{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-meta-llama--llama-3.1-8b-instruct","slug":"meta-llama--llama-3.1-8b-instruct","name":"Llama-3.1-8B-Instruct","type":"model","url":"https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct","page_url":"https://unfragile.ai/meta-llama--llama-3.1-8b-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","base_model:meta-llama/Llama-3.1-8B","base_model:finetune:meta-llama/Llama-3.1-8B"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-model-meta-llama--llama-3.1-8b-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, contextually-aware text responses to user prompts using a transformer-based architecture with 8 billion parameters fine-tuned on instruction-following data. The model processes input tokens through 32 transformer layers with grouped-query attention (GQA) to reduce memory overhead, enabling efficient inference on consumer hardware. Supports multi-turn conversation by maintaining context across sequential exchanges without explicit memory management, using standard causal language modeling with a 128K token context window.","intents":["Build a conversational chatbot that understands complex instructions and maintains context across multiple turns","Deploy a local LLM assistant that doesn't require cloud API calls or external service dependencies","Create domain-specific applications (customer support, tutoring, content generation) with instruction-tuned behavior"],"best_for":["Solo developers and small teams building privacy-conscious LLM applications","Organizations requiring on-premise deployment without vendor lock-in","Researchers and builders prototyping multi-agent systems with local inference"],"limitations":["8B parameter model trades off reasoning depth vs. larger models (70B, 405B); struggles with complex multi-step mathematical reasoning and specialized domain knowledge","Context window of 128K tokens is fixed; cannot dynamically extend for extremely long documents without chunking strategies","Inference latency on CPU-only systems ranges 5-15 seconds per response; GPU acceleration (NVIDIA, AMD) required for sub-second latency","Knowledge cutoff date limits real-time information; no built-in web search or external knowledge integration"],"requires":["Python 3.8+","PyTorch 2.0+ or compatible deep learning framework","Minimum 16GB RAM for quantized inference (8-bit), 32GB+ for full precision","GPU with 8GB+ VRAM recommended (NVIDIA CUDA 11.8+, AMD ROCm 5.7+, or Apple Metal for M-series)","HuggingFace transformers library 4.36.0+","SafeTensors format support for efficient model loading"],"input_types":["plain text prompts","multi-turn conversation history (as concatenated text or structured messages)","system prompts for behavioral guidance","code snippets for code-related tasks"],"output_types":["plain text responses","structured text (JSON, markdown, code blocks)","multi-paragraph narratives","code generation in 10+ programming languages"],"categories":["text-generation-language","conversational-ai"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-meta-llama--llama-3.1-8b-instruct__cap_1","uri":"capability://text.generation.language.multilingual.text.generation.across.9.languages","name":"multilingual text generation across 9 languages","description":"Generates fluent, contextually appropriate text in English, German, French, Italian, Portuguese, Hindi, Spanish, Thai, and Japanese through shared transformer embeddings trained on multilingual instruction data. The model uses a unified vocabulary (128K tokens) with language-specific token distributions, allowing seamless code-switching and cross-lingual understanding without separate language-specific models. Achieves multilingual capability via instruction tuning on diverse language datasets rather than explicit language routing logic.","intents":["Build global applications that serve users in multiple languages from a single model deployment","Create translation-adjacent features (content adaptation, localization) without separate translation models","Support code-switching scenarios where users mix languages in single prompts"],"best_for":["International teams building products for non-English markets","Developers reducing model count by consolidating language-specific deployments","Applications requiring Hindi, Thai, or other underrepresented languages in LLM ecosystems"],"limitations":["Multilingual performance degrades for low-resource languages (Thai, Hindi) vs. English; quality variance across language pairs","No explicit language detection or routing; relies on context to determine output language, sometimes producing mixed-language outputs","Training data imbalance favors English; non-English languages receive less instruction-tuning coverage, reducing instruction-following quality","Character-level phenomena (tone marks in Thai, diacritics in Portuguese) occasionally mishandled due to tokenizer limitations"],"requires":["Python 3.8+","HuggingFace transformers 4.36.0+","Support for UTF-8 encoding in input/output pipelines","Language-specific fonts/rendering for proper display (especially Thai, Hindi, Japanese)"],"input_types":["text in any of 9 supported languages","code-switched prompts mixing multiple languages","language-tagged prompts (e.g., '[EN] English text [FR] French text')"],"output_types":["text in requested language","code-switched responses","translations or adaptations across language