| --- |
| license: apache-2.0 |
| language: |
| - en |
| tags: |
| - qa |
| - questionanswer |
| - response |
| - chat |
| - llm |
| pretty_name: MyQA |
| --- |
| |
| # MagnumAI/MyQA |
|
|
| A compact conversational **prompt → response** dataset for experimenting with instruction following, supervised fine-tuning (SFT), and general-purpose conversational behavior. |
|
|
| The dataset contains natural-language prompts ranging from simple greetings and factual questions to everyday advice, explanations, how-to questions, and casual conversation. |
|
|
| ## Dataset Overview |
|
|
| **Dataset:** `MagnumAI/MyQA` |
| **Format:** JSONL |
| **Fields:** `prompt`, `response` |
| **Approximate size:** 550 KB |
| **Task:** Conversational question answering / instruction following |
|
|
| Each record consists of a user-style prompt paired with an assistant response: |
|
|
| ```json |
| {"prompt": "hey", "response": "Hey! 😊 What's up?"} |
| ``` |
|
|
| ## What's Inside? |
|
|
| The dataset covers a range of everyday conversational patterns, including: |
|
|
| * Greetings and small talk |
| * General questions |
| * Geography and general knowledge |
| * Cooking and food |
| * Practical how-to questions |
| * Hobbies and learning |
| * Games and skill development |
| * Animal behavior |
| * Language and grammar |
| * Productivity and habits |
| * Entertainment recommendations |
| * Philosophical questions |
| * Casual conversation |
|
|
| Responses generally aim to be: |
|
|
| * Helpful and conversational |
| * Direct without being overly terse |
| * Structured when an explanation benefits from it |
| * Friendly and approachable |
| * Honest about uncertainty |
| * Actionable for practical questions |
|
|
| Longer responses commonly use Markdown headings, bullet points, numbered steps, and follow-up suggestions. |
|
|
| ## Format |
|
|
| The dataset is stored as **JSON Lines (JSONL)**, with one example per line. |
|
|
| ### Schema |
|
|
| | Field | Type | Description | |
| | ---------- | -------- | --------------------------------------------- | |
| | `prompt` | `string` | The user's question or conversational message | |
| | `response` | `string` | The corresponding assistant response | |
|
|
| Example: |
|
|
| ```json |
| {"prompt": "what's the capital of Australia?", "response": "Canberra! 🇦🇺 Not Sydney or Melbourne — those are the two people usually guess."} |
| ``` |
|
|
| Longer responses may contain escaped newlines: |
|
|
| ```json |
| { |
| "prompt": "how do I boil an egg?", |
| "response": "Easy one — here's the reliable method.\n## The Steps\n1. Place eggs in a pot and cover with cold water..." |
| } |
| ``` |
|
|
| ## Intended Uses |
|
|
| `MagnumAI/MyQA` can be used for: |
|
|
| * Supervised fine-tuning experiments |
| * Instruction-following experiments |
| * Conversational model prototyping |
| * Testing prompt/response formatting |
| * Small-scale chatbot experiments |
| * Studying conversational response styles |
| * Dataset preprocessing and JSONL pipeline testing |
|
|
| A typical training example can be converted into the format expected by your model or training framework: |
|
|
| ```python |
| { |
| "messages": [ |
| {"role": "user", "content": example["prompt"]}, |
| {"role": "assistant", "content": example["response"]} |
| ] |
| } |
| ``` |
|
|
| ## Limitations |
|
|
| This is a relatively small dataset and **should not be treated as a comprehensive knowledge or safety benchmark**. |
|
|
| In particular: |
|
|
| * The dataset is not designed to provide exhaustive factual coverage. |
| * Some responses contain recommendations or subjective advice. |
| * General-knowledge answers may become outdated over time. |
| * Practical advice may require additional context. |
| * The dataset does not provide a comprehensive evaluation of model safety. |
| * The examples primarily represent everyday conversational interactions rather than adversarial or safety-critical prompts. |
| * A model trained on this dataset may learn its particular tone, formatting habits, and response patterns. |
|
|
| Models fine-tuned on this dataset should be evaluated independently rather than assuming dataset quality guarantees model quality. |
|
|
| ## Example |
|
|
| **Prompt** |
|
|
| > how do I stop procrastinating? |
|
|
| **Response** |
|
|
| > Procrastination is usually less about laziness and more about avoiding discomfort — boredom, anxiety, ambiguity, whatever the task stirs up. So the fix isn't willpower, it's lowering the friction. |
| > |
| > ## Start Smaller Than Feels Reasonable |
| > |
| > "Write the report" is too big. "Open the doc and write one sentence" is doable. The goal is to get past the starting point, because momentum often handles the rest. |
| > |
| > ... |
|
|
| This illustrates the dataset's general preference for **conversational explanations combined with structured, practical advice**. |
|
|
| ## Fine-Tuning |
|
|
| For a chat-oriented model, examples can be represented as: |
|
|
| ```json |
| { |
| "messages": [ |
| { |
| "role": "user", |
| "content": "what's the capital of Canada?" |
| }, |
| { |
| "role": "assistant", |
| "content": "Ottawa! 🇨🇦 Toronto and Vancouver get guessed more often, but Ottawa's the one." |
| } |
| ] |
| } |
| ``` |
|
|
| The exact formatting should be adapted to the chat template expected by the base model. |
|
|
| ## Dataset Characteristics |
|
|
| | Characteristic | Description | |
| | ---------------- | ------------------------------------ | |
| | Data type | Conversational QA | |
| | Input | Natural-language prompts | |
| | Output | Assistant responses | |
| | Storage | JSONL | |
| | Size | ~300 KB | |
| | Style | Friendly, conversational, structured | |
| | Primary language | English | |
| | Intended setting | General-purpose conversational AI | |
|
|
| ## License |
|
|
| See the repository's Hugging Face dataset metadata for the applicable license. |
|
|
| If you are redistributing, modifying, or using the dataset commercially, verify that the license and provenance of the underlying examples permit your intended use. |
|
|
| ## Contributions |
|
|
| Suggestions, corrections, additional examples, and improvements to the dataset are welcome. |
|
|
| When contributing examples, prefer: |
|
|
| * Clear prompts |
| * Useful, self-contained responses |
| * Accurate information |
| * Natural conversational language |
| * Consistent formatting |
| * No unnecessary personal or sensitive information |
|
|
| ## Acknowledgements |
|
|
| Created and maintained by **MagnumAI**. |
|
|
