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license: apache-2.0
task_categories:
- text-generation
tags:
- k2-horizon
- training-data
- parquet
- sft
- instruction-following
- reasoning
configs:
- config_name: instruction_following
data_files:
- split: train
path: instruction_following/*.parquet
- config_name: 3efforts-pretrain
data_files:
- split: train
path: 3efforts-pretrain/*.parquet
---
# SFT-Reasoning
## Dataset Description
Instruction-following and reasoning data prepared for supervised fine-tuning. This repository is part of the [K2 Horizon collection](https://huggingface.co/collections/IFM/k2-horizon).
The repository is organized into multiple subsets. Every subset has a `train` split backed by Parquet shards, which supports Dataset Viewer inspection and streaming access.
## K2 Horizon Dataset Series
| Dataset repository | Focus | Subsets |
| -------------------------------------------------------------------------------- | ---------------------------------------- | ------: |
| [IFM/TxT360-v2](https://huggingface.co/datasets/IFM/TxT360-v2) | Web and question-answering text | 3 |
| [IFM/Code-Reasoning](https://huggingface.co/datasets/IFM/Code-Reasoning) | Code reasoning and task synthesis | 7 |
| [IFM/Math-Reasoning](https://huggingface.co/datasets/IFM/Math-Reasoning) | Mathematical reasoning and dialogue | 5 |
| [IFM/SFT-Reasoning](https://huggingface.co/datasets/IFM/SFT-Reasoning) | Instruction following and SFT-style data | 2 |
| [IFM/Pretrain-Behaviors](https://huggingface.co/datasets/IFM/Pretrain-Behaviors) | Behavior-focused pretraining data | 7 |
## Dataset Subsets
| Subset | Data files |
| ----------------------- | --------------------------------- |
| `instruction-following` | `instruction-following/*.parquet` |
| `3efforts-pretrain` | `3efforts-pretrain/*.parquet` |
## Repository Structure
```text
README.md
instruction-following/
<source-file>-<stable-id>-00000.parquet
<source-file>-<stable-id>-00001.parquet
3efforts-pretrain/
<source-file>-<stable-id>-00000.parquet
<source-file>-<stable-id>-00001.parquet
```
The shard prefix is derived from the source JSONL filename and a stable identifier. Updating one source JSONL file replaces only that file's Parquet shards.
## Data Fields
Records originate as JSON objects and are converted to Parquet for release. Field names and nested structures can differ by subset. Inspect `features` before building a processing pipeline:
```python
from datasets import load_dataset
dataset = load_dataset(
"IFM/SFT-Reasoning",
"instruction-following",
split="train",
streaming=True,
)
print(dataset.features)
print(next(iter(dataset)))
```
## Data Provenance and Processing
Individual subsets may have undergone source-specific filtering, cleaning, deduplication, quality scoring, or synthetic-data generation. Users should evaluate each subset for their target use case and inspect the available provenance metadata.
## Intended Use
This dataset is intended for language-model training and research. The subsets can be streamed independently, combined with user-defined sampling weights, or inspected through the Hugging Face Dataset Viewer.
## Limitations and Responsible Use
Large-scale training data can contain factual errors, duplicated material, sensitive topics, stereotypes, unsafe content, and other artifacts. Dataset users are responsible for performing evaluations, risk assessment, and filtering appropriate to their application.
## License and Terms of Use
This dataset is licensed under the Apache License 2.0 available at https://www.apache.org/licenses/LICENSE-2.0. |