{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"falcon-180b","slug":"falcon-180b","name":"Falcon 180B","type":"model","url":"https://falconllm.tii.ae/","page_url":"https://unfragile.ai/falcon-180b","categories":["model-training","testing-quality"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"falcon-180b__cap_0","uri":"capability://text.generation.language.large.scale.autoregressive.text.generation.with.180b.parameters","name":"large-scale autoregressive text generation with 180b parameters","description":"Generates coherent multi-token text sequences using a 180-billion parameter transformer architecture trained on 3.5 trillion tokens from RefinedWeb. The model employs standard autoregressive decoding (predicting next token given previous context) with learned attention patterns across the full parameter space. Supports variable-length prompts and generates text until end-of-sequence or max-length constraints are reached, enabling open-ended content creation, summarization, and dialogue.","intents":["Generate long-form text content from short prompts or partial documents","Complete code snippets, documentation, or creative writing with contextual coherence","Build chatbots or conversational agents that maintain semantic consistency across turns","Perform zero-shot or few-shot task adaptation by conditioning on in-context examples"],"best_for":["Research teams and enterprises requiring state-of-the-art open-source language capabilities without vendor lock-in","Organizations with sufficient GPU infrastructure (8+ A100 80GB) willing to self-host for data privacy","Developers building specialized domain applications where fine-tuning on proprietary data is required"],"limitations":["Requires minimum 8x A100 80GB GPUs for inference (~360GB full precision memory footprint), making deployment cost-prohibitive for most small teams","No quantized variants documented in provided source material, limiting deployment to high-end hardware","Context window size unknown — may be limited compared to newer models (e.g., Claude 3's 200K tokens)","Inference speed benchmarks not provided; actual tokens/second throughput unknown","No built-in safety alignment or instruction-following fine-tuning documented — base model may require additional RLHF for production use"],"requires":["8x NVIDIA A100 80GB GPUs minimum for full-precision inference","CUDA 11.8+ and cuDNN 8.6+ for GPU acceleration","PyTorch 2.0+ or equivalent inference framework (vLLM, TensorRT, or similar)","360GB+ GPU memory for loading full 180B parameter model in float32","Access to model weights (Hugging Face Hub or TII repository — format unknown)"],"input_types":["text (natural language prompts, code snippets, few-shot examples)","structured prompts with system instructions or role definitions"],"output_types":["text (generated sequences of variable length)","logits (raw probability distributions over vocabulary for custom sampling)"],"categories":["text-generation-language","foundation-model"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"falcon-180b__cap_1","uri":"capability://planning.reasoning.reasoning.and.multi.step.problem.decomposition","name":"reasoning and multi-step problem decomposition","description":"Demonstrates strong performance on reasoning benchmarks through learned patterns in chain-of-thought problem solving, enabling the model to break complex queries into intermediate steps and derive conclusions. The 180B parameter capacity and 3.5T token training on diverse RefinedWeb data enable the model to recognize reasoning patterns across domains (mathematics, logic, code analysis) without explicit reasoning-specific fine-tuning. Supports prompting techniques like few-shot examples and explicit step-by-step instructions to elicit structured reasoning.","intents":["Solve multi-step math problems by generating intermediate calculations and logical steps","Debug code by reasoning about program state, control flow, and potential error sources","Answer complex knowledge questions requiring synthesis of multiple facts or logical inference","Generate structured analysis of documents or scenarios with explicit reasoning justification"],"best_for":["AI research teams evaluating reasoning capabilities of open-source models","Organizations building question-answering or knowledge-work automation systems","Developers creating AI tutoring systems that need to explain reasoning steps"],"limitations":["Reasoning performance benchmarks not specified in documentation — 'competitive with early GPT-4' claim is unverified and lacks specific MMLU, GSM8K, or ARC scores","No explicit chain-of-thought fine-tuning documented; reasoning emerges from scale and data quality rather than specialized training","Reasoning quality degrades with longer chains (typical transformer limitation) — context window constraints unknown","No built-in guardrails against logical fallacies or hallucinations in reasoning steps"],"requires":["8x A100 80GB GPUs for inference","Prompt engineering expertise to elicit structured reasoning (few-shot examples, explicit step-by-step instructions)","Evaluation framework to validate reasoning correctness (no built-in verification)"],"input_types":["text prompts with explicit reasoning requests","few-shot examples demonstrating step-by-step problem solving","structured queries with intermediate checkpoints"],"output_types":["text with intermediate reasoning steps and final conclusions","structured reasoning traces (if prompted with specific