{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"magpie","slug":"magpie","name":"Magpie","type":"dataset","url":"https://huggingface.co/datasets/Magpie-Align/Magpie-Pro-300K-Filtered","page_url":"https://unfragile.ai/magpie","categories":["model-training","testing-quality"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"magpie__cap_0","uri":"capability://data.processing.analysis.reverse.instruction.generation.from.aligned.models","name":"reverse-instruction-generation-from-aligned-models","description":"Extracts instruction-response pairs by leveraging the latent instruction distribution within aligned LLMs through a two-stage generation process: first, a pre-filled assistant template prompts the model to generate the user instruction in reverse, then the model completes its own response to that instruction. This approach bypasses the need for human-authored seed instructions, instead harvesting the model's own understanding of what constitutes valid tasks and appropriate responses.","intents":["Generate diverse instruction datasets without manual annotation overhead","Create training data that reflects the capabilities and alignment of a specific base model","Scale instruction dataset creation beyond human-curated seed data limitations","Produce instruction pairs that are naturally aligned with model behavior patterns"],"best_for":["ML researchers training instruction-tuned models with limited human annotation budgets","Teams building domain-specific LLMs that need diverse task coverage","Organizations seeking to distill knowledge from larger aligned models into smaller ones"],"limitations":["Quality ceiling bounded by the base model's own capabilities and biases — cannot generate instructions for tasks the source model cannot perform","Potential for distribution drift if the base model's instruction understanding diverges from human expectations","Requires a pre-aligned model as input; cannot be applied to base models without instruction-following capability","Generated instructions may exhibit similar failure modes or blind spots as the source model"],"requires":["Access to an aligned LLM (e.g., GPT-3.5, Claude, Llama-2-Chat) with API or local inference capability","Computational resources for batch generation of 300K+ examples (GPU recommended for inference speed)","Filtering pipeline to remove low-quality or duplicate examples post-generation"],"input_types":["pre-filled assistant template (text prompt structure)","model configuration parameters (temperature, max_tokens, sampling strategy)"],"output_types":["instruction-response pairs (JSON/JSONL format)","structured dataset with metadata (task category, difficulty, source model)"],"categories":["data-processing-analysis","model-training"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"magpie__cap_1","uri":"capability://data.processing.analysis.filtered.instruction.dataset.curation","name":"filtered-instruction-dataset-curation","description":"Applies multi-stage filtering and quality control to the 300K generated instruction-response pairs to remove duplicates, low-quality examples, and off-distribution samples. The filtering pipeline likely includes deduplication hashing, length/complexity thresholds, and potentially model-based quality scoring to retain only high-fidelity examples suitable for downstream training.","intents":["Remove duplicate and near-duplicate instruction pairs from synthetic generation","Filter out malformed, incomplete, or incoherent examples before training","Ensure dataset quality meets standards for supervised fine-tuning","Reduce dataset size while maintaining diversity and coverage"],"best_for":["Teams preparing synthetic datasets for production model training","Researchers validating dataset quality before publication","Organizations with strict data quality requirements for fine-tuning"],"limitations":["Filtering heuristics may remove valid but unusual instructions, reducing long-tail task coverage","Quality thresholds are dataset-specific and may not generalize across domains","No transparency into exact filtering criteria used in the published 300K subset","Potential for filtering to introduce systematic biases if thresholds favor certain task types"],"requires":["Raw generated instruction dataset (pre-filtered version)","Deduplication and quality scoring infrastructure","Computational resources for batch processing and filtering"],"input_types":["raw instruction-response pairs (JSONL format)","quality scoring metrics (optional: model-based or heuristic-based)"],"output_types":["filtered instruction dataset (300K examples in JSONL/Parquet format)","quality statistics and filtering reports"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"magpie__cap_2","uri":"capability://data.processing.analysis.diverse.task.coverage.instruction.distribution","name":"diverse-task-coverage-instruction-distribution","description":"The generated dataset covers diverse task categories and instruction types by leveraging the aligned model's broad instruction distribution. The reverse-generation approach naturally samples from the model's learned task space, producing instructions across multiple domains (writing, coding, reasoning, analysis, etc.) without explicit task-based sampling or stratification. The 300K scale ensures sufficient coverage of long-tail tasks.","intents":["Obtain instruction data spanning multiple task domains without manual categorization","Ensure downstream models trained on this data inherit broad capability coverage","Discover what task distributions the source model considers valid and important","Create balanced representation across common and uncommon instruction types"],"best_for":["Building general-purpose instruction-tuned models with diverse capability requirements","Researchers studying what task distributions aligned models learn","Teams needing multi-domain instruction data without domain-specific annotation"],"limitations":["Task distribution reflects the source model's biases, not necessarily human task importance","No explicit control over task category balance — distribution is emergent from model sampling","May over-represent common tasks (writing, summarization) and under-represent specialized domains","Diversity is bounded by the source model's training data and alignment process"],"requires":["Aligned LLM with broad instruction-following capability across multiple domains","Sufficient generation scale (300K+) to capture long-tail task distribution"],"input_types":["model sampling parameters (temperature, top-p for diversity control)"],"output_types":["instruction dataset with implicit task distribution","optional: task category labels (if post-hoc classification applied)"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"magpie__cap_3","uri":"capability://data.processing.analysis.model.capability.reflection.in.training.data","name":"model-capability-reflection-in-training-data","description":"The dataset inherently captures and reflects the capabilities, limitations, and behavioral patterns of the source aligned model through the instruction-response pairs it generates. Because instructions are generated by the model itself and responses are completed by the same model, the resulting dataset encodes the model's own understanding of task feasibility, response quality standards, and instruction-following patterns. This creates a natural alignment between training data and model capabilities.","intents":["Train new models that inherit the capability profile of the source model","Distill knowledge and behavioral patterns from larger models into smaller ones","Create training data that is naturally aligned with a specific model's strengths","Understand what instruction-following patterns a model has learned"],"best_for":["Model distillation pipelines where capability transfer is the primary goal","Teams building smaller models that should mimic a larger model's behavior","Researchers studying how instruction-following patterns propagate through model training"],"limitations":["Trained models will inherit the source model's biases, failure modes, and blind spots","Cannot improve upon the source model's capabilities — ceiling is bounded by source model performance","If source model has systematic errors or misunderstandings, these propagate to training data","May reduce diversity of approaches if source model converges to narrow solution patterns"],"requires":["High-quality aligned source model with strong instruction-following capability","Acceptance that downstream models will reflect source model's behavioral patterns"],"input_types":["source model (API access or local inference)"],"output_types":["instruction-response pairs reflecting source model's capability profile","implicit behavioral patterns and quality standards"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"magpie__cap_4","uri":"capability://data.processing.analysis.seed.data.free.instruction.dataset.generation","name":"seed-data-free-instruction-dataset-generation","description":"Eliminates the requirement for human-authored seed instructions by using a pre-filled assistant template as the sole input to trigger instruction generation. The model generates instructions directly from its learned distribution without any human examples to guide it. This approach scales instruction dataset creation without the bottleneck of manual seed curation, though it requires a sufficiently capable aligned model to generate coherent instructions without examples.","intents":["Generate instruction datasets without human seed data annotation","Scale instruction dataset creation to arbitrary sizes without human effort","Avoid the bias introduced by human-selected seed instructions","Reduce time-to-dataset for rapid prototyping and iteration"],"best_for":["Organizations with limited annotation budgets or tight timelines","Researchers studying instruction distributions without human bias","Teams building instruction datasets for multiple languages or domains simultaneously"],"limitations":["Requires a pre-aligned model capable of generating coherent instructions without examples","Quality depends entirely on source model's instruction-generation capability","Cannot incorporate domain-specific knowledge or task requirements that humans would naturally include","May generate instructions that are valid but not representative of real user needs"],"requires":["Aligned LLM with strong instruction-generation capability (e.g., GPT-3.5, Claude, Llama-2-Chat)","Pre-filled assistant template (minimal human input)"],"input_types":["assistant template (text prompt structure, typically 1-2 sentences)"],"output_types":["instruction-response pairs without human seed data"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"magpie__cap_5","uri":"capability://data.processing.analysis.instruction.response.pair.generation.with.template.control","name":"instruction-response-pair-generation-with-template-control","description":"Generates instruction-response pairs through a controlled two-stage process: first, a pre-filled assistant template constrains the model to generate the user instruction in a specific format, then the model completes its response to that instruction. The template acts as a structural constraint that guides generation while allowing the model's learned distribution to determine content. This enables reproducible, format-controlled generation at scale.","intents":["Generate instruction-response pairs in a consistent, parseable format","Control the structure and style of generated instructions through template design","Ensure generated data is compatible with downstream training pipelines","Reproduce generation process with consistent formatting across large batches"],"best_for":["Teams needing structured instruction data for fine-tuning pipelines","Researchers studying how template structure affects instruction generation","Organizations with strict data format requirements"],"limitations":["Template design significantly impacts instruction diversity — overly constrained templates reduce variety","Template bias may favor certain instruction types or styles over others","Requires careful template engineering to balance structure with diversity","Generated instructions are constrained by template assumptions about valid instruction format"],"requires":["Well-designed pre-filled assistant template","Model capable of following template constraints while generating diverse content"],"input_types":["assistant template (text with placeholders or structural markers)"],"output_types":["structured instruction-response pairs (JSON, JSONL, or custom format)"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"magpie__cap_6","uri":"capability://data.processing.analysis.latent.instruction.distribution.harvesting","name":"latent-instruction-distribution-harvesting","description":"Extracts and materializes the latent instruction distribution that exists within aligned LLMs by prompting the model to generate instructions it would accept and respond to. The approach assumes that aligned models have learned an implicit distribution over valid tasks and instructions during training, and this distribution can be harvested by reversing the typical generation direction (instruction → response becomes response ← instruction). The 300K dataset represents a sample from this latent distribution.","intents":["Understand what instruction distributions aligned models have learned","Extract the implicit task understanding encoded in aligned models","Create training data that reflects a model's learned instruction space","Study how instruction-following capability is represented in model weights"],"best_for":["ML researchers studying instruction-following and alignment","Teams analyzing what task distributions models learn","Organizations interested in model interpretability through data generation"],"limitations":["The latent distribution is implicit and not directly observable — only accessible through generation","Distribution may not align with human intuitions about valid or important tasks","Sampling from the distribution (temperature, top-p) significantly affects what is extracted","No guarantee that the extracted distribution is complete or representative of all learned patterns"],"requires":["Aligned LLM with instruction-following capability","Theoretical acceptance that models encode implicit task distributions"],"input_types":["model and sampling parameters"],"output_types":["300K instruction-response pairs representing a sample from the latent distribution"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"magpie__cap_7","uri":"capability://data.processing.analysis.model.capability.reflection.in.training.data","name":"model-capability-reflection-in-training-data","description":"Ensures training data reflects the actual capabilities and knowledge of the source aligned model by extracting instructions the model implicitly understands. Unlike human-authored instruction datasets that may include tasks the model cannot perform, Magpie generates instructions grounded in the model's demonstrated capabilities. This creates a training dataset where every instruction-response pair represents a task the source model can actually handle, improving alignment between training data and model capabilities.","intents":["Create training data that reflects the actual capabilities of the source model","Avoid training on instructions for tasks the source model cannot perform","Ensure instruction-response pairs are grounded in demonstrated model capabilities"],"best_for":["Teams training models where instruction-capability alignment is critical","Researchers studying the relationship between training data and model capabilities","Organizations wanting to inherit the capabilities of their source aligned model"],"limitations":["Training data is limited to tasks the source model can perform — cannot extend beyond the source model's capabilities","Capability reflection may amplify biases or limitations in the source model","No explicit validation that generated instructions actually reflect model capabilities — relies on implicit learning","Difficult to audit or verify capability coverage without manual inspection"],"requires":["An aligned model with well-defined capabilities","Sufficient generation volume to capture the breadth of model capabilities"],"input_types":["Response templates (text)"],"output_types":["Instruction-response pairs reflecting model capabilities"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"magpie__headline","uri":"capability://model.training.instruction.dataset.for.training.aligned.language.models","name":"instruction dataset for training aligned language models","description":"A novel instruction dataset generated from aligned LLMs that provides high-quality instruction pairs for training other models, reflecting the model's own capabilities with 300K diverse examples.","intents":["best instruction dataset","instruction dataset for model training","high-quality instruction pairs for LLMs","aligned model training data","datasets for improving language model instructions"],"best_for":[],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["model-training","testing-quality"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":57,"verified":false,"data_access_risk":"low","permissions":["Access to an aligned LLM (e.g., GPT-3.5, Claude, Llama-2-Chat) with API or local inference capability","Computational resources for batch generation of 300K+ examples (GPU recommended for inference speed)","Filtering pipeline to remove low-quality or duplicate examples post-generation","Raw generated instruction dataset (pre-filtered version)","Deduplication and quality scoring infrastructure","Computational resources for batch processing and filtering","Aligned LLM with broad instruction-following capability across multiple domains","Sufficient generation scale (300K+) to capture long-tail task distribution","High-quality aligned source model with strong instruction-following capability","Acceptance that downstream models will reflect source model's behavioral patterns"],"failure_modes":["Quality ceiling bounded by the base model's own capabilities and biases — cannot generate instructions for tasks the source model cannot perform","Potential for distribution drift if the base model's instruction understanding diverges from human expectations","Requires a pre-aligned model as input; cannot be applied to base models without instruction-following capability","Generated instructions may exhibit similar failure modes or blind spots as the source model","Filtering heuristics may remove valid but unusual instructions, reducing long-tail task coverage","Quality thresholds are dataset-specific and may not generalize across domains","No transparency into exact filtering criteria used in the published 300K subset","Potential for filtering to introduce systematic biases if thresholds favor certain task types","Task distribution reflects the source model's biases, not necessarily human task importance","No explicit control over task category balance — distribution is emergent from model sampling","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":0.3,"quality":0.25,"ecosystem":0.1,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:23.328Z","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=magpie","compare_url":"https://unfragile.ai/compare?artifact=magpie"}},"signature":"KWS+NqbNfP+sAlXdAMRYtv3FvpzooBLmpsNuOaAkXmNIJUifm3eodyVFkKfONeRLLOakX3pdkTA0BgmepD1aAQ==","signedAt":"2026-06-21T01:10:47.961Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/magpie","artifact":"https://unfragile.ai/magpie","verify":"https://unfragile.ai/api/v1/verify?slug=magpie","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"}}