tokens — long documents require chunking or summarization preprocessing","No built-in memory persistence across sessions — conversation history must be managed externally","Inference latency on CPU ranges 5-15 tokens/second depending on hardware; GPU acceleration required for production throughput","Knowledge cutoff date limits factual accuracy on recent events; no built-in retrieval-augmented generation (RAG) integration"],"requires":["Python 3.8+","PyTorch 2.0+ or compatible framework (transformers library 4.36+)","4GB+ RAM for quantized inference, 8GB+ for full precision (bfloat16)","Optional: CUDA 11.8+ for GPU acceleration, or compatible accelerator (Metal for Apple Silicon)"],"input_types":["plain text (UTF-8 encoded)","structured prompts with system/user/assistant role markers","code snippets and technical documentation"],"output_types":["plain text responses","code generation (Python, JavaScript, SQL, etc.)","structured text (JSON, YAML, markdown)"],"categories":["text-generation-language","conversational-ai"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-meta-llama--llama-3.2-3b-instruct__cap_1","uri":"capability://text.generation.language.multilingual.text.generation.across.9.languages","name":"multilingual text generation across 9 languages","description":"Generates fluent text in English, German, French, Italian, Portuguese, Hindi, Spanish, Thai, and Chinese through shared transformer embeddings trained on multilingual instruction-following corpora. The model uses a single tokenizer (shared vocabulary) across all languages, enabling code-switching and cross-lingual transfer without language-specific model variants. Achieves language-specific performance through instruction-based prompting (e.g., 'Respond in Spanish:') rather than separate model weights.","intents":["Build a single chatbot that serves users across multiple languages without deploying separate models","Generate multilingual content (documentation, customer support, localization) from a single inference endpoint","Create code-switching applications where users mix languages in a single conversation"],"best_for":["International SaaS platforms needing cost-effective multilingual support without model duplication","Content creators generating documentation or marketing copy in multiple languages","Developers building chatbots for non-English-speaking regions with limited compute budgets"],"limitations":["Performance degrades for low-resource languages (Hindi, Thai) compared to English — ~5-10% lower BLEU scores on translation benchmarks","Tokenizer efficiency varies by language; Thai and Chinese require 1.3-1.5x more tokens than English for equivalent semantic content","No explicit language detection — model relies on user-provided language hints in prompts; ambiguous prompts may produce code-switched output","Limited cultural adaptation — responses reflect training data biases and may not account for regional context or preferences"],"requires":["Python 3.8+","transformers library 4.36+ with multilingual tokenizer support","4GB+ RAM (multilingual embeddings add ~200MB overhead vs English-only models)"],"input_types":["plain text in any of the 9 supported languages","mixed-language prompts (code-switching)","language-tagged instructions (e.g., '[DE] Antworte auf Deutsch:')"],"output_types":["plain text in target language","code with multilingual comments","structured data with language-specific formatting"],"categories":["text-generation-language","multilingual-nlp"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-meta-llama--llama-3.2-3b-instruct__cap_2","uri":"capability://text.generation.language.efficient.inference.through.quantization.friendly.architecture","name":"efficient inference through quantization-friendly architecture","description":"Supports multiple quantization schemes (int8, int4, bfloat16, float16) without retraining through a quantization-aware architecture using grouped-query attention and normalized layer designs. The model's 3B parameter count and GQA design reduce KV cache memory requirements, enabling 4-bit quantization with minimal quality loss. Inference frameworks (llama.cpp, vLLM, TensorRT-LLM) can apply post-training quantization without model-specific tuning.","intents":["Deploy the model on consumer GPUs (RTX 3060, RTX 4060) or CPU with acceptable latency","Run inference on mobile devices or edge hardware with <2GB memory footprint","Reduce inference costs by 60-80% through quantization while maintaining acceptable output quality"],"best_for":["Startups and indie developers with limited GPU budgets seeking cost-effective inference","Edge computing teams deploying models on IoT devices, mobile, or on-premise servers","Teams optimizing inference latency for real-time applications (chatbots, code completion)"],"limitations":["int4 quantization reduces output quality by ~3-5% on reasoning benchmarks compared to bfloat16 