{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-huggingface-co-meta-llama-3-70b-instruct","slug":"huggingface-co-meta-llama-3-70b-instruct","name":"huggingface.co/Meta-Llama-3-70B-Instruct","type":"model","url":"https://huggingface.co/chat/models/meta-llama/Meta-Llama-3-70B-Instruct","page_url":"https://unfragile.ai/huggingface-co-meta-llama-3-70b-instruct","categories":["model-training"],"tags":[],"pricing":{"model":"unknown","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-huggingface-co-meta-llama-3-70b-instruct__cap_0","uri":"capability://text.generation.language.instruction.following.conversational.generation.with.70b.parameters","name":"instruction-following conversational generation with 70b parameters","description":"Generates contextually relevant, multi-turn conversational responses using a 70-billion parameter transformer architecture fine-tuned on instruction-following datasets. The model uses grouped query attention (GQA) for efficient inference, reducing memory bandwidth requirements while maintaining output quality across diverse domains including coding, analysis, creative writing, and reasoning tasks.","intents":["Build a chatbot that understands complex instructions and maintains conversation context across multiple turns","Generate code explanations, documentation, or refactoring suggestions in natural language","Create a reasoning engine that can break down multi-step problems and explain its logic","Develop an AI assistant that handles domain-specific queries (legal, medical, technical) with nuanced responses"],"best_for":["Teams building production chatbots and conversational AI systems","Developers creating code-aware assistants and documentation generators","Researchers and enterprises requiring open-source alternatives to proprietary LLMs","Organizations with on-premise deployment requirements or data sovereignty constraints"],"limitations":["Context window limited to 8,192 tokens, constraining ability to process very long documents or multi-document reasoning","No native vision capabilities — cannot process images, PDFs with visual content, or video","Inference latency scales with sequence length; generating long outputs (>2000 tokens) requires significant compute","Knowledge cutoff date limits awareness of events after training completion; cannot access real-time information","No built-in tool calling or function invocation without additional fine-tuning or prompt engineering","Hallucination rate on factual queries remains higher than some proprietary models; requires fact-checking in production"],"requires":["GPU with minimum 40GB VRAM for full model inference (A100, H100, or equivalent)","CUDA 11.8+ or compatible GPU compute framework (PyTorch 2.0+, vLLM, or TensorRT-LLM)","Hugging Face Transformers library (version 4.36+) or compatible inference engine","Internet connection for initial model download (~140GB for full precision weights)","Python 3.10+ for local deployment, or access to Hugging Face Inference API"],"input_types":["text (natural language instructions, questions, prompts)","code snippets (for analysis, explanation, or generation tasks)","structured prompts (system messages, few-shot examples, chain-of-thought templates)"],"output_types":["text (conversational responses, explanations, creative content)","code (Python, JavaScript, SQL, Bash, and 40+ other languages)","structured text (JSON, YAML, markdown formatted responses)","reasoning traces (step-by-step problem decomposition)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-huggingface-co-meta-llama-3-70b-instruct__cap_1","uri":"capability://text.generation.language.multi.turn.context.aware.conversation.management","name":"multi-turn context-aware conversation management","description":"Maintains coherent conversation state across multiple exchanges by processing the full conversation history as a single input sequence, with attention mechanisms that weight recent messages and user intent more heavily. The model learns to track entities, pronouns, and implicit references across turns without explicit state management, enabling natural dialogue flow without conversation reset or context loss.","intents":["Build a customer support chatbot that remembers previous issues and context across sessions","Create an interactive coding tutor that builds on previous explanations and student questions","Develop a collaborative writing assistant that maintains narrative consistency and character details","Implement a debugging assistant that tracks the problem statement and previous attempted solutions"],"best_for":["Conversational AI applications requiring stateful interactions","Educational and tutoring platforms with multi-turn learning flows","Customer service and support systems with complex issue resolution","Interactive debugging and pair-programming scenarios"],"limitations":["Context window of 8,192 tokens limits conversation history to approximately 3,000-4,000 words before truncation","No explicit memory mechanism — older conversation turns are compressed/forgotten as context fills up","Attention mechanism can lose track of details from very early conversation turns (>20 turns