formatting)"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"falcon-180b__cap_2","uri":"capability://text.generation.language.knowledge.retrieval.and.factual.question.answering","name":"knowledge retrieval and factual question answering","description":"Answers factual questions by leveraging 3.5 trillion tokens of training data from RefinedWeb, which includes diverse knowledge sources (web text, reference materials, technical documentation). The model encodes factual knowledge in its parameters through standard transformer training, enabling zero-shot retrieval of facts without external knowledge bases. Supports both direct factual queries and complex multi-fact synthesis, though accuracy degrades on recent events or specialized domains not well-represented in training data.","intents":["Answer general knowledge questions about history, science, geography, and culture","Retrieve technical information about programming languages, APIs, and software frameworks","Synthesize information from multiple facts to answer complex analytical questions","Provide definitions, explanations, and context for domain-specific terminology"],"best_for":["Teams building question-answering systems for general knowledge domains","Educational applications requiring factual explanations without external API calls","Organizations needing offline knowledge retrieval without dependency on search engines or external APIs"],"limitations":["Knowledge cutoff date unknown — likely trained on data up to ~2023, making recent events or current information unreliable","No mechanism to cite sources or provide evidence for factual claims — answers appear authoritative but may be hallucinated","Factual accuracy not quantified in documentation — 'competitive with early GPT-4' claim unverified for knowledge benchmarks","No built-in fact-checking or confidence scoring — model cannot distinguish high-confidence from low-confidence knowledge","Biases in RefinedWeb dataset (web-sourced data skews toward English, Western perspectives, and popular topics) propagate to model outputs"],"requires":["8x A100 80GB GPUs for inference","Understanding of model limitations and hallucination risks for production deployment","Evaluation dataset to measure factual accuracy on domain-specific questions"],"input_types":["natural language questions (factual, analytical, definitional)","multi-part questions requiring synthesis of multiple facts"],"output_types":["text answers with explanations","structured knowledge (if prompted with specific formatting)"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"falcon-180b__cap_3","uri":"capability://code.generation.editing.code.generation.and.programming.task.completion","name":"code generation and programming task completion","description":"Generates code across multiple programming languages by learning patterns from code-containing portions of RefinedWeb training data. The model predicts syntactically valid code sequences given natural language descriptions, partial code, or function signatures. Supports completion of functions, classes, scripts, and documentation with context-aware indentation and language-specific conventions. Reasoning capability enables debugging and refactoring suggestions, though code correctness is not guaranteed.","intents":["Auto-complete code functions or methods from natural language descriptions or partial implementations","Generate boilerplate code for common patterns (API handlers, database queries, test cases)","Translate algorithms between programming languages or refactor code for readability","Generate documentation, docstrings, and code comments from function signatures"],"best_for":["Developers using local development environments with sufficient GPU resources","Teams building code-generation features into IDEs or development tools","Organizations requiring code generation without sending code to third-party APIs (data privacy)"],"limitations":["Code correctness not guaranteed — model may generate syntactically valid but logically incorrect code","No built-in testing or validation — generated code requires manual review and testing","Supported languages unknown — likely biased toward popular languages (Python, JavaScript, Java) with less coverage for niche languages","Context window constraints unknown — may struggle with large files or complex multi-file dependencies","No integration with language servers, linters, or type checkers for real-time validation","Training data cutoff unknown — may not include recent language features or library APIs"],"requires":["8x A100 80GB GPUs for inference","IDE or editor integration (not provided by TII — requires custom implementation)","Code review process to validate generated code before deployment"],"input_types":["natural language descriptions of desired code behavior","partial code with function signatures or docstrings","code snippets for refactoring or translation","comments or documentation prompts"],"output_types":["code in target programming language","multiple code variants (if sampled with temperature > 0)","explanations of generated code logic"],"categories":["code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"falcon-180b__cap_4","uri":"capability://planning.reasoning.few.shot.in.context.learning.and.task.adaptation","name":"few-shot in-context learning and task adaptation","description":"Adapts to new tasks by learning from examples provided in the prompt (few-shot learning) without requiring model fine-tuning or retraining. The model uses 