| --- |
| 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 |
| --- |
| > [!WARNING] |
| > **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: |
|
|
| ```json |
| { |
| "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 |
|
|
| ```bibtex |
| @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. |