back)","No built-in conversation summarization; developers must implement their own history management","Performance degrades linearly with conversation length; longer histories increase latency by ~10-15% per 1,000 tokens"],"requires":["Conversation history management system (in-memory, database, or vector store)","Token counting utility to track context window usage (tiktoken or Hugging Face tokenizers)","Message formatting/templating system to structure multi-turn conversations","GPU inference setup as specified in parent capability"],"input_types":["text (user messages in conversational format)","structured conversation history (array of {role, content} objects)","system prompts defining conversation behavior and constraints"],"output_types":["text (contextually aware responses referencing previous turns)","structured conversation metadata (detected entities, intent, sentiment)"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-huggingface-co-meta-llama-3-70b-instruct__cap_2","uri":"capability://code.generation.editing.code.generation.and.explanation.across.40.programming.languages","name":"code generation and explanation across 40+ programming languages","description":"Generates syntactically correct, idiomatic code and detailed explanations across Python, JavaScript, Java, C++, SQL, Bash, Go, Rust, and 30+ other languages. The model was trained on diverse code repositories and instruction-tuned with code-specific examples, enabling it to understand language-specific idioms, standard libraries, and common patterns. It can generate complete functions, debug existing code, explain algorithms, and suggest optimizations with language-aware reasoning.","intents":["Generate boilerplate code and starter templates for new projects in any language","Explain complex algorithms and code snippets with language-specific context","Debug code by identifying logical errors, type mismatches, or performance issues","Refactor code to improve readability, performance, or adherence to language-specific best practices","Generate SQL queries, database schemas, and data transformation logic"],"best_for":["Full-stack development teams working across multiple languages and frameworks","Educational platforms teaching programming across diverse languages","Code review and refactoring workflows requiring language-aware suggestions","Data engineering and database teams generating SQL and ETL logic"],"limitations":["No real-time syntax validation — generated code may contain subtle errors requiring manual testing","Limited understanding of large codebases; cannot analyze or refactor entire projects without explicit context","No access to language-specific type systems or static analysis tools; may miss type errors in statically-typed languages","Generated code may not follow project-specific conventions or architectural patterns without explicit examples","Performance optimization suggestions are heuristic-based, not derived from profiling or benchmarking","Cannot generate code for proprietary or niche languages with limited training data (COBOL, Fortran, etc.)"],"requires":["Code context provided as text input (full files or snippets)","Optional: language specification in prompt to improve idiom accuracy","Testing framework or linter to validate generated code before deployment"],"input_types":["text (code snippets, functions, or full files)","natural language (instructions for what code to generate or how to refactor)","structured prompts (language specification, framework context, requirements)"],"output_types":["code (complete functions, classes, scripts, or full files)","text (explanations, documentation, refactoring suggestions)","structured code (JSON, YAML, SQL schemas)"],"categories":["code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-huggingface-co-meta-llama-3-70b-instruct__cap_3","uri":"capability://planning.reasoning.reasoning.and.chain.of.thought.problem.decomposition","name":"reasoning and chain-of-thought problem decomposition","description":"Decomposes complex problems into step-by-step reasoning chains, explicitly showing intermediate logic and decision points before arriving at conclusions. The model was fine-tuned on reasoning-focused datasets including math problems, logical puzzles, and multi-step analysis tasks, enabling it to generate transparent reasoning traces that can be validated and debugged by users. This capability supports both mathematical reasoning and natural language reasoning across diverse domains.","intents":["Solve multi-step math problems with explicit reasoning shown at each step","Analyze complex scenarios by breaking them into logical components and dependencies","Debug reasoning errors by examining intermediate steps and identifying where logic diverged","Generate explainable AI outputs where the decision-making process is transparent and auditable","Create educational content that teaches problem-solving methodology, not just answers"],"best_for":["Educational platforms and tutoring systems requiring transparent problem-solving","Enterprise systems requiring explainable AI and audit trails for decisions","Research and analysis workflows where reasoning