180B parameters to recognize patterns from 2-5 input-output examples and generalize to new instances of the same task. This capability emerges from transformer attention mechanisms that can bind task-specific patterns to the current context window. Supports diverse task types: classification, extraction, summarization, translation, and reasoning.","intents":["Classify text into custom categories by providing 3-5 labeled examples without retraining","Extract structured information (entities, relationships, attributes) from unstructured text using example patterns","Translate between domain-specific terminology or jargon by demonstrating mappings in examples","Adapt the model to new domains (medical, legal, technical) by providing domain-specific examples"],"best_for":["Teams needing rapid task adaptation without fine-tuning infrastructure","Organizations with domain-specific tasks that change frequently or have small labeled datasets","Researchers studying emergent capabilities and generalization in large language models"],"limitations":["Few-shot performance degrades with task complexity — simple classification works well, but complex reasoning may require more examples than context window allows","Context window size unknown — limits number of examples that can be provided (typical LLMs support 5-10 examples before context exhaustion)","No explicit few-shot optimization in training — performance depends on emergent capabilities rather than specialized training","Example quality and ordering significantly impact performance — no automatic example selection or ordering","Hallucination risk increases with few-shot learning — model may invent plausible but incorrect patterns from limited examples"],"requires":["8x A100 80GB GPUs for inference","Carefully curated examples that represent task distribution","Prompt engineering expertise to format examples and instructions effectively","Evaluation on held-out test set to validate few-shot performance"],"input_types":["prompt with task description and few examples","new instances to apply learned task pattern","structured or unstructured input data"],"output_types":["predictions following example pattern","structured outputs (if examples demonstrate structure)","explanations of reasoning (if prompted)"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"falcon-180b__cap_5","uri":"capability://automation.workflow.self.hosted.inference.with.apache.2.0.licensed.weights","name":"self-hosted inference with apache 2.0 licensed weights","description":"Provides fully open-source model weights under Apache 2.0 license, enabling unrestricted self-hosted deployment without vendor lock-in, licensing fees, or API rate limits. Organizations download model weights from Hugging Face or TII repositories and run inference on their own infrastructure using frameworks like PyTorch, vLLM, or TensorRT. Apache 2.0 license permits commercial use, redistribution, and modification, enabling custom fine-tuning and integration into proprietary products without legal restrictions.","intents":["Deploy language model capabilities on-premises for data privacy and regulatory compliance (HIPAA, GDPR, SOC 2)","Integrate model into proprietary products or services without API dependencies or usage-based pricing","Fine-tune model on proprietary data without sharing data with third-party API providers","Customize inference pipeline (quantization, pruning, distillation) for specific hardware or latency requirements"],"best_for":["Enterprises with strict data privacy requirements or regulatory constraints","Organizations with sufficient GPU infrastructure and MLOps expertise to manage self-hosted models","Teams building commercial products that require language model capabilities without API dependencies","Research institutions requiring full model transparency and ability to modify architecture"],"limitations":["Requires 8x A100 80GB GPUs minimum — significant capital and operational expense (~$100K+ hardware cost, $10K+/month electricity)","No managed inference service provided by TII — organizations must build/maintain deployment infrastructure, monitoring, and scaling","Model format unknown (GGUF, safetensors, etc.) — may require conversion or compatibility work with specific inference frameworks","No official support or SLA — organizations rely on community documentation and self-troubleshooting","Quantized variants not documented — full-precision inference requires 360GB+ GPU memory, limiting deployment options"],"requires":["8x NVIDIA A100 80GB GPUs (or equivalent high-end GPUs)","CUDA 11.8+, cuDNN 8.6+, PyTorch 2.0+ or equivalent inference framework","MLOps infrastructure for model serving (Kubernetes, Docker, load balancing)","Monitoring and observability tools for production deployment","Legal review of Apache 2.0 license terms for commercial use case"],"input_types":["model weights (downloaded from repository)","inference requests (text prompts via API or direct Python calls)"],"output_types":["generated text responses","logits or embeddings (if inference framework supports)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"falcon-180b__cap_6","uri":"capability://text.generation.language.multi.domain.knowledge.synthesis.and.cross.domain.transfer","name":"multi-domain knowledge synthesis and cross-domain transfer","description":"Synthesizes knowledge across diverse domains (science, technology, humanities, business) by learning from 3.5 trillion tokens of RefinedWeb data spanning multiple knowledge