baseline","Quantization requires framework-specific implementations — int4 in llama.cpp differs from vLLM's approach, limiting portability","KV cache quantization (int8) introduces ~2-3% accuracy loss on long-context tasks (>4K tokens)","No official quantization guidelines from Meta — community-maintained quantization scripts may introduce inconsistencies"],"requires":["Python 3.8+ with transformers 4.36+ or llama.cpp/vLLM","For int4: 2-3GB VRAM or RAM (vs 6GB for bfloat16)","Optional: CUDA 11.8+ for GPU quantization, or CPU-only inference (slower)"],"input_types":["text prompts (any length up to 8K tokens)","structured prompts with role markers"],"output_types":["text responses","code generation","structured outputs"],"categories":["text-generation-language","model-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-meta-llama--llama-3.2-3b-instruct__cap_3","uri":"capability://code.generation.editing.code.generation.and.technical.reasoning","name":"code generation and technical reasoning","description":"Generates syntactically correct code across multiple programming languages (Python, JavaScript, SQL, Bash, C++, Java) through instruction-tuning on code-specific datasets and reasoning tasks. The model uses causal attention to maintain code structure and indentation, and is trained on problem-solving patterns that enable multi-step reasoning for algorithm design and debugging. Supports code-in-context learning where examples in the prompt guide output format and style.","intents":["Generate code snippets from natural language descriptions for rapid prototyping","Assist with debugging by analyzing error messages and suggesting fixes","Create SQL queries, shell scripts, or infrastructure-as-code from specifications"],"best_for":["Solo developers using code generation to accelerate development velocity","Teams building internal code generation tools or IDE plugins","Educators using the model to generate coding examples and explanations"],"limitations":["Code generation quality degrades for complex algorithms or multi-file projects — single-file generation is most reliable","No built-in syntax validation — generated code may contain logical errors or edge-case bugs requiring human review","Limited understanding of project-specific libraries or frameworks — requires in-context examples to generate idiomatic code","No codebase awareness — cannot reference existing code or maintain consistency across generated files without external context management"],"requires":["Python 3.8+","transformers library 4.36+","4GB+ RAM for inference"],"input_types":["natural language code specifications","code snippets with comments","error messages and stack traces","problem descriptions with examples"],"output_types":["code in Python, JavaScript, SQL, Bash, C++, Java, and other languages","code with inline comments and docstrings","debugging suggestions and explanations"],"categories":["code-generation-editing","technical-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-meta-llama--llama-3.2-3b-instruct__cap_4","uri":"capability://text.generation.language.few.shot.learning.through.in.context.examples","name":"few-shot learning through in-context examples","description":"Adapts behavior to new tasks by learning from examples provided in the prompt context without requiring model fine-tuning or retraining. The model uses attention mechanisms to identify patterns in provided examples and apply them to new inputs, enabling task adaptation within the 8K token context window. Supports multiple example formats (input-output pairs, step-by-step reasoning, code patterns) and automatically generalizes to unseen variations.","intents":["Adapt the model to domain-specific tasks (e.g., customer support, medical terminology) by providing 2-5 examples","Create task-specific prompts that guide output format without model retraining","Implement zero-shot and few-shot evaluation benchmarks to measure model generalization"],"best_for":["Researchers evaluating model generalization and few-shot learning capabilities","Teams building prompt-based applications that need to adapt to new domains without retraining","Developers prototyping task-specific behaviors before committing to fine-tuning"],"limitations":["Few-shot performance plateaus after 5-10 examples — additional examples provide diminishing returns and consume context tokens","In-context learning is less effective than fine-tuning for complex tasks requiring deep domain knowledge","Example order and formatting significantly impact output quality — requires careful prompt engineering","Context window limit (8K tokens) restricts the number of examples and input length, limiting applicability to long-document tasks"],"requires":["Python 3.8+","transformers library 4.36+","Understanding of prompt engineering and example formatting"],"input_types":["natural language examples with input-output pairs","code examples demonstrating desired