{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"capybara","slug":"capybara","name":"Capybara","type":"dataset","url":"https://huggingface.co/datasets/LDJnr/Capybara","page_url":"https://unfragile.ai/capybara","categories":["model-training","testing-quality"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"capybara__cap_0","uri":"capability://data.processing.analysis.multi.turn.dialogue.dataset.curation.with.reasoning.chains","name":"multi-turn dialogue dataset curation with reasoning chains","description":"Provides a curated collection of multi-turn conversations structured to capture complex reasoning patterns, instruction-following behaviors, and dialogue coherence. The dataset is organized as conversation sequences with explicit reasoning chains embedded within turns, enabling models to learn step-by-step problem decomposition and justification patterns during fine-tuning. Data is hosted on Hugging Face Hub with streaming and local caching support via the datasets library.","intents":["Fine-tune a language model to follow complex, multi-step instructions with explicit reasoning","Train models that can engage in nuanced, context-aware dialogue across diverse topics","Create instruction-following models that explain their reasoning process in responses","Build steerable models that adapt behavior based on conversational context and user intent"],"best_for":["ML engineers training custom instruction-tuned models for production deployment","Researchers studying dialogue quality and reasoning chain emergence in LLMs","Teams building domain-specific conversational AI with complex task requirements"],"limitations":["Dataset size and composition not fully documented — unclear how many conversations, average turn count, or topic distribution","No built-in filtering or stratification by difficulty level, reasoning complexity, or domain","Requires external evaluation framework to measure reasoning quality improvements post-training","No versioning or changelog — unclear if dataset has been updated or if quality issues have been addressed","Language coverage unknown — likely English-dominant, limiting multilingual training applications"],"requires":["Python 3.7+","Hugging Face datasets library (pip install datasets)","Hugging Face account for authenticated access if dataset is gated","GPU memory for batch processing (16GB+ recommended for efficient loading)","Training framework (PyTorch, TensorFlow, or similar) to consume dataset"],"input_types":["structured conversation JSON/Parquet format","multi-turn dialogue sequences with speaker labels"],"output_types":["training-ready tensor batches","conversation dictionaries with reasoning annotations","dialogue turn pairs for supervised fine-tuning"],"categories":["data-processing-analysis","model-training"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"capybara__cap_1","uri":"capability://data.processing.analysis.instruction.response.pair.extraction.and.formatting","name":"instruction-response pair extraction and formatting","description":"Transforms raw multi-turn conversation data into structured instruction-response pairs optimized for supervised fine-tuning (SFT). The dataset encodes conversation context, speaker roles, and reasoning annotations into a format compatible with standard LLM training pipelines (e.g., Hugging Face Transformers, LLaMA-Factory). Handles variable-length contexts and supports both single-turn and multi-turn context windows.","intents":["Extract clean instruction-response pairs from conversational data for SFT training","Preserve multi-turn context when training models to maintain dialogue coherence","Format conversations into tokenizer-friendly sequences with proper attention masking","Create training batches that balance instruction diversity with reasoning depth"],"best_for":["ML engineers implementing supervised fine-tuning pipelines for instruction-tuned models","Teams using Hugging Face Transformers or similar frameworks for model training","Researchers comparing instruction-tuning datasets with different context window strategies"],"limitations":["No explicit documentation of context window handling — unclear if truncation, sliding windows, or full-conversation encoding is used","Formatting may not be optimized for all tokenizers — potential token count mismatches with different base models","No built-in support for weighted sampling by reasoning complexity or instruction difficulty","Requires manual validation that extracted pairs preserve semantic coherence across context boundaries"],"requires":["Python 3.7+","Hugging Face datasets library","Tokenizer compatible with target model (e.g., LLaMA, Mistral, GPT tokenizers)","Storage for processed dataset (10GB+ depending on full dataset size)"],"input_types":["multi-turn conversation sequences with speaker labels","reasoning annotations or chain-of-thought markers"],"output_types":["instruction-response pair dictionaries","tokenized sequences with attention masks","training batches in PyTorch DataLoader format"],"categories":["data-processing-analysis","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"capybara__cap_2","uri":"capability://data.processing.analysis.diverse.topic.coverage.with.nuanced.instruction.variants","name":"diverse topic coverage with nuanced instruction variants","description":"Curates conversations across multiple domains and topic areas, with intentional variation in instruction phrasing, complexity, and specificity. The dataset includes examples where the same underlying task is expressed with different levels of detail, formality, and constraint specification, teaching models to handle instruction ambiguity and adapt to varied user communication styles. Topics span technical, creative, analytical, and interpersonal domains.","intents":["Train models that generalize across diverse instruction phrasings and communication styles","Build models robust to ambiguous or under-specified instructions","Create instruction-following models that work across technical and non-technical domains","Develop models that can clarify or ask for missing information when instructions are vague"],"best_for":["Teams building general-purpose instruction-tuned models for broad user bases","Researchers studying instruction robustness and generalization across domains","Product teams training models for consumer-facing applications with diverse user inputs"],"limitations":["Topic distribution and coverage not publicly documented — unclear which domains are over/under-represented","No explicit stratification by instruction complexity, ambiguity level, or domain difficulty","Nuance variations may not be systematic — unclear if instruction variants are algorithmically generated or manually curated","No metadata tags for topic, domain, or instruction style — requires manual inspection to understand coverage","Potential bias toward certain instruction phrasings or communication styles depending on curation methodology"],"requires":["Python 3.7+","Hugging Face datasets library","Domain expertise to validate topic coverage for specific use cases","Evaluation framework to measure generalization across instruction variants"],"input_types":["multi-turn conversations with topic/domain labels","instruction variants with different phrasing and specificity levels"],"output_types":["training examples grouped by topic