areas. The 180B parameter capacity enables the model to learn domain-specific terminology, concepts, and reasoning patterns while maintaining cross-domain connections. Supports transfer learning where knowledge from one domain (e.g., physics) informs reasoning in another domain (e.g., engineering), enabling novel problem-solving approaches and analogical reasoning.","intents":["Answer questions requiring synthesis of knowledge from multiple domains (e.g., 'How do neural networks relate to biological neurons?')","Generate creative solutions by applying concepts from one domain to problems in another","Explain complex topics by drawing analogies to more familiar domains","Identify connections and patterns across seemingly unrelated fields"],"best_for":["Educational platforms requiring comprehensive knowledge across subjects","Research teams exploring interdisciplinary connections and novel approaches","Content creation platforms needing diverse knowledge for writing and analysis","Organizations building general-purpose AI assistants for knowledge workers"],"limitations":["Cross-domain transfer quality not quantified — no benchmarks measuring analogical reasoning or transfer learning capability","Domain-specific accuracy may be lower than specialized models — 180B general model may underperform domain-specific 7B models on technical tasks","Knowledge integration biased toward popular domains well-represented in web data — niche or emerging fields may lack sufficient training coverage","No mechanism to weight domain expertise or confidence — model treats all domains equally regardless of training data quality","Hallucinations may be more likely in cross-domain synthesis where model must invent connections between domains"],"requires":["8x A100 80GB GPUs for inference","Domain expertise to validate cross-domain synthesis and identify hallucinations","Evaluation framework measuring transfer learning quality on held-out cross-domain tasks"],"input_types":["cross-domain questions requiring synthesis","prompts requesting analogies or connections between domains","multi-part questions spanning different knowledge areas"],"output_types":["synthesized explanations connecting multiple domains","analogies and metaphors bridging domains","structured knowledge maps (if prompted with specific formatting)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"falcon-180b__cap_7","uri":"capability://text.generation.language.long.context.understanding.and.multi.document.reasoning","name":"long-context understanding and multi-document reasoning","description":"Processes extended text sequences and reasons across multiple documents by leveraging transformer attention mechanisms that can attend to distant context. The model maintains semantic coherence over long passages and synthesizes information from multiple sources within a single inference pass. Supports document-level tasks like summarization, comparative analysis, and cross-document question answering without requiring external retrieval systems.","intents":["Summarize long documents (research papers, reports, articles) while preserving key information and structure","Answer questions requiring information from multiple documents or sections of a document","Compare and contrast information across multiple sources (competitive analysis, literature review)","Extract key insights from long conversations or meeting transcripts"],"best_for":["Organizations processing large documents (legal contracts, research papers, technical documentation)","Teams building document analysis and summarization tools","Research institutions analyzing multi-document collections without external retrieval systems"],"limitations":["Context window size unknown — likely 2K-4K tokens (standard for 2023 models), limiting document length to ~1500-3000 words","Attention complexity grows quadratically with context length — inference latency increases significantly with longer documents","Performance degrades toward end of long context (lost-in-the-middle problem) — information in middle of document may be underweighted","No explicit long-context fine-tuning documented — long-context capability emerges from scale rather than specialized training","Multi-document reasoning limited by context window — cannot process multiple full documents simultaneously without truncation"],"requires":["8x A100 80GB GPUs for inference","Document preprocessing to fit within context window (chunking, summarization, or filtering)","Evaluation framework measuring summarization quality and information retention"],"input_types":["long text documents (up to context window limit)","multiple documents concatenated within context window","questions about document content"],"output_types":["summaries of variable length","answers to document-based questions","comparative analysis across documents"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"falcon-180b__cap_8","uri":"capability://text.generation.language.instruction.following.and.task.specific.prompt.adaptation","name":"instruction-following and task-specific prompt adaptation","description":"Follows natural language instructions to perform specific tasks by learning instruction-following patterns from training data. The model interprets task descriptions, constraints, and output format requirements from prompts and generates outputs matching specified criteria. Supports diverse instruction types: classification, extraction, generation, analysis, and creative tasks. Instruction-following