output format","step-by-step reasoning examples"],"output_types":["text responses following example patterns","code following example style and structure","structured outputs matching example format"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-meta-llama--llama-3.2-3b-instruct__cap_5","uri":"capability://planning.reasoning.reasoning.and.chain.of.thought.decomposition","name":"reasoning and chain-of-thought decomposition","description":"Generates step-by-step reasoning chains that decompose complex problems into intermediate steps, improving accuracy on multi-step reasoning tasks. The model is trained on chain-of-thought (CoT) examples that demonstrate explicit reasoning before providing final answers. Supports both implicit reasoning (internal model computation) and explicit reasoning (generating intermediate steps in output) through instruction-based prompting.","intents":["Solve math problems, logic puzzles, and reasoning tasks by generating intermediate steps","Improve output reliability on complex tasks by requesting explicit reasoning before final answers","Debug model reasoning by inspecting intermediate steps and identifying error sources"],"best_for":["Researchers studying model reasoning capabilities and failure modes","Teams building applications requiring explainable AI and transparent decision-making","Educators using the model to teach problem-solving approaches through generated examples"],"limitations":["Reasoning quality degrades on tasks requiring specialized domain knowledge (advanced mathematics, physics) — general reasoning patterns may not apply","Explicit reasoning increases token generation by 2-5x, raising inference latency and cost","Model may generate plausible but incorrect intermediate steps (hallucinated reasoning) — explicit reasoning does not guarantee correctness","Context window limit restricts reasoning depth — very complex problems may exceed 8K token budget for reasoning + output"],"requires":["Python 3.8+","transformers library 4.36+","Prompts explicitly requesting step-by-step reasoning (e.g., 'Think step by step:')"],"input_types":["math problems and logic puzzles","multi-step reasoning tasks","questions requiring explanation and justification"],"output_types":["step-by-step reasoning chains","intermediate calculations and logic steps","final answers with supporting reasoning"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-meta-llama--llama-3.2-3b-instruct__cap_6","uri":"capability://safety.moderation.safety.aligned.response.generation.with.refusal.patterns","name":"safety-aligned response generation with refusal patterns","description":"Generates responses that avoid harmful content through instruction-tuning on safety examples and constitutional AI principles. The model learns to recognize unsafe requests (illegal activities, violence, hate speech, sexual content) and decline them with explanatory refusals rather than generating harmful content. Safety alignment is achieved through supervised fine-tuning on safety examples and reinforcement learning from human feedback (RLHF), not through post-hoc filtering.","intents":["Deploy the model in production with reduced risk of generating harmful content","Build customer-facing applications that require safety guarantees and compliance","Evaluate model safety through adversarial prompts and red-teaming"],"best_for":["Teams building public-facing chatbots and customer support systems","Compliance-focused organizations requiring documented safety measures","Researchers studying model alignment and safety evaluation"],"limitations":["Safety alignment is probabilistic — adversarial prompts or jailbreak attempts may still generate unsafe content in rare cases","Refusal patterns may be overly conservative, declining legitimate requests (e.g., educational content on sensitive topics)","Safety training reflects Meta's values and policies — may not align with all organizational or cultural contexts","No transparency into specific safety training data or RLHF procedures — difficult to audit or customize safety behavior"],"requires":["Python 3.8+","transformers library 4.36+","Understanding of model limitations and need for human review in high-stakes applications"],"input_types":["any text input, including adversarial or unsafe prompts"],"output_types":["safe responses to legitimate requests","refusals with explanations for unsafe requests"],"categories":["safety-moderation","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-meta-llama--llama-3.2-3b-instruct__cap_7","uri":"capability://text.generation.language.long.context.understanding.and.summarization","name":"long-context understanding and summarization","description":"Processes and summarizes documents up to 8,192 tokens through causal attention and instruction-tuning on summarization tasks. The model maintains coherence across long sequences by using grouped-query attention to reduce