Datasets:

Nebulixlabs
/

FineTuning

license: mit
task_categories:
  - question-answering
  - text-generation
language:
  - en
tags:
  - small-language-models
  - slm
  - fine-tuning
  - short-qa
  - low-resource
  - hallucination-reduction
pretty_name: Short QA Fine-Tuning Dataset for Small Language Models
size_categories:
  - 10K<n<100K
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
dataset_info:
  features:
    - name: prompt
      dtype: string
    - name: answer
      dtype: string
  splits:
    - name: train
      num_bytes: 572797
      num_examples: 5020
  download_size: 310328
  dataset_size: 572797

This is v2 Of FineTuning dataset so this dataset is update version of old one and this dataset is amazing and factual dataset

Dataset Card for Nebulixlabs/FineTuning

Dataset Summary

Nebulixlabs/FineTuning is a short question-answer (QA) pair dataset purpose-built for fine-tuning very small language models (SLMs) — roughly in the 1M to 150M parameter range. Each example consists of a short question (typically 5–6 words) and a short answer (typically 3–4 words).

The dataset was created around an empirical observation: very small base models (1M–50M params) can only reliably predict short sequences of about 12–15 words before accuracy degrades and hallucination increases, regardless of how large their context window is. Training such models on long-form QA pairs pushes them past this reliable generation length, causing hallucinated or incoherent answers.

By constraining both inputs and outputs to short, low-complexity spans, this dataset lets small models learn to answer questions within their actual reliable generation capacity, significantly reducing hallucination compared to training on longer QA pairs.

Supported Tasks

Question Answering (short-form): Train/fine-tune a model to map a short question to a short, factual or direct answer.
Hallucination mitigation research: Useful for studying how output length and training data length affect hallucination rates in small language models.

Intended Models

This dataset is primarily intended for:

Small language models (SLMs) in the 1M–50M parameter range (primary use case).
Mid-size models up to ~150M parameters, since empirical experiments showed models around this size are also prone to high hallucination rates and benefit from short-pair fine-tuning.

It is not recommended for larger/mature models (e.g., 1B+ parameters), which can already handle longer context and generation reliably and may be under-utilized by such short pairs.

Languages

English (en).

Dataset Structure

Data Instances

Each instance is a short question-answer pair. Example:

{
  "prompt": "What is the capital of France?",
  "answer": "Paris."
}

Data Fields

Field	Type	Description
`prompt`	string	A short question, typically 5–6 words long.
`answer`	string	A short, direct answer, typically 3–4 words long.

Data Splits

Split	# Examples
train	5k

Dataset Creation

Rationale

Experiments showed that very small base models (1M–50M parameters) reliably generate correct output only up to 12–15 words, independent of context length. Fine-tuning such models on longer QA pairs causes hallucination even within a large context window. This dataset keeps both question and answer short so the generation task stays within the model's reliable output range, which:

Reduces hallucination during inference.
Makes training signal cleaner and easier for tiny models to fit.
Allows models as large as ~150M parameters (which also showed elevated hallucination rates in testing) to benefit from the same short-pair format.

Curation Rationale / Source Data

This dataset is synthatic generated by Sarwam 105B Conversation model

Annotations

This dataset is tested and verified

Considerations for Using the Data

Limitations

Short QA format is not suitable for tasks requiring long-form reasoning, multi-turn dialogue, or detailed explanations.
Intended specifically for small-parameter models; larger models may not show meaningful benefit.
Answer brevity may limit factual nuance (e.g., no room for caveats or partial answers).

Additional Information

Licensing

Specify the license under which this dataset is released (e.g., MIT, CC-BY-4.0, Apache-2.0).

Citation

@dataset{nebulixlabs_finetuning,
  title = {Nebulixlabs/FineTuning: Short QA Pairs for Small Language Model Fine-Tuning},
  author = {Nebulixlabs},
  year = {2026},
  url = {https://huggingface.co/datasets/Nebulixlabs/FineTuning}
}

Contact

For questions, issues, or contributions, open a discussion on the dataset's Hugging Face repository page.