capability emerges from training on diverse RefinedWeb data containing instructional text, though no explicit instruction-tuning fine-tuning is documented.","intents":["Perform custom tasks by providing natural language instructions without fine-tuning","Constrain output format (JSON, CSV, bullet points) through prompt instructions","Specify tone, style, or perspective for generated content (formal, casual, technical, creative)","Implement multi-step workflows by chaining instructions in a single prompt"],"best_for":["Teams building flexible AI systems that adapt to user-specified tasks","Organizations requiring custom task adaptation without fine-tuning infrastructure","Developers creating prompt-based automation workflows"],"limitations":["Instruction-following quality not quantified — no benchmarks measuring instruction adherence or constraint satisfaction","Complex instructions may be misinterpreted — model may ignore constraints or misunderstand multi-part instructions","No explicit instruction-tuning fine-tuning documented — instruction-following capability is weaker than models explicitly trained on instruction datasets (e.g., InstructGPT, Alpaca)","Output format constraints not guaranteed — model may ignore JSON/CSV formatting requests or produce malformed output","Instruction sensitivity high — small changes in wording significantly impact output quality"],"requires":["8x A100 80GB GPUs for inference","Prompt engineering expertise to craft clear, unambiguous instructions","Output validation to verify format and constraint compliance"],"input_types":["natural language instructions describing task","constraints on output format, style, or content","input data to process according to instructions"],"output_types":["task-specific outputs matching instruction criteria","structured outputs (JSON, CSV) if format specified","explanations or reasoning (if requested in instructions)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"falcon-180b__headline","uri":"capability://text.generation.language.open.source.large.language.model.for.reasoning.and.knowledge.tasks","name":"open-source large language model for reasoning and knowledge tasks","description":"Falcon 180B is an open-source large language model with 180 billion parameters, designed for high-performance reasoning and knowledge tasks, making it a strong alternative to proprietary models like GPT-4.","intents":["best open-source large language model","large language model for reasoning tasks","top models for knowledge-based tasks","open-source alternatives to GPT-4","AI models for text generation and reasoning"],"best_for":["research","development","AI applications requiring reasoning"],"limitations":["requires significant compute resources"],"requires":["8x A100 80GB GPUs"],"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":"high","permissions":["8x NVIDIA A100 80GB GPUs minimum for full-precision inference","CUDA 11.8+ and cuDNN 8.6+ for GPU acceleration","PyTorch 2.0+ or equivalent inference framework (vLLM, TensorRT, or similar)","360GB+ GPU memory for loading full 180B parameter model in float32","Access to model weights (Hugging Face Hub or TII repository — format unknown)","8x A100 80GB GPUs for inference","Prompt engineering expertise to elicit structured reasoning (few-shot examples, explicit step-by-step instructions)","Evaluation framework to validate reasoning correctness (no built-in verification)","Understanding of model limitations and hallucination risks for production deployment","Evaluation dataset to measure factual accuracy on domain-specific questions"],"failure_modes":["Requires minimum 8x A100 80GB GPUs for inference (~360GB full precision memory footprint), making deployment cost-prohibitive for most small teams","No quantized variants documented in provided source material, limiting deployment to high-end hardware","Context window size unknown — may be limited compared to newer models (e.g., Claude 3's 200K tokens)","Inference speed benchmarks not provided; actual tokens/second throughput unknown","No built-in safety alignment or instruction-following fine-tuning documented — base model may require additional RLHF for production use","Reasoning performance benchmarks not specified in documentation — 'competitive with early GPT-4' claim is unverified and lacks specific MMLU, GSM8K, or ARC scores","No explicit chain-of-thought fine-tuning documented; reasoning emerges from scale and data quality rather than specialized training","Reasoning quality degrades with longer chains (typical transformer limitation) — context window constraints unknown","No built-in guardrails against logical fallacies or hallucinations in reasoning steps","Knowledge cutoff date unknown — likely trained on data up to ~2023, making recent events or current information unreliable","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.8500000000000001,"ecosystem":0.39999999999999997,"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":"2026-05-24T12:16:21.548Z","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=falcon-180b","compare_url":"https://unfragile.ai/compare?artifact=falcon-180b"}},"signature":"aWK2AZif0AEoJVX+mCOeFdXTbh1pKLZ5xbtFB9Zn+DadPPrqpOdErlhVGd8V59TmnluFhzA5EO4WWxWua9yrBQ==","signedAt":"2026-06-21T12:00:23.879Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/falcon-180b","artifact":"https://unfragile.ai/falcon-180b","verify":"https://unfragile.ai/api/v1/verify?slug=falcon-180b","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"}}