transparency is critical","Quality assurance and testing scenarios where intermediate logic must be validated"],"limitations":["Reasoning chains can be verbose, increasing token consumption by 2-5x compared to direct answers","No guarantee of correctness — reasoning traces may contain logical errors or false premises","Longer reasoning chains (>10 steps) show degraded accuracy; complex problems may require external verification","Mathematical reasoning limited to algebra and basic calculus; advanced mathematics may require symbolic solvers","No access to external tools or calculators; arithmetic errors can propagate through reasoning chains","Reasoning style is learned from training data; may not match domain-specific reasoning conventions"],"requires":["Prompt engineering to explicitly request step-by-step reasoning (e.g., 'Let's think step by step')","Sufficient context window to accommodate longer reasoning traces (6,000+ tokens for complex problems)","Optional: external verification tools (calculators, theorem provers) to validate mathematical reasoning"],"input_types":["text (problem statements, questions, scenarios)","structured prompts (explicit requests for step-by-step reasoning)","context (background information, constraints, definitions)"],"output_types":["text (step-by-step reasoning traces with intermediate conclusions)","structured reasoning (numbered steps, logical operators, decision trees)","final answers with supporting reasoning"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-huggingface-co-meta-llama-3-70b-instruct__cap_4","uri":"capability://text.generation.language.domain.specific.knowledge.synthesis.and.analysis","name":"domain-specific knowledge synthesis and analysis","description":"Synthesizes and analyzes information across technical, scientific, legal, medical, and business domains by leveraging training data that includes domain-specific literature, documentation, and expert-written content. The model can explain complex domain concepts, compare approaches within a domain, and provide nuanced analysis that accounts for domain-specific constraints and best practices. This capability extends beyond generic language understanding to include domain-aware reasoning patterns.","intents":["Explain complex technical concepts (distributed systems, machine learning, cryptography) with appropriate depth","Analyze legal documents and contracts, identifying key clauses and potential risks","Provide medical information summaries while appropriately disclaiming limitations and recommending professional consultation","Compare business strategies and market approaches with industry-specific context","Generate domain-specific documentation and technical specifications"],"best_for":["Professional services firms requiring domain-aware analysis and synthesis","Technical documentation and knowledge base generation","Compliance and legal review workflows requiring nuanced domain understanding","Enterprise training and onboarding systems requiring domain expertise","Research and analysis platforms across specialized fields"],"limitations":["Knowledge cutoff limits awareness of recent developments in rapidly-evolving domains (AI, biotech, finance)","No access to proprietary or confidential domain databases; analysis limited to public training data","Domain expertise is learned from training data; may not reflect latest research or industry standards","Cannot provide professional advice (legal, medical, financial) — outputs require expert review","Domain-specific terminology may be misinterpreted if training data contained conflicting definitions","No ability to access or analyze domain-specific tools (medical imaging software, legal databases, financial platforms)"],"requires":["Domain context provided in prompts (e.g., 'In the context of distributed systems...')","Optional: domain-specific examples or reference materials to guide analysis","Expert review process for outputs in regulated domains (medical, legal, financial)"],"input_types":["text (domain-specific questions, documents, scenarios)","structured prompts (domain specification, context, constraints)","reference materials (examples, standards, best practices)"],"output_types":["text (domain-aware explanations, analyses, recommendations)","structured analysis (comparisons, risk assessments, decision frameworks)","documentation (technical specifications, guides, summaries)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-huggingface-co-meta-llama-3-70b-instruct__cap_5","uri":"capability://text.generation.language.creative.content.generation.with.style.and.tone.control","name":"creative content generation with style and tone control","description":"Generates creative content including stories, poetry, marketing copy, and dialogue with controllable style, tone, and voice. The model learns stylistic patterns from training data and can adapt output to match specified tones (formal, casual, humorous, technical) and styles (Shakespearean, noir, sci-fi, etc.). This capability supports both original content creation and style-transfer tasks where existing content is rewritten in a different voice.","intents":["Generate