computational complexity, enabling efficient processing of multi-page documents, code files, and conversation histories. Supports extractive and abstractive summarization through instruction-based prompting.","intents":["Summarize long documents, articles, or code files into concise overviews","Extract key information from lengthy conversations or meeting transcripts","Analyze multi-file codebases by processing concatenated source code within context window"],"best_for":["Content creators and researchers processing large volumes of text","Teams building document analysis and knowledge extraction tools","Developers analyzing code repositories and generating documentation"],"limitations":["Context window limited to 8,192 tokens — documents exceeding this length require chunking or hierarchical summarization","Summarization quality degrades for documents with complex structure or multiple topics — single-topic documents perform best","Long-context inference is slower than short-context (2-3x latency increase for 8K tokens vs 512 tokens)","Model may lose important details when summarizing highly technical content — human review recommended for critical applications"],"requires":["Python 3.8+","transformers library 4.36+","4GB+ RAM for inference","Text preprocessing to handle document encoding and chunking"],"input_types":["plain text documents (UTF-8 encoded)","code files and source code","conversation histories with role markers","structured documents (markdown, HTML stripped to text)"],"output_types":["abstractive summaries","extractive summaries (key sentences)","structured summaries (bullet points, JSON)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-meta-llama--llama-3.2-3b-instruct__cap_8","uri":"capability://text.generation.language.instruction.following.with.structured.output.formatting","name":"instruction-following with structured output formatting","description":"Generates structured outputs (JSON, YAML, CSV, XML) that conform to user-specified schemas through instruction-tuning on structured data generation tasks. The model learns to parse format specifications from prompts and generate valid structured outputs without external validation or post-processing. Supports schema-based prompting where users provide examples or formal specifications (e.g., 'Output as JSON with fields: name, age, email').","intents":["Extract structured data from unstructured text (e.g., convert customer emails to JSON records)","Generate configuration files, API responses, or data exports in specific formats","Create machine-readable outputs that integrate with downstream systems without parsing errors"],"best_for":["Data engineers building ETL pipelines that require structured output from language models","API developers generating structured responses from natural language inputs","Teams automating data extraction and transformation workflows"],"limitations":["Structured output generation is not guaranteed to be valid — JSON may have syntax errors, missing fields, or type mismatches requiring post-processing validation","Complex schemas with many fields or nested structures increase error rates — simple, flat schemas perform best","Model may hallucinate fields or values not present in input — requires careful prompt engineering and validation","No built-in schema validation or error recovery — external tools (JSON schema validators) required for production use"],"requires":["Python 3.8+","transformers library 4.36+","JSON schema validation library (e.g., jsonschema) for output validation","Careful prompt engineering to specify desired output format"],"input_types":["natural language text","unstructured data (emails, documents, logs)","format specifications (JSON schema, examples)"],"output_types":["JSON objects and arrays","YAML documents","CSV rows","XML elements","structured text (markdown tables, key-value pairs)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":52,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","PyTorch 2.0+ or compatible framework (transformers library 4.36+)","4GB+ RAM for quantized inference, 8GB+ for full precision (bfloat16)","Optional: CUDA 11.8+ for GPU acceleration, or compatible accelerator (Metal for Apple Silicon)","transformers library 4.36+ with multilingual tokenizer support","4GB+ RAM (multilingual embeddings add ~200MB overhead vs English-only models)","Python 3.8+ with transformers 4.36+ or llama.cpp/vLLM","For int4: 2-3GB VRAM or RAM (vs 6GB for bfloat16)","Optional: CUDA 11.8+ for GPU quantization, or CPU-only inference (slower)","transformers library 4.36+"],"failure_modes":["Context window limited to 8,192 tokens — long documents require chunking or summarization preprocessing","No built-in memory persistence across sessions — conversation history must be managed externally","Inference latency on CPU ranges 5-15 tokens/second depending on hardware; 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