pairs"],"categories":["text-generation-language","multilingual-support"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-meta-llama--llama-3.1-8b-instruct__cap_10","uri":"capability://text.generation.language.few.shot.learning.and.in.context.adaptation","name":"few-shot learning and in-context adaptation","description":"Adapts behavior and output format based on examples provided in the prompt (few-shot learning) without requiring model fine-tuning or retraining. The model processes example input-output pairs in the prompt context, learns patterns from these examples through transformer attention, and applies learned patterns to new inputs. Supports 1-shot, 2-shot, and multi-shot learning scenarios where providing 2-5 examples significantly improves performance on specific tasks.","intents":["Adapt model behavior to task-specific formats without fine-tuning (e.g., 'extract data in JSON format' with examples)","Improve performance on specialized tasks by providing domain-specific examples","Implement zero-shot to few-shot learning pipelines for rapid task adaptation"],"best_for":["Applications requiring rapid task adaptation without fine-tuning infrastructure","Teams building flexible systems that handle diverse task types","Scenarios where labeled training data is limited but examples can be provided in prompts"],"limitations":["Few-shot learning effectiveness depends heavily on example quality and relevance; poor examples degrade performance","Context window is shared between examples and input; providing many examples reduces space for actual task input","Learning from examples is less stable than fine-tuning; performance can vary based on example order and phrasing","Complex tasks requiring >5-7 examples may exceed practical context limits or degrade performance due to attention dilution","No explicit mechanism for learning from negative examples; model sometimes overgeneralizes from limited examples"],"requires":["Python 3.8+","HuggingFace transformers 4.36.0+","Carefully curated examples relevant to target task","Token counting to ensure examples fit within context window"],"input_types":["example input-output pairs demonstrating desired behavior","new input to be processed using learned patterns","task descriptions and format specifications"],"output_types":["outputs following format and patterns demonstrated in examples","task-specific structured data (JSON, CSV, etc.)","responses adapted to example style and tone"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-meta-llama--llama-3.1-8b-instruct__cap_11","uri":"capability://code.generation.editing.token.efficient.inference.with.quantization.support","name":"token-efficient inference with quantization support","description":"Supports multiple quantization formats (8-bit, 4-bit, GPTQ) enabling efficient inference on resource-constrained hardware by reducing model size from 16GB (full precision) to 4-8GB (quantized) with minimal quality loss. The model weights are quantized (reduced precision) during loading, reducing memory footprint and enabling faster inference on consumer GPUs and edge devices. Quantization is applied transparently through libraries like bitsandbytes and GPTQ, requiring no code changes to inference pipelines.","intents":["Deploy models on consumer GPUs (8GB VRAM) and edge devices without expensive enterprise hardware","Reduce inference latency and memory usage for high-throughput applications","Enable local deployment on laptops and mobile devices for privacy-sensitive applications"],"best_for":["Teams with limited GPU budgets requiring efficient inference","Edge deployment scenarios (laptops, mobile, IoT devices)","High-throughput applications where inference speed is critical"],"limitations":["Quantization introduces quality degradation; 4-bit quantization reduces reasoning quality ~5-10% vs. full precision","Quantization formats are not standardized; different quantization methods (GPTQ, AWQ, bitsandbytes) have different performance characteristics","Quantized models require specific libraries and hardware support; not all quantization formats work on all devices","Quantization overhead during model loading (~30-60 seconds) adds startup latency","Some quantization methods (8-bit) still require significant VRAM; not suitable for extremely resource-constrained devices"],"requires":["Python 3.8+","HuggingFace transformers 4.36.0+","bitsandbytes library for 8-bit quantization (requires NVIDIA GPU)","GPTQ or AWQ quantized model weights (separate downloads)","Optional: CUDA 11.8+ for GPU acceleration"],"input_types":["standard text prompts (same as full-precision model)"],"output_types":["text responses (quality comparable to full precision, with minor degradation)"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-meta-llama--llama-3.1-8b-instruct__cap_2","uri":"capability://code.generation.editing.code.generation.and.explanation.across.10.programming.languages","name":"code generation and explanation across 10+ programming languages","description":"Generates syntactically valid, functional code in Python, JavaScript, TypeScript, Java, C++, C#, Go, Rust, SQL, and Bash through instruction-tuned patterns learned from code-heavy training data. The model understands code structure, variable scoping, and language idioms via transformer attention mechanisms that learn to recognize code patterns; generates code by predicting token sequences that follow programming language grammar rules. Supports both code generation from natural language descriptions and code explanation/documentation tasks.","intents":["Generate boilerplate code, utility functions, and API integrations from natural language