or instruction style","stratified batches for balanced domain coverage","instruction-response pairs with domain metadata"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"capybara__cap_3","uri":"capability://data.processing.analysis.reasoning.chain.annotation.and.step.by.step.decomposition","name":"reasoning chain annotation and step-by-step decomposition","description":"Embeds explicit reasoning chains and step-by-step problem decomposition within conversation turns, allowing models to learn intermediate reasoning steps rather than just final answers. The dataset includes examples where models articulate their reasoning process, break down complex problems into sub-steps, and justify intermediate conclusions. This enables training of models that can produce interpretable, verifiable reasoning traces.","intents":["Train models that produce explicit reasoning chains and intermediate steps in responses","Build models capable of step-by-step problem decomposition for complex tasks","Create models that can justify their reasoning and explain decision-making processes","Develop models that learn to catch and correct reasoning errors through explicit step verification"],"best_for":["Teams building reasoning-focused models for scientific, mathematical, or analytical tasks","Researchers studying chain-of-thought emergence and reasoning quality in LLMs","Product teams requiring explainable AI with verifiable reasoning traces"],"limitations":["Reasoning chain quality and consistency not documented — unclear if chains are human-verified or algorithmically generated","No explicit metrics for reasoning correctness, completeness, or efficiency","Chains may not cover all problem-solving approaches — potential bias toward specific reasoning styles","Requires external evaluation to measure if trained models actually produce correct intermediate steps","No stratification by reasoning complexity or task type — unclear if chains are uniformly detailed across domains"],"requires":["Python 3.7+","Hugging Face datasets library","Training framework supporting longer sequences (reasoning chains increase token count)","Evaluation framework for measuring reasoning quality (e.g., step correctness, logical consistency)"],"input_types":["multi-turn conversations with embedded reasoning annotations","step-by-step decomposition markers or chain-of-thought labels"],"output_types":["training examples with reasoning chains as target outputs","intermediate step predictions for multi-step reasoning tasks","structured reasoning traces with justification annotations"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"capybara__cap_4","uri":"capability://data.processing.analysis.steerable.model.behavior.through.contextual.instruction.adaptation","name":"steerable model behavior through contextual instruction adaptation","description":"Includes conversation examples where model behavior adapts based on user intent shifts, constraint changes, or clarifications within a single dialogue thread. The dataset demonstrates how models should modify their approach, tone, or output format in response to evolving user requirements. This teaches models to be 'steerable' — responsive to mid-conversation instruction changes rather than locked into initial behavior patterns.","intents":["Train models that adapt behavior and output style based on user feedback within a conversation","Build models that respond to constraint changes or clarifications without losing context","Create models that can shift between different reasoning approaches or output formats on demand","Develop models that maintain coherence while adapting to evolving user intent"],"best_for":["Teams building interactive AI systems where users refine requirements mid-conversation","Researchers studying instruction-following robustness and behavioral adaptability","Product teams creating conversational interfaces for complex, iterative tasks"],"limitations":["Adaptation patterns and transition quality not documented — unclear if behavior shifts are smooth or abrupt","No explicit metrics for measuring steerability or adaptation fidelity","Limited documentation on which types of instruction changes are covered (tone, format, constraints, etc.)","Requires careful evaluation to ensure models don't lose context or coherence during adaptation","Potential for models to over-fit to specific adaptation patterns rather than generalizing to novel instruction changes"],"requires":["Python 3.7+","Hugging Face datasets library","Training framework supporting long context windows (adaptation requires maintaining full conversation history)","Evaluation framework for measuring behavioral consistency and adaptation quality"],"input_types":["multi-turn conversations with instruction changes or clarifications","examples of user feedback or constraint modifications mid-dialogue"],"output_types":["model responses that adapt to instruction changes","behavior shift examples with maintained context coherence","training pairs demonstrating instruction-responsive behavior"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"capybara__cap_5","uri":"capability://data.processing.analysis.high.quality.dialogue.filtering.and.quality.assurance","name":"high-quality dialogue filtering and quality assurance","description":"Applies curation and filtering to ensure conversation quality, coherence, and factual accuracy. The dataset excludes low-quality turns, incoherent exchanges, and factually incorrect information through manual review or automated quality metrics. This produces a higher-signal training set compared to raw web-scraped dialogue data, reducing noise and improving model training efficiency.","intents":["Train models on high-quality dialogue examples that improve response coherence and accuracy","Reduce training noise from low-quality or incoherent conversation data","Build models with better factual grounding by filtering out incorrect information","Improve training efficiency by focusing on curated, high-signal examples"],"best_for":["Teams training production models where dialogue quality directly impacts user experience","Researchers studying the impact of data quality on model performance and coherence","Organizations with limited training budgets seeking to maximize signal-to-noise ratio"],"limitations":["Quality filtering methodology not documented — unclear what metrics or human review standards were applied","No explicit quality scores or metadata indicating which examples are highest quality","Filtering criteria may not align with specific use cases — quality for one domain may differ from another","Potential for over-filtering to remove edge cases or challenging examples that improve robustness","No transparency on false negatives — unclear if high-quality examples were incorrectly filtered out"],"requires":["Python 3.7+","Hugging Face datasets library","Domain expertise to validate quality standards for specific applications","Evaluation framework to measure quality improvements in trained models"],"input_types":["raw or pre-filtered multi-turn conversations","quality annotations or filtering metadata"],"output_types":["curated conversation sequences meeting quality thresholds","training examples with quality scores or confidence levels","filtered dialogue pairs for supervised 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