{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"llama-3-3-70b","slug":"llama-3-3-70b","name":"Llama 3.3 70B","type":"model","url":"https://www.llama.com/","page_url":"https://unfragile.ai/llama-3-3-70b","categories":["model-training"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"llama-3-3-70b__cap_0","uri":"capability://text.generation.language.general.purpose.text.generation.with.instruction.following","name":"general-purpose text generation with instruction following","description":"Autoregressive transformer decoder that generates coherent multi-turn text responses up to 128K token context windows. Uses improved instruction-following mechanisms (vs. Llama 3.1) to better parse and execute user directives, with training optimized for both zero-shot and few-shot prompting patterns. Processes text sequentially, predicting the next token based on preceding context using standard causal attention masking across 70B parameters.","intents":["Generate natural language responses to open-ended questions and prompts","Build chatbot or conversational AI systems without fine-tuning","Create long-form content (essays, stories, documentation) with coherent multi-paragraph output","Execute complex multi-step instructions in a single prompt"],"best_for":["Enterprise teams building self-hosted LLM applications","Developers prioritizing cost-efficiency over maximum capability","Organizations requiring permissive commercial licensing"],"limitations":["Text-only input; no native image understanding or multimodal reasoning","128K context window hard limit may truncate very long documents or conversation histories","Performance claims (matching 405B) are Meta's claims; independent verification not provided in documentation","Specific failure modes, hallucination rates, and edge cases not documented"],"requires":["GPU with sufficient VRAM for 70B parameter model (specific VRAM requirements not documented; typically 40-80GB for fp16)","Inference framework supporting transformer models (vLLM, llama.cpp, TensorRT-LLM, or similar)","Meta community license acceptance for commercial deployment"],"input_types":["text (natural language prompts, instructions, few-shot examples)"],"output_types":["text (generated continuations, responses, structured text)"],"categories":["text-generation-language","instruction-following"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llama-3-3-70b__cap_1","uri":"capability://text.generation.language.multilingual.text.generation.across.8.languages","name":"multilingual text generation across 8 languages","description":"Transformer model trained on multilingual corpora supporting text generation, translation, and instruction following in 8 distinct languages. Uses shared embedding and attention layers across language pairs, allowing the model to generalize instruction-following patterns across languages without language-specific fine-tuning. Specific languages supported are not enumerated in documentation but include major global languages.","intents":["Generate content in non-English languages without separate model deployment","Build multilingual chatbots and customer support systems from a single model","Translate or rephrase content across supported language pairs","Support international teams with localized LLM applications"],"best_for":["Global enterprises requiring multilingual AI without managing multiple models","Teams building products for non-English-speaking markets","Organizations seeking to reduce infrastructure complexity via single-model deployment"],"limitations":["Only 8 languages supported; specific language list not documented","Performance across languages not benchmarked individually; MMLU/HumanEval scores likely represent English-dominant performance","No documented translation quality metrics or language-specific capability analysis","Cross-lingual transfer quality unknown for low-resource language pairs"],"requires":["Input text in one of 8 supported languages (language list not specified)","Same GPU/inference framework requirements as base text generation capability"],"input_types":["text (prompts, instructions, content in supported languages)"],"output_types":["text (generated or translated content in supported languages)"],"categories":["text-generation-language","translation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llama-3-3-70b__cap_10","uri":"capability://text.generation.language.prompt.engineering.and.few.shot.learning.for.task.adaptation","name":"prompt engineering and few-shot learning for task adaptation","description":"Supports in-context learning through few-shot prompting, where task examples are provided in the prompt to guide model behavior without fine-tuning. Improved instruction-following (vs. Llama 3.1) enables more reliable parsing of complex prompt structures, chain-of-thought reasoning patterns, and structured output formats. Model learns task patterns from examples and applies them to new inputs within the same context window, enabling rapid task adaptation without training.","intents":["Adapt model behavior to new tasks by providing in-context examples","Implement chain-of-thought reasoning by structuring prompts with reasoning steps","Generate structured outputs (JSON, CSV, tables) through prompt formatting","Improve accuracy on domain-specific tasks without fine-tuning"],"best_for":["Developers rapidly prototyping new LLM applications","Teams without ML infrastructure for fine-tuning","Applications requiring dynamic task adaptation"],"limitations":["Few-shot learning quality depends heavily on example selection and prompt engineering","Performance may degrade with very different test cases vs. training examples","Prompt engineering requires experimentation and domain expertise","No systematic method for optimal example selection documented"],"requires":["Prompt engineering expertise to design effective few-shot examples","Understanding of model behavior and instruction-following patterns","Validation pipeline to assess few-shot performance on target tasks"],"input_types":["text (few-shot examples, task descriptions, input data)"],"output_types":["text (task-specific outputs, structured data)"],"categories":["text-generation-language","prompt-engineering"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llama-3-3-70b__cap_11","uri":"capability://automation.workflow.inference.optimization.and.batching.for.throughput.scaling","name":"inference optimization and batching for throughput scaling","description":"Supports batch inference and token-level optimization through compatible inference frameworks (vLLM with paged attention, TensorRT-LLM, llama.cpp). These frameworks implement continuous batching, KV-cache optimization, and attention kernel optimizations to maximize throughput on GPU hardware. Enables high-throughput serving scenarios where multiple requests are processed simultaneously, with automatic scheduling and memory management to maximize GPU utilization.","intents":["Scale inference throughput for high-volume production deployments","Minimize latency for batch processing of multiple requests","Optimize GPU memory utilization for cost-effective inference","Build scalable API services with multiple concurrent requests"],"best_for":["Production deployments requiring high throughput (100+ requests/second)","Batch processing pipelines with multiple inference requests","Cost-optimized inference infrastructure"],"limitations":["Specific throughput benchmarks not provided; performance depends on hardware and framework","Batching introduces queuing latency for individual requests","Memory optimization techniques may reduce per-request performance","Framework-specific optimizations vary in effectiveness"],"requires":["Inference framework with batching support (vLLM, TensorRT-LLM, or similar)","GPU hardware with sufficient VRAM for batch processing","Load balancing and request scheduling infrastructure"],"input_types":["text (batch of prompts)"],"output_types":["text (batch of generated responses)"],"categories":["automation-workflow","performance-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llama-3-3-70b__cap_2","uri":"capability://code.generation.editing.code.generation.and.completion.with.88.4.humaneval.performance","name":"code generation and completion with 88.4% humaneval performance","description":"Transformer decoder trained on code corpora and instruction-following datasets, generating syntactically valid code across multiple programming languages. Achieves 88.4% pass rate on HumanEval benchmark (function-level code generation from docstrings). Uses standard causal attention and next-token prediction to generate code sequences, with training optimized for both standalone function generation and multi-file code context understanding.","intents":["Generate code functions from natural language descriptions or docstrings","Complete code snippets or fill in missing function implementations","Build AI-assisted development tools or IDE plugins","Automate routine coding tasks in software development workflows"],"best_for":["Development teams building internal code generation tools","Solo developers using self-hosted LLM for local code completion","Organizations with strict data residency requirements (self-hosted deployment)"],"limitations":["HumanEval benchmark measures function-level generation; multi-file codebase understanding not benchmarked","No documented support for IDE-specific features (inline completion, refactoring, debugging)","Code generation quality varies by language; benchmark scores likely skew toward Python","No built-in syntax validation or error correction; generated code may contain logical errors"],"requires":["Inference framework with streaming token support for real-time code completion UX","Integration layer to parse code context and format prompts for code generation","Same GPU/compute requirements as base text generation"],"input_types":["text (function docstrings, code comments, natural language descriptions, partial code)"],"output_types":["text (generated code in Python, JavaScript, Java, C++, and other languages)"],"categories":["code-generation-editing","code-completion"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llama-3-3-70b__cap_3","uri":"capability://data.processing.analysis.synthetic.data.generation.for.model.training.and.evaluation","name":"synthetic data generation for model training and evaluation","description":"Generates diverse, high-quality synthetic datasets by prompting the model to produce training examples, instruction-response pairs, or evaluation data. Uses the model's instruction-following and text generation capabilities to create labeled data at scale without manual annotation. Supports templated prompting and few-shot examples to control synthetic data distribution and quality. Commonly paired with Meta's Synthetic Data Toolkit for systematic generation workflows.","intents":["Generate training data for fine-tuning smaller models or domain-specific LLMs","Create evaluation datasets for benchmarking and testing without manual labeling","Produce diverse instruction-response pairs for instruction-tuning custom models","Scale data collection for machine learning projects without annotation costs"],"best_for":["ML teams building domain-specific models and needing training data at scale","Researchers evaluating model behavior across synthetic scenarios","Organizations with limited budgets for manual data annotation"],"limitations":["Synthetic data may exhibit model biases or distribution artifacts from Llama 3.3's training","Quality and diversity of synthetic data not independently benchmarked","No built-in deduplication, filtering, or quality assurance mechanisms documented","Synthetic data may not cover edge cases or adversarial scenarios as effectively as human-curated data"],"requires":["Prompt engineering expertise to design generation templates and few-shot examples","Downstream validation pipeline to assess synthetic data quality before use","Meta Synthetic Data Toolkit (optional but recommended for systematic generation)"],"input_types":["text (generation templates, seed examples, task descriptions)"],"output_types":["text (synthetic training examples, instruction-response pairs, evaluation datasets)"],"categories":["data-processing-analysis","synthetic-data-generation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llama-3-3-70b__cap_4","uri":"capability://text.generation.language.long.context.reasoning.with.128k.token.window","name":"long-context reasoning with 128k token window","description":"Supports processing and reasoning over documents, conversations, or code repositories up to 128K tokens (~96K words) in a single context window. Uses standard transformer attention mechanisms with position embeddings optimized for long sequences, enabling the model to maintain coherence and reference information across extended contexts without chunking or retrieval augmentation. Enables tasks like full-document analysis, long conversation history understanding, and multi-file code reasoning.","intents":["Analyze entire documents or research papers in a single prompt without summarization","Maintain coherent multi-turn conversations with full history without context loss","Reason over large codebases or multiple source files simultaneously","Extract information or answer questions about long-form content without external retrieval"],"best_for":["Enterprise applications requiring document analysis without external RAG systems","Conversational AI systems with long interaction histories","Code analysis and refactoring tools working with large repositories"],"limitations":["128K token hard limit; documents or conversations exceeding this length require chunking or summarization","Attention computation scales quadratically with context length, increasing latency for maximum-length inputs","Long-context performance not independently benchmarked; quality degradation at extreme lengths unknown","Position embedding extrapolation beyond training context may degrade performance"],"requires":["Inference framework supporting long-context inference (vLLM with paged attention, llama.cpp with appropriate settings)","Sufficient GPU VRAM to hold full context in memory (typically 80-120GB for fp16 at 128K tokens)","Prompt engineering to structure long contexts for optimal model performance"],"input_types":["text (documents, conversation histories, code files, up to 128K tokens)"],"output_types":["text (analysis, answers, reasoning over full context)"],"categories":["text-generation-language","long-context-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llama-3-3-70b__cap_5","uri":"capability://code.generation.editing.fine.tuning.and.adaptation.for.domain.specific.tasks","name":"fine-tuning and adaptation for domain-specific tasks","description":"Supports fine-tuning the 70B parameter model on custom datasets to adapt it for specific domains, tasks, or instruction styles. Meta provides fine-tuning documentation and guides, though specific fine-tuning methodology (LoRA, full-parameter, QLoRA) is not detailed in provided materials. Enables organizations to customize the model's behavior, knowledge, and output format without training from scratch. Fine-tuned models can be deployed self-hosted with the same inference infrastructure as the base model.","intents":["Adapt Llama 3.3 to domain-specific terminology and knowledge (legal, medical, financial)","Customize instruction-following behavior for proprietary task formats","Reduce hallucination or improve accuracy on specialized domains through targeted training","Create organization-specific model variants without managing multiple base models"],"best_for":["Enterprise teams with domain-specific data and ML expertise","Organizations building proprietary AI products requiring customization","Teams with sufficient compute resources for fine-tuning (GPU clusters or cloud training)"],"limitations":["Specific fine-tuning methodology not documented (LoRA vs. full-parameter vs. QLoRA unknown)","Hardware requirements for fine-tuning not specified; likely requires multiple GPUs","No documented best practices for dataset size, learning rates, or convergence criteria","Fine-tuning may degrade performance on out-of-domain tasks (catastrophic forgetting)"],"requires":["Custom training dataset in target domain (size and format requirements not specified)","GPU cluster or cloud training infrastructure (specific requirements unknown)","Meta fine-tuning guides and documentation","ML expertise for hyperparameter tuning and evaluation"],"input_types":["text (training examples, instruction-response pairs, domain-specific data)"],"output_types":["model (fine-tuned Llama 3.3 weights, deployable via same inference frameworks)"],"categories":["code-generation-editing","model-customization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llama-3-3-70b__cap_6","uri":"capability://code.generation.editing.quantization.and.model.compression.for.efficient.deployment","name":"quantization and model compression for efficient deployment","description":"Supports quantization techniques (int8, int4, and other formats) to reduce model size and memory footprint for deployment on resource-constrained hardware. Quantized versions are available in formats like GGUF (for llama.cpp) and other serialization formats, enabling inference on consumer GPUs, CPUs, and edge devices. Quantization trades off some precision for dramatic reductions in VRAM requirements and inference latency, with specific format options and quality trade-offs not detailed in documentation.","intents":["Deploy Llama 3.3 on consumer GPUs (RTX 4090, RTX 4080) with limited VRAM","Run inference on CPU-only systems or edge devices for latency-sensitive applications","Reduce infrastructure costs for high-throughput inference deployments","Enable local, offline inference without cloud API dependencies"],"best_for":["Developers building local AI tools or desktop applications","Edge deployment scenarios (on-device inference, IoT)","Cost-conscious teams optimizing inference infrastructure"],"limitations":["Specific quantization formats available for Llama 3.3 not enumerated (GGUF, safetensors, int8, int4 support unknown)","Quality degradation from quantization not benchmarked; performance loss at different bit-widths unknown","Quantized models may lose capability on complex reasoning tasks","Quantization format compatibility varies across inference frameworks"],"requires":["Quantized model weights in supported format (GGUF, safetensors, etc.)","Inference framework supporting quantized models (llama.cpp for GGUF, vLLM with AWQ/GPTQ, Ollama)","Hardware with sufficient VRAM for quantized model (typically 8-24GB for int4 quantization)"],"input_types":["text (same as base model)"],"output_types":["text (same as base model, with potential quality degradation)"],"categories":["code-generation-editing","model-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llama-3-3-70b__cap_7","uri":"capability://automation.workflow.self.hosted.deployment.with.permissive.commercial.licensing","name":"self-hosted deployment with permissive commercial licensing","description":"Available under Meta's permissive community license enabling unrestricted self-hosted deployment for both research and commercial applications. Model weights are freely downloadable from Meta and partner platforms (Hugging Face, Kaggle), with no usage restrictions, API quotas, or vendor lock-in. Organizations retain full control over model execution, data privacy, and infrastructure, with no telemetry or usage tracking by Meta.","intents":["Deploy LLM infrastructure without cloud vendor dependencies or API costs","Build AI products with full data privacy and no external API calls","Maintain complete control over model behavior, updates, and infrastructure","Avoid licensing restrictions or usage-based pricing models"],"best_for":["Enterprise organizations with strict data residency or privacy requirements","Teams building commercial AI products without API licensing costs","Organizations seeking vendor independence and infrastructure control"],"limitations":["Requires in-house ML infrastructure and DevOps expertise","No official SLA, support, or uptime guarantees (unlike commercial API providers)","Responsibility for security, updates, and model monitoring falls on deploying organization","Inference performance and cost optimization require infrastructure tuning"],"requires":["GPU infrastructure (single H100/A100 or multiple consumer GPUs for quantized versions)","Inference framework (vLLM, llama.cpp, TensorRT-LLM, or similar)","DevOps expertise for deployment, scaling, and monitoring","Acceptance of Meta community license terms"],"input_types":["text (same as base model)"],"output_types":["text (same as base model)"],"categories":["automation-workflow","deployment"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llama-3-3-70b__cap_8","uri":"capability://tool.use.integration.integration.with.langchain.and.llamaindex.frameworks","name":"integration with langchain and llamaindex frameworks","description":"Natively supported by LangChain and LlamaIndex Python frameworks through pre-built integrations, enabling rapid development of LLM applications without custom API wrappers. Integrations handle prompt formatting, token counting, streaming, and context management, reducing boilerplate code. Developers can use Llama 3.3 as a drop-in replacement for other LLMs in LangChain chains and LlamaIndex RAG pipelines, with consistent APIs across frameworks.","intents":["Build LLM applications using LangChain chains and agents without custom integration code","Create RAG systems with LlamaIndex using Llama 3.3 as the generation model","Rapidly prototype AI applications with framework abstractions","Switch between LLM providers (OpenAI, Anthropic, Llama) with minimal code changes"],"best_for":["Python developers building LLM applications with LangChain or LlamaIndex","Teams prototyping AI features quickly without infrastructure setup","Organizations migrating from closed-source LLMs to open-weight alternatives"],"limitations":["Framework integrations add abstraction overhead (~50-200ms per chain step)","Framework-specific features may not expose all Llama 3.3 capabilities","Streaming and real-time features depend on framework implementation","Token counting and cost estimation require framework-specific configuration"],"requires":["Python 3.8+","LangChain or LlamaIndex library installed","Local Llama 3.3 deployment or API endpoint","Framework-specific configuration (model name, API endpoint, credentials)"],"input_types":["text (prompts, chains, RAG queries)"],"output_types":["text (generated responses, chain outputs)"],"categories":["tool-use-integration","framework-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llama-3-3-70b__cap_9","uri":"capability://text.generation.language.mathematical.reasoning.with.math.benchmark.performance","name":"mathematical reasoning with math benchmark performance","description":"Trained on mathematical problem-solving datasets and instruction-following examples, enabling the model to solve mathematical problems, show step-by-step reasoning, and generate mathematical explanations. Benchmark performance on MATH dataset is mentioned but specific score not provided in documentation. Uses standard transformer architecture without specialized mathematical modules, relying on learned patterns from training data to perform arithmetic, algebra, calculus, and logic problems.","intents":["Solve mathematical problems from natural language descriptions","Generate step-by-step solutions with mathematical reasoning","Create educational content explaining mathematical concepts","Verify mathematical correctness or identify errors in solutions"],"best_for":["Educational technology platforms requiring math tutoring capabilities","Research applications needing symbolic reasoning and mathematical problem-solving","Teams building STEM-focused AI applications"],"limitations":["Specific MATH benchmark score not provided; performance level unknown","No symbolic math engine; relies on learned patterns which may fail on novel problem types","Arithmetic errors possible, especially in multi-step calculations","No formal verification of mathematical correctness; solutions may be plausible but incorrect"],"requires":["Prompts formatted with clear mathematical problem statements","Validation pipeline to verify mathematical correctness of generated solutions"],"input_types":["text (mathematical problems, equations, reasoning prompts)"],"output_types":["text (mathematical solutions, step-by-step reasoning, explanations)"],"categories":["text-generation-language","reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llama-3-3-70b__headline","uri":"capability://text.generation.language.high.performance.open.weight.text.model","name":"high-performance open-weight text model","description":"Llama 3.3 70B is a leading open-weight text model that delivers exceptional performance on various benchmarks while being cost-effective and suitable for self-hosted enterprise deployments.","intents":["best open-weight text model","text model for enterprise deployment","high-performance model for code generation","open-source model for multilingual support","text generation model with large context window"],"best_for":["enterprise use","cost-effective deployments"],"limitations":[],"requires":[],"input_types":["text"],"output_types":["text"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":57,"verified":false,"data_access_risk":"low","permissions":["GPU with sufficient VRAM for 70B parameter model (specific VRAM requirements not documented; typically 40-80GB for fp16)","Inference framework supporting transformer models (vLLM, llama.cpp, TensorRT-LLM, or similar)","Meta community license acceptance for commercial deployment","Input text in one of 8 supported languages (language list not specified)","Same GPU/inference framework requirements as base text generation capability","Prompt engineering expertise to design effective few-shot examples","Understanding of model behavior and instruction-following patterns","Validation pipeline to assess few-shot performance on target tasks","Inference framework with batching support (vLLM, TensorRT-LLM, or similar)","GPU hardware with sufficient VRAM for batch processing"],"failure_modes":["Text-only input; no native image understanding or multimodal reasoning","128K context window hard limit may truncate very long documents or conversation histories","Performance claims (matching 405B) are Meta's claims; independent verification not provided in documentation","Specific failure modes, hallucination rates, and edge cases not documented","Only 8 languages supported; specific language list not documented","Performance across languages not benchmarked individually; MMLU/HumanEval scores likely represent English-dominant performance","No documented translation quality metrics or language-specific capability analysis","Cross-lingual transfer quality unknown for low-resource language pairs","Few-shot learning quality depends heavily on example selection and prompt engineering","Performance may degrade with very different test cases vs. training examples","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.3,"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":"2026-05-24T12:16:23.327Z","last_scraped_at":null,"last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=llama-3-3-70b","compare_url":"https://unfragile.ai/compare?artifact=llama-3-3-70b"}},"signature":"LlaOJCS2FglQLbNIFsCFomYeyPbTt4MNL/JM3gujzcYRySrut2Z+8ip4WQEEcEBUpG5OKgu07WoHRcjsY+dwBQ==","signedAt":"2026-06-22T02:42:36.545Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/llama-3-3-70b","artifact":"https://unfragile.ai/llama-3-3-70b","verify":"https://unfragile.ai/api/v1/verify?slug=llama-3-3-70b","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"}}