specifications","Explain existing code snippets, generate documentation, and suggest refactoring improvements","Assist in debugging by analyzing error messages and suggesting fixes"],"best_for":["Junior developers and non-programmers prototyping code solutions","Teams using LLMs for code scaffolding and documentation generation","Educational contexts where students need code explanation and learning support"],"limitations":["Generated code requires human review; model produces syntactically correct but logically flawed code ~15-30% of the time on complex tasks","No real-time compilation or execution feedback; cannot validate generated code against actual runtime environments","Limited to 128K context window; struggles with refactoring large codebases or understanding complex multi-file dependencies","Code generation quality degrades for domain-specific languages (Terraform, Solidity, VHDL) with less training data representation","No built-in security analysis; may generate code with SQL injection, XSS, or other vulnerabilities without explicit prompting"],"requires":["Python 3.8+","HuggingFace transformers 4.36.0+","Optional: language-specific linters (pylint, eslint, rustc) for post-generation validation","Optional: code execution sandbox for testing generated code"],"input_types":["natural language descriptions of desired code behavior","code snippets for explanation or refactoring","error messages with context for debugging assistance","function signatures or API documentation for implementation"],"output_types":["complete functions or scripts","code snippets and examples","documentation and comments","refactored code with improvements"],"categories":["code-generation-editing","developer-tools"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-meta-llama--llama-3.1-8b-instruct__cap_3","uri":"capability://planning.reasoning.reasoning.and.step.by.step.problem.decomposition","name":"reasoning and step-by-step problem decomposition","description":"Breaks down complex problems into intermediate reasoning steps through chain-of-thought patterns learned during instruction tuning, enabling the model to show work before arriving at conclusions. The model generates explicit reasoning tokens (e.g., 'Let me think about this step by step...') that improve accuracy on multi-step problems by forcing sequential token prediction through logical intermediate states. This capability emerges from training on datasets containing reasoning traces and explanations, not from explicit reasoning modules.","intents":["Solve math problems, logic puzzles, and analytical questions with transparent reasoning","Generate detailed explanations for complex topics with step-by-step breakdowns","Improve accuracy on tasks requiring multiple reasoning hops by prompting for explicit thinking"],"best_for":["Educational applications requiring transparent problem-solving explanations","Analytical tasks where reasoning transparency improves user trust and verifiability","Applications where intermediate reasoning steps are valuable outputs (tutoring, research assistance)"],"limitations":["Reasoning quality is inconsistent; model sometimes generates plausible-sounding but incorrect intermediate steps (hallucinated reasoning)","Chain-of-thought prompting adds 2-3x token generation overhead, increasing latency and cost","No formal verification of reasoning steps; cannot guarantee logical correctness of intermediate conclusions","Struggles with problems requiring >5-7 reasoning steps; longer chains accumulate errors","Mathematical reasoning limited to arithmetic and basic algebra; fails on advanced calculus, formal proofs, and symbolic manipulation"],"requires":["Python 3.8+","HuggingFace transformers 4.36.0+","Prompting strategy that explicitly requests step-by-step reasoning (e.g., 'Think step by step')"],"input_types":["math problems and equations","logic puzzles and analytical questions","complex multi-step instructions","reasoning-heavy prompts"],"output_types":["step-by-step reasoning traces","intermediate conclusions","final answers with justification","detailed explanations"],"categories":["planning-reasoning","analytical-tasks"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-meta-llama--llama-3.1-8b-instruct__cap_4","uri":"capability://text.generation.language.content.summarization.and.extraction","name":"content summarization and extraction","description":"Condenses long documents, articles, or conversations into concise summaries while preserving key information through abstractive summarization learned during instruction tuning. The model reads full input text (up to 128K tokens), identifies salient information via transformer attention mechanisms, and generates compressed output that captures main points. Supports multiple summarization styles (bullet points, paragraphs, headlines) and can extract specific information (entities, dates, key facts) from unstructured text.","intents":["Summarize long documents, research papers, or meeting transcripts into executive summaries","Extract key facts, entities, and dates from unstructured text for structured data pipelines","Generate concise overviews of conversations or articles for quick consumption"],"best_for":["Knowledge workers processing large volumes of text (researchers, analysts, journalists)","Applications requiring document preprocessing and information extraction","Teams building search or retrieval systems needing text condensation"],"limitations":["Abstractive summarization sometimes omits critical details or introduces