marketing copy and product descriptions with brand-appropriate tone and messaging","Create fictional narratives, character dialogue, and story outlines with consistent voice","Rewrite existing content in different styles or tones (e.g., formal to casual, technical to accessible)","Generate poetry, song lyrics, and creative writing with specified constraints (rhyme scheme, meter, themes)","Create engaging educational content that explains technical concepts in accessible, narrative-driven ways"],"best_for":["Content marketing and copywriting teams requiring rapid iteration on messaging","Creative writing and storytelling platforms","Educational content creators requiring engaging, accessible explanations","Brand communication teams maintaining consistent voice across channels","Game and interactive fiction developers generating narrative content"],"limitations":["Generated content may lack originality or contain clichéd phrases common in training data","Tone and style control requires explicit prompting; implicit style requests may be misinterpreted","Long-form content (>2000 words) shows degraded consistency; narrative coherence decreases with length","No access to real-time trends or current events; generated marketing copy may feel dated","Humor generation is inconsistent and may not align with target audience sensibilities","No built-in fact-checking; creative content may inadvertently contain false information presented as fact"],"requires":["Clear style and tone specifications in prompts (e.g., 'Write in the style of a noir detective novel')","Optional: examples of target style or voice to guide generation","Human review process to ensure generated content aligns with brand voice and messaging"],"input_types":["text (creative prompts, story premises, content briefs)","style specifications (tone, voice, genre, constraints)","reference materials (examples of target style, brand guidelines)"],"output_types":["text (stories, poetry, marketing copy, dialogue)","structured content (story outlines, character descriptions, plot summaries)","styled variations (same content in multiple tones or voices)"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-huggingface-co-meta-llama-3-70b-instruct__cap_6","uri":"capability://text.generation.language.summarization.and.information.extraction.from.long.documents","name":"summarization and information extraction from long documents","description":"Extracts key information and generates summaries from long documents by identifying salient points, relationships, and hierarchies within text. The model can produce summaries at multiple granularities (abstract, bullet points, key takeaways) and extract structured information (entities, dates, relationships) from unstructured text. This capability works within the 8,192 token context window, requiring document chunking for very long texts.","intents":["Summarize research papers, articles, and reports into concise overviews","Extract key facts, dates, and entities from legal documents, contracts, or compliance materials","Generate bullet-point summaries of meeting notes or transcripts","Create table-of-contents or outline from long documents","Identify and extract specific information (pricing, requirements, deadlines) from unstructured text"],"best_for":["Knowledge management and document processing workflows","Legal and compliance teams reviewing large document volumes","Research and academic workflows requiring rapid literature review","Business intelligence and market research teams synthesizing information","Customer support and documentation teams extracting relevant information"],"limitations":["Context window of 8,192 tokens limits document length to approximately 5,000-6,000 words; longer documents require chunking","Summarization quality degrades for documents with complex structure or multiple disconnected topics","Extracted information may be incomplete or inaccurate if key details are scattered throughout the document","No built-in document parsing; requires pre-processing to extract text from PDFs, images, or formatted documents","Summarization style is learned from training data; may not match domain-specific summary conventions","No ability to verify extracted information against source documents; hallucinations possible for factual claims"],"requires":["Document text in plain text format (PDF extraction, OCR, or HTML parsing required for other formats)","Document chunking strategy for texts exceeding context window (sliding window, semantic chunking, or recursive summarization)","Optional: extraction templates or schemas to guide structured information extraction"],"input_types":["text (full documents, articles, reports)","structured prompts (summary style, length, focus areas)","extraction templates (schema for structured information)"],"output_types":["text (summaries at multiple granularities, bullet points, key takeaways)","structured data (extracted entities, relationships, key facts)","outlines (document structure, section summaries)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-huggingface-co-meta-llama-3-70b-instruct__cap_7","uri":"capability://text.generation.language.translation.and.multilingual.understanding.across.100.languages","name":"translation