| --- |
| license: apache-2.0 |
| language: |
| - en |
| task_categories: |
| - text-generation |
| - conversational |
| - question-answering |
| tags: |
| - flatbuild |
| - conversational |
| - chatbot |
| - synthetic |
| - instruction |
| - dialogue |
| - training |
| - llm |
| - pretraining |
| size_categories: |
| - 10K<n<100K |
| --- |
| |
| # FlatBuild Demo Chat 10K Dataset |
|
|
| The **FlatBuild Demo Chat 10K Dataset** is the official conversational training dataset for **FlatBuild** and is used to train **Flatbot-Mini-35M**, the flagship demonstration language model of the **Flatseek** ecosystem. |
|
|
| The dataset showcases the complete workflow of building a conversational language model entirely from scratch, including: |
|
|
| - dataset preparation |
| - tokenizer training |
| - chat data preprocessing |
| - Transformer training |
| - checkpoint export |
| - GGUF conversion |
| - efficient inference with FlatRun |
|
|
| Designed for reproducibility and fast experimentation, the dataset enables developers to train compact language models on consumer hardware while demonstrating the complete FlatBuild pipeline. |
|
|
| --- |
|
|
| # Overview |
|
|
| The dataset contains approximately **10,000** synthetic conversational examples covering a broad range of general-purpose assistant interactions. |
|
|
| Rather than focusing solely on instruction-following, the conversations emphasize natural dialogue, contextual understanding, and realistic multi-turn interactions. |
|
|
| The resulting models learn conversational capabilities such as: |
|
|
| - greetings |
| - introductions |
| - question answering |
| - explanations |
| - recommendations |
| - comparisons |
| - coding assistance |
| - troubleshooting |
| - brainstorming |
| - summarization |
| - simple reasoning |
| - follow-up conversations |
| - polite refusals |
| - general knowledge |
|
|
| --- |
|
|
| # Dataset Format |
|
|
| Each line is stored as a JSON object representing a single conversation. |
|
|
| Example: |
|
|
| ```json |
| { |
| "messages": [ |
| { |
| "role": "system", |
| "content": "<system instruction>" |
| }, |
| { |
| "role": "user", |
| "content": "<user message>" |
| }, |
| { |
| "role": "assistant", |
| "content": "<assistant response>" |
| } |
| ] |
| } |
| ``` |
|
|
| Each conversation consists of an ordered sequence of chat messages using the standard `system`, `user`, and `assistant` roles. Conversation lengths range from single-turn exchanges to longer multi-turn dialogues. |
|
|
| --- |
|
|
| # Characteristics |
|
|
| | Property | Value | |
| |----------|------:| |
| | Conversations | ~10,000 | |
| | Format | JSONL | |
| | Structure | Multi-turn chat | |
| | Language | English | |
| | Domain | General-purpose assistant | |
| | Data Type | Synthetic | |
| | Training Split | 95% | |
| | Validation Split | 5% | |
| | Recommended Context Length | 512 tokens | |
|
|
| --- |
|
|
| # Topics |
|
|
| The dataset includes conversations across a diverse set of everyday subjects, including: |
|
|
| - greetings |
| - introductions |
| - daily life |
| - education |
| - productivity |
| - mathematics |
| - programming |
| - Python |
| - JavaScript |
| - technology |
| - artificial intelligence |
| - science |
| - history |
| - geography |
| - travel |
| - food |
| - weather |
| - books |
| - movies |
| - health |
| - finance |
| - recommendations |
| - comparisons |
| - troubleshooting |
| - brainstorming |
| - summarization |
| - logical reasoning |
| - general knowledge |
|
|
| Conversation lengths intentionally vary to encourage both short-response generation and longer context retention. |
|
|
| --- |
|
|
| # Intended Use |
|
|
| This dataset is intended for: |
|
|
| - training conversational language models from scratch |
| - tokenizer training |
| - compact LLM research |
| - Transformer architecture experiments |
| - educational purposes |
| - reproducible language model training |
| - FlatBuild demonstrations |
|
|
| It is particularly suitable for developers interested in understanding how modern decoder-only language models can be trained without relying on pretrained foundation models. |
|
|
| --- |
|
|
| # Not Intended For |
|
|
| This dataset is **not** intended for: |
|
|
| - production chatbots |
| - factual knowledge benchmarks |
| - safety evaluations |
| - alignment research |
| - instruction tuning of large pretrained models |
| - replacing large public conversational datasets |
|
|
| --- |
|
|
| # Training |
|
|
| The dataset is used by the official FlatBuild training configuration for **Flatbot-Mini-35M**. |
|
|
| ```bash |
| flatbuild train configs/flatbot-mini-35M.yaml |
| ``` |
|
|
| After training, checkpoints can be exported to SafeTensors or GGUF for inference with FlatRun or other GGUF-compatible runtimes. |
|
|
| --- |
|
|
| # Design Goals |
|
|
| This dataset was created with the following objectives: |
|
|
| - Natural conversational flow |
| - Diverse wording |
| - Broad topic coverage |
| - Strong multi-turn context retention |
| - Minimal template repetition |
| - High-quality synthetic dialogue |
| - Fast training on consumer hardware |
| - Fully reproducible end-to-end workflow |
|
|
| Rather than maximizing dataset size, the focus is on producing a clean, high-quality conversational corpus that enables compact language models to learn practical dialogue behaviors efficiently. |
|
|
| --- |
|
|
| # Models Trained Using This Dataset |
|
|
| This dataset is used by the official FlatBuild demonstration models, including: |
|
|
| - **Flatbot-Micro-4M** |
| - **Flatbot-Mini-35M** