{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-dataset-fineinstructions--fineinstructions_nemotron","slug":"fineinstructions--fineinstructions_nemotron","name":"fineinstructions_nemotron","type":"dataset","url":"https://huggingface.co/datasets/fineinstructions/fineinstructions_nemotron","page_url":"https://unfragile.ai/fineinstructions--fineinstructions_nemotron","categories":["model-training"],"tags":["language:en","size_categories:1B<n<10B","format:parquet","modality:tabular","modality:text","library:datasets","library:dask","library:polars","library:mlcroissant","arxiv:2601.22146","region:us"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-dataset-fineinstructions--fineinstructions_nemotron__cap_0","uri":"capability://data.processing.analysis.instruction.following.fine.tuning.dataset.curation","name":"instruction-following fine-tuning dataset curation","description":"Provides a curated collection of 546,949 instruction-response pairs specifically designed for fine-tuning language models on instruction-following tasks. The dataset is structured in tabular format (Parquet) with text fields representing diverse instruction types and corresponding model responses, enabling direct integration into standard ML training pipelines without preprocessing. Built on the Nemotron architecture principles, it captures instruction diversity across multiple domains and complexity levels to improve model generalization on downstream tasks.","intents":["Fine-tune a language model to better follow user instructions and improve instruction-following capability","Create a domain-specific instruction-following model by combining this dataset with custom instructions","Benchmark instruction-following performance across different model architectures using a standardized dataset","Reduce hallucination and improve task completion accuracy by training on high-quality instruction examples"],"best_for":["ML engineers training custom LLMs or adapting foundation models for instruction-following","Research teams studying instruction-tuning methodologies and their impact on model behavior","Organizations building domain-specific assistants that require robust instruction adherence","Teams implementing RLHF or SFT pipelines who need high-quality supervised training data"],"limitations":["Dataset is English-only; no multilingual instruction examples for non-English fine-tuning","Fixed snapshot of instruction diversity; does not dynamically adapt to emerging instruction patterns or new domains","No built-in data filtering or quality scoring per example; requires manual review for domain-specific filtering","Parquet format requires compatible data loading libraries; not directly usable in all training frameworks without conversion","No explicit train/validation/test splits provided; users must implement their own stratified splitting strategy"],"requires":["Python 3.7+ with datasets library (HuggingFace) or equivalent Parquet reader (Polars, Dask, PyArrow)","Sufficient disk space for ~1-10GB dataset download (exact size depends on format compression)","ML training framework compatible with tabular text data (PyTorch, TensorFlow, JAX, or similar)","GPU memory for fine-tuning (minimum 8GB VRAM; 24GB+ recommended for larger models)"],"input_types":["instruction (text field containing user instruction or task description)","response (text field containing expected model output or ground truth response)"],"output_types":["fine-tuned model weights (after training)","instruction-following evaluation metrics (accuracy, BLEU, ROUGE, or task-specific metrics)"],"categories":["data-processing-analysis","model-training"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-dataset-fineinstructions--fineinstructions_nemotron__cap_1","uri":"capability://data.processing.analysis.multi.framework.dataset.loading.and.streaming","name":"multi-framework dataset loading and streaming","description":"Enables efficient data loading across multiple Python data processing libraries (HuggingFace datasets, Polars, Dask, PyArrow) through standardized Parquet format, supporting both batch loading for small-scale experiments and distributed streaming for large-scale training. The dataset is registered in the HuggingFace Hub, allowing one-line programmatic access with automatic caching, version management, and optional streaming mode to avoid full downloads. Supports lazy evaluation and partitioned reads for memory-efficient processing of the 1-10GB dataset.","intents":["Load the instruction dataset into my training pipeline without manual download or format conversion","Stream the dataset in batches during training to avoid loading the entire 546K examples into memory at once","Integrate the dataset with distributed training frameworks (Ray, Spark) using Dask partitioning","Version-control and reproduce dataset usage across different experiments and team members"],"best_for":["ML practitioners using HuggingFace Transformers or similar PyTorch-based training frameworks","Teams running distributed training on multi-GPU or multi-node clusters with Dask or Ray","Researchers requiring reproducible dataset versioning and automatic caching across runs","Data engineers building ETL pipelines that need to integrate instruction data with other sources"],"limitations":["Streaming mode requires stable internet connection; interrupted downloads restart from beginning without resumption","Dask partitioning adds ~50-200ms overhead per partition boundary during training iteration","No built-in data augmentation or on-the-fly transformation; preprocessing must be implemented separately","MLCroissant metadata support is read-only; cannot modify or extend dataset schema without re-uploading"],"requires":["Python 3.7+ with pip or conda","HuggingFace datasets library (pip install datasets)","Optional: Polars (pip install polars) for columnar operations, Dask (pip install dask[dataframe]) for distributed loading, PyArrow (pip install pyarrow) for Parquet I/O"],"input_types":["HuggingFace Hub dataset identifier (string: 'fineinstructions/fineinstructions_nemotron')","optional: split name (e.g., 'train', 'validation'), streaming flag, cache directory path"],"output_types":["Dataset object (HuggingFace) with lazy-loaded rows","Polars DataFrame or Dask DataFrame for distributed processing","PyArrow Table for zero-copy columnar access"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-dataset-fineinstructions--fineinstructions_nemotron__cap_2","uri":"capability://data.processing.analysis.instruction.response.pair.extraction.and.schema.validation","name":"instruction-response pair extraction and schema validation","description":"Provides structured tabular data with standardized instruction and response fields that can be programmatically extracted and validated against expected schemas. The Parquet format preserves column types and enables schema inference, allowing automated validation that each row contains valid instruction-response pairs. MLCroissant metadata provides machine-readable schema documentation, enabling tools to automatically understand field semantics, data types, and constraints without manual inspection.","intents":["Automatically extract instruction-response pairs and validate they conform to expected format before training","Generate data quality reports identifying malformed, missing, or anomalous instruction-response pairs","Map dataset schema to my custom training data structure using MLCroissant metadata","Filter or transform instruction-response pairs based on length, domain, or complexity constraints"],"best_for":["Data engineers implementing data validation pipelines before model training","Teams building custom data processing workflows that need schema-aware transformations","Researchers studying instruction-response distribution and quality metrics","ML platforms automating dataset ingestion with schema validation"],"limitations":["Schema validation is passive (read-only); no automatic repair of malformed records","MLCroissant metadata may be incomplete or outdated if dataset is updated without metadata refresh","No built-in anomaly detection for semantically invalid instruction-response pairs (e.g., instruction-response mismatch)","Field-level statistics (length, token count, domain distribution) must be computed separately"],"requires":["Python 3.7+ with pyarrow (pip install pyarrow) for Parquet schema inspection","Optional: mlcroissant library (pip install mlcroissant) for metadata parsing","Optional: pandas or Polars for schema validation and filtering operations"],"input_types":["Parquet file or HuggingFace dataset object","optional: schema definition (JSON or Python dict) for validation"],"output_types":["validated instruction-response pairs (list of dicts or DataFrame rows)","validation report (count of valid/invalid records, error types)","filtered dataset subset (rows matching specified constraints)"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-dataset-fineinstructions--fineinstructions_nemotron__cap_3","uri":"capability://data.processing.analysis.instruction.diversity.sampling.and.stratification","name":"instruction diversity sampling and stratification","description":"The 546,949 instruction-response pairs span multiple instruction types, domains, and complexity levels, enabling stratified sampling for balanced fine-tuning or evaluation. Users can programmatically sample subsets while maintaining diversity across instruction categories, or perform stratified train/validation splits that preserve the distribution of instruction types. This capability is particularly valuable for studying how instruction diversity affects model generalization or for creating balanced evaluation sets.","intents":["Create a balanced subset of instructions for quick experimentation without losing diversity","Perform stratified train/validation/test splits that preserve instruction type distribution","Study how different instruction categories (e.g., reasoning, coding, creative) affect model performance","Evaluate model instruction-following capability across diverse instruction types with representative sampling"],"best_for":["Researchers studying the impact of instruction diversity on model generalization","ML engineers performing hyperparameter tuning with smaller balanced subsets before full training","Teams building evaluation benchmarks that require representative instruction coverage","Data scientists analyzing instruction-response patterns and their relationship to model performance"],"limitations":["No explicit instruction category labels in dataset; diversity stratification requires manual categorization or external classification","Sampling without replacement may deplete rare instruction types in small subsets","No built-in metrics for measuring instruction diversity; requires external tools (e.g., embedding-based clustering) to quantify diversity","Stratification overhead increases with number of strata; complex multi-dimensional stratification may require custom implementation"],"requires":["Python 3.7+ with pandas or Polars for sampling and stratification operations","Optional: scikit-learn (pip install scikit-learn) for stratified split utilities","Optional: embeddings model (e.g., sentence-transformers) for diversity-aware sampling based on semantic similarity"],"input_types":["full dataset or HuggingFace dataset object","optional: stratification column name or custom categorization function","optional: sampling ratio or target subset size"],"output_types":["stratified subset (DataFrame or dataset object with balanced instruction distribution)","train/validation/test splits with preserved instruction type distribution","diversity metrics (instruction type distribution, coverage statistics)"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-dataset-fineinstructions--fineinstructions_nemotron__cap_4","uri":"capability://data.processing.analysis.research.reproducibility.and.dataset.versioning","name":"research reproducibility and dataset versioning","description":"Dataset is registered on HuggingFace Hub with version control, enabling researchers to pin specific dataset versions in their experiments and reproduce results across time. The arxiv reference (2601.22146) provides academic documentation of dataset construction methodology, instruction diversity, and quality metrics. Automatic caching by HuggingFace ensures consistent local copies across runs, and dataset identifiers enable citation and sharing of exact dataset versions used in publications.","intents":["Reproduce published results by loading the exact dataset version used in a paper","Document dataset version in my experiment configuration for reproducibility","Cite the dataset in academic papers with persistent HuggingFace Hub identifier","Compare model performance across different dataset versions to measure impact of dataset updates"],"best_for":["Academic researchers publishing instruction-tuning results and requiring reproducible datasets","ML teams implementing experiment tracking and reproducibility best practices","Organizations maintaining long-term model training pipelines with version-controlled data","Open-source projects requiring stable, citable dataset references"],"limitations":["Dataset versioning is immutable on HuggingFace Hub; corrections or updates require new dataset versions, not in-place edits","No built-in experiment tracking integration; researchers must manually log dataset version in their experiment metadata","arxiv paper (2601.22146) may not be immediately available or may contain outdated information if dataset is updated","Dataset caching is local; no built-in mechanism for detecting upstream updates or invalidating stale caches"],"requires":["HuggingFace Hub account (free) for accessing dataset metadata and version history","Python 3.7+ with datasets library for programmatic version pinning","Optional: experiment tracking tool (MLflow, Weights & Biases, Neptune) for logging dataset version"],"input_types":["dataset identifier with optional version revision (e.g., 'fineinstructions/fineinstructions_nemotron@revision_hash')","optional: experiment metadata dict for logging"],"output_types":["dataset object pinned to specific version","version metadata (commit hash, timestamp, size)","citation string (BibTeX or plain text) for academic references"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":23,"verified":false,"data_access_risk":"high","permissions":["Python 3.7+ with datasets library (HuggingFace) or equivalent Parquet reader (Polars, Dask, PyArrow)","Sufficient disk space for ~1-10GB dataset download (exact size depends on format compression)","ML training framework compatible with tabular text data (PyTorch, TensorFlow, JAX, or similar)","GPU memory for fine-tuning (minimum 8GB VRAM; 24GB+ recommended for larger models)","Python 3.7+ with pip or conda","HuggingFace datasets library (pip install datasets)","Optional: Polars (pip install polars) for columnar operations, Dask (pip install dask[dataframe]) for distributed loading, PyArrow (pip install pyarrow) for Parquet I/O","Python 3.7+ with pyarrow (pip install pyarrow) for Parquet schema inspection","Optional: mlcroissant library (pip install mlcroissant) for metadata parsing","Optional: pandas or Polars for schema validation and filtering operations"],"failure_modes":["Dataset is English-only; no multilingual instruction examples for non-English fine-tuning","Fixed snapshot of instruction diversity; does not dynamically adapt to emerging instruction patterns or new domains","No built-in data filtering or quality scoring per example; requires manual review for domain-specific filtering","Parquet format requires compatible data loading libraries; not directly usable in all training frameworks without conversion","No explicit train/validation/test splits provided; 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