and multilingual understanding across 100+ languages","description":"Translates text between 100+ languages and understands multilingual context, including code-switching and language-specific idioms. The model was trained on diverse multilingual corpora and can maintain semantic meaning and cultural context across language boundaries. It supports both direct translation and explanation of language-specific concepts that may not have direct equivalents in other languages.","intents":["Translate content between major languages while preserving tone and meaning","Explain language-specific idioms and cultural references to non-native speakers","Identify and correct language-specific grammar, spelling, and style issues","Generate multilingual content for global audiences","Understand and respond to queries in non-English languages"],"best_for":["Global content platforms and multilingual applications","International business and customer support teams","Language learning and education platforms","Localization and internationalization workflows","Research and analysis across multilingual sources"],"limitations":["Translation quality varies significantly across language pairs; less common languages show lower accuracy","Idioms and cultural references may be mistranslated or lose meaning in translation","No access to language-specific resources (dictionaries, style guides, terminology databases)","Code-switching (mixing languages) may confuse the model; explicit language specification improves accuracy","Proper nouns and named entities may be mistranslated or transliterated incorrectly","Low-resource languages show significantly degraded translation quality compared to high-resource languages"],"requires":["Source and target language specification in prompts","Optional: glossaries or terminology lists for domain-specific translation","Human review for critical translations, especially for low-resource language pairs"],"input_types":["text (content to translate, language-specific queries)","language specifications (source and target languages)","context (domain, tone, terminology preferences)"],"output_types":["text (translated content, explanations of language-specific concepts)","multilingual responses (understanding and responding in non-English languages)"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"high","permissions":["GPU with minimum 40GB VRAM for full model inference (A100, H100, or equivalent)","CUDA 11.8+ or compatible GPU compute framework (PyTorch 2.0+, vLLM, or TensorRT-LLM)","Hugging Face Transformers library (version 4.36+) or compatible inference engine","Internet connection for initial model download (~140GB for full precision weights)","Python 3.10+ for local deployment, or access to Hugging Face Inference API","Conversation history management system (in-memory, database, or vector store)","Token counting utility to track context window usage (tiktoken or Hugging Face tokenizers)","Message formatting/templating system to structure multi-turn conversations","GPU inference setup as specified in parent capability","Code context provided as text input (full files or snippets)"],"failure_modes":["Context window limited to 8,192 tokens, constraining ability to process very long documents or multi-document reasoning","No native vision capabilities — cannot process images, PDFs with visual content, or video","Inference latency scales with sequence length; generating long outputs (>2000 tokens) requires significant compute","Knowledge cutoff date limits awareness of events after training completion; cannot access real-time information","No built-in tool calling or function invocation without additional fine-tuning or prompt engineering","Hallucination rate on factual queries remains higher than some proprietary models; requires fact-checking in production","Context window of 8,192 tokens limits conversation history to approximately 3,000-4,000 words before truncation","No explicit memory mechanism — older conversation turns are compressed/forgotten as context fills up","Attention mechanism can lose track of details from very early conversation turns (>20 turns back)","No built-in conversation summarization; developers must implement their own history management","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.41,"ecosystem":0.25,"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":"2026-06-17T09:51:03.041Z","last_scraped_at":"2026-05-03T14:00:25.471Z","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=huggingface-co-meta-llama-3-70b-instruct","compare_url":"https://unfragile.ai/compare?artifact=huggingface-co-meta-llama-3-70b-instruct"}},"signature":"qlG2H3b55bF2+gdBe+YgQEQ66co0SuSB60lXP7pdrc8oOYBSzMEPGjcfQTO7a5qDLmIOzIYp4wqQT8uWlkXfBQ==","signedAt":"2026-06-20T16:14:26.657Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/huggingface-co-meta-llama-3-70b-instruct","artifact":"https://unfragile.ai/huggingface-co-meta-llama-3-70b-instruct","verify":"https://unfragile.ai/api/v1/verify?slug=huggingface-co-meta-llama-3-70b-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"}}