subtle inaccuracies; requires human review for high-stakes applications","Struggles with domain-specific jargon and technical terminology; may oversimplify specialized content","No explicit fact-checking; summaries can contain hallucinated details not present in source material","Performance degrades on very long documents (>50K tokens) due to attention dilution across massive context","Extractive capabilities limited; cannot reliably extract structured data without explicit formatting instructions"],"requires":["Python 3.8+","HuggingFace transformers 4.36.0+","Input text in plain text, markdown, or HTML format","Optional: document parsing libraries for PDF/DOCX input"],"input_types":["long-form text documents","articles and blog posts","meeting transcripts and conversations","research papers and technical documentation","web page content"],"output_types":["abstractive summaries (paragraph or bullet-point format)","extracted entities and facts","key takeaways and highlights","structured data (JSON) with extracted information"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-meta-llama--llama-3.1-8b-instruct__cap_5","uri":"capability://text.generation.language.creative.writing.and.content.generation","name":"creative writing and content generation","description":"Generates original creative content including stories, poetry, marketing copy, and dialogue through learned patterns from diverse text corpora in training data. The model predicts coherent token sequences that follow narrative structures, stylistic conventions, and genre-specific patterns learned implicitly via transformer attention. Supports style transfer, tone adaptation, and format-specific generation (social media posts, email copy, product descriptions) through instruction-tuned prompting.","intents":["Generate marketing copy, product descriptions, and promotional content","Create story outlines, dialogue, and narrative content for creative projects","Adapt content tone and style for different audiences and platforms"],"best_for":["Content creators and marketers generating bulk copy and variations","Creative professionals using AI as a brainstorming and drafting tool","Teams building personalized content generation systems"],"limitations":["Generated content sometimes lacks originality; may reproduce patterns or phrases from training data verbatim","Struggles with maintaining consistent character voice and personality across long narratives","No built-in fact-checking; creative content may contain false claims presented as fact","Tone and style adaptation inconsistent; requires careful prompt engineering to achieve desired voice","Limited understanding of cultural context and sensitivity; may generate culturally inappropriate content without explicit guardrails"],"requires":["Python 3.8+","HuggingFace transformers 4.36.0+","Detailed style and tone specifications in prompts for best results"],"input_types":["creative briefs and outlines","style and tone specifications","genre and format descriptions","partial content for continuation or completion"],"output_types":["complete stories and narratives","poetry and creative verse","marketing copy and product descriptions","dialogue and character interactions","social media content and posts"],"categories":["text-generation-language","creative-content"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-meta-llama--llama-3.1-8b-instruct__cap_6","uri":"capability://text.generation.language.question.answering.and.knowledge.retrieval","name":"question answering and knowledge retrieval","description":"Answers factual and analytical questions by retrieving and synthesizing relevant information from its training data through transformer attention mechanisms that identify relevant context tokens. The model generates answers by predicting token sequences that directly address the question, leveraging learned associations between question patterns and answer patterns from instruction-tuned training data. Supports open-ended questions, multiple-choice reasoning, and follow-up question handling within conversation context.","intents":["Build FAQ systems and knowledge bases that answer user questions without explicit database lookups","Create educational tutoring systems that explain concepts and answer student questions","Implement customer support chatbots that answer common questions autonomously"],"best_for":["Applications requiring general knowledge Q&A without domain-specific expertise","Educational and tutoring platforms needing conversational question answering","Customer support systems handling FAQ-style inquiries"],"limitations":["Knowledge cutoff limits answers to training data (April 2024); cannot answer questions about recent events or current information","Hallucination risk: model generates plausible-sounding but false answers ~10-20% of the time on factual questions","No explicit knowledge source attribution; cannot cite sources or provide confidence scores for answers","Struggles with questions requiring specialized domain knowledge (medical, legal, technical) without explicit fine-tuning","Cannot update knowledge without retraining; answers become stale as world changes"],"requires":["Python 3.8+","HuggingFace transformers 4.36.0+","Optional: external knowledge base integration (RAG) for domain-specific or current information"],"input_types":["factual questions","analytical and reasoning questions","follow-up questions in conversation context","multiple-choice questions with reasoning"],"output_types":["direct answers to questions","explanations and reasoning","multiple answer options with reasoning","clarifying questions for ambiguous inputs"],"categories":["text-generation-language","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-meta-llama--llama-3.1-8b-instruct__cap_7","uri":"capability://text.generation.language.conversational.context.management.across.multi.turn.exchanges","name":"conversational context management across multi-turn exchanges","description":"Maintains coherent conversation state across multiple user-assistant exchanges by processing full conversation history as input context, enabling the model to reference previous messages, maintain consistent persona, and build on prior statements. The model uses causal attention masking to prevent looking at future tokens, processing conversation history sequentially to build contextual understanding. Supports conversation memory up to 128K tokens, allowing 50-100+ turn conversations depending on message length.","intents":["Build multi-turn chatbots that remember context and maintain conversation coherence","Create conversational agents that reference previous statements and build on prior context","Implement dialogue systems where user intent depends on conversation history"],"best_for":["Chatbot and conversational AI applications requiring context awareness","Customer support systems where conversation history informs responses","Interactive tutoring and mentoring systems building on prior exchanges"],"limitations":["Context window is fixed at 128K tokens; very long conversations require pruning or summarization of old messages","No explicit memory management; model cannot distinguish between recent and distant context equally, sometimes losing track of early conversation details","Conversation history must be manually managed by application; no built-in persistence or session management","Token counting for conversation history requires careful management to avoid exceeding context limits mid-conversation","No explicit conversation state tracking; model cannot maintain structured state (e.g., user preferences, conversation goals) without encoding in text"],"requires":["Python 3.8+","HuggingFace transformers 4.36.0+","Application-level conversation history management (storing and formatting prior messages)","Token counting library to track context usage (e.g., tiktoken or transformers.AutoTokenizer)"],"input_types":["conversation history as formatted text (e.g., 'User: ... Assistant: ...')","system prompts defining assistant behavior","current user message"],"output_types":["contextually-aware assistant responses","references to prior conversation points","consistent persona and tone across turns"],"categories":["text-generation-language","conversational-ai"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-meta-llama--llama-3.1-8b-instruct__cap_8","uri":"capability://text.generation.language.system.prompt.and.behavioral.instruction.following","name":"system prompt and behavioral instruction following","description":"Adapts behavior, tone, and response style based on system prompts and behavioral instructions through instruction tuning that teaches the model to respect and follow explicit directives. The model learns to parse system-level instructions (e.g., 'You are a helpful coding assistant') and apply them consistently across all subsequent responses in a conversation. Supports role-playing, tone adaptation (formal/casual), and constraint-based behavior (e.g., 'respond in under 100 words').","intents":["Create specialized assistants with distinct personalities and expertise areas (coding assistant, writing coach, therapist, etc.)","Enforce response constraints and formatting requirements (length limits, output format, tone)","Implement role-based systems where the assistant adopts specific personas for different use cases"],"best_for":["Applications requiring customizable assistant behavior without fine-tuning","Multi-purpose platforms where users can configure assistant personality","Systems needing consistent tone and style across different interaction contexts"],"limitations":["System prompt adherence is probabilistic; model sometimes ignores or partially follows instructions, especially conflicting ones","No hard constraints; system prompts are suggestions, not guarantees — model can violate length limits, tone requirements, or behavioral constraints","Prompt injection vulnerability: user messages can override system prompts if carefully crafted, compromising intended behavior","Complex behavioral instructions sometimes confuse the model, leading to inconsistent or contradictory responses","No built-in monitoring or enforcement of system prompt compliance; requires external validation"],"requires":["Python 3.8+","HuggingFace transformers 4.36.0+","Clear, concise system prompt design (verbose or contradictory prompts reduce effectiveness)","Optional: prompt validation and injection detection systems"],"input_types":["system prompts defining assistant behavior","user messages to be processed under system prompt constraints"],"output_types":["responses adhering to system prompt specifications","tone and style adapted per instructions","formatted output matching specified constraints"],"categories":["text-generation-language","conversational-ai"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-meta-llama--llama-3.1-8b-instruct__cap_9","uri":"capability://safety.moderation.safety.aligned.response.generation.with.refusal.capabilities","name":"safety-aligned response generation with refusal capabilities","description":"Declines to engage with harmful requests (violence, illegal activities, abuse) through safety training that teaches the model to recognize harmful intents and generate refusal responses. The model learns safety boundaries during instruction tuning on datasets containing harmful prompts paired with refusal responses, enabling it to identify unsafe requests and respond with explanations of why it cannot help. Safety alignment is probabilistic, not absolute — the model uses learned patterns to estimate harm likelihood rather than explicit content filters.","intents":["Deploy LLM applications with built-in safety guardrails reducing harmful outputs","Reduce liability and compliance risk by refusing illegal, abusive, or dangerous requests","Create user-facing applications where safety is a core requirement"],"best_for":["Public-facing applications requiring baseline safety protections","Organizations with compliance requirements (healthcare, finance, education)","Teams building consumer-grade chatbots and assistants"],"limitations":["Safety alignment is probabilistic and can be circumvented through adversarial prompting (jailbreaks); no absolute safety guarantees","Over-refusal: model sometimes declines benign requests due to overly broad safety training, reducing utility","Safety training is English-centric; non-English languages have weaker safety alignment","No real-time content moderation; safety decisions are made at generation time with no external oversight","Cannot handle novel harm categories not covered in training data; adversarial or emerging harms may bypass safety mechanisms"],"requires":["Python 3.8+","HuggingFace transformers 4.36.0+","Optional: external content moderation APIs for additional safety layers","Optional: red-teaming and adversarial testing to identify safety gaps"],"input_types":["user prompts (benign and potentially harmful)","system prompts defining safety boundaries"],"output_types":["helpful responses to benign requests","refusal messages explaining why harmful requests cannot be fulfilled","alternative suggestions for reframing harmful requests"],"categories":["safety-moderation","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-meta-llama--llama-3.1-8b-instruct__headline","uri":"capability://text.generation.language.text.generation.model.for.chatbots.and.assistants","name":"text-generation model for chatbots and assistants","description":"Llama-3.1-8B-Instruct is a powerful text-generation model designed for creating conversational agents and chatbots, enabling natural and engaging interactions.","intents":["best text-generation model","text-generation model for chatbots","top conversational AI models","best AI for generating dialogue","text-generation solutions for customer support"],"best_for":[],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":56,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","PyTorch 2.0+ or compatible deep learning framework","Minimum 16GB RAM for quantized inference (8-bit), 32GB+ for full precision","GPU with 8GB+ VRAM recommended (NVIDIA CUDA 11.8+, AMD ROCm 5.7+, or Apple Metal for M-series)","HuggingFace transformers library 4.36.0+","SafeTensors format support for efficient model loading","HuggingFace transformers 4.36.0+","Support for UTF-8 encoding in input/output pipelines","Language-specific fonts/rendering for proper display (especially Thai, Hindi, Japanese)","Carefully curated examples relevant to target task"],"failure_modes":["8B parameter model trades off reasoning depth vs. larger models (70B, 405B); struggles with complex multi-step mathematical reasoning and specialized domain knowledge","Context window of 128K tokens is fixed; cannot dynamically extend for extremely long documents without chunking strategies","Inference latency on CPU-only systems ranges 5-15 seconds per response; GPU acceleration (NVIDIA, AMD) required for sub-second latency","Knowledge cutoff date limits real-time information; no built-in web search or external knowledge integration","Multilingual performance degrades for low-resource languages (Thai, Hindi) vs. English; quality variance across language pairs","No explicit language detection or routing; relies on context to determine output language, sometimes producing mixed-language outputs","Training data imbalance favors English; non-English languages receive less instruction-tuning coverage, reducing instruction-following quality","Character-level phenomena (tone marks in Thai, diacritics in Portuguese) occasionally mishandled due to tokenizer limitations","Few-shot learning effectiveness depends heavily on example quality and relevance; poor examples degrade performance","Context window is shared between examples and input; providing many examples reduces space for actual task input","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.9500775015191898,"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":9566721,"model_likes":5782}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=meta-llama--llama-3.1-8b-instruct","compare_url":"https://unfragile.ai/compare?artifact=meta-llama--llama-3.1-8b-instruct"}},"signature":"7LPPuhJr/elrql4SzQw2edK72KtsXMDXulQwR2j6vyjVG+WETGQTs+niNvhtT1314i8yNHFHYivVI45/3KCrBQ==","signedAt":"2026-06-22T21:17:25.536Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/meta-llama--llama-3.1-8b-instruct","artifact":"https://unfragile.ai/meta-llama--llama-3.1-8b-instruct","verify":"https://unfragile.ai/api/v1/verify?slug=meta-llama--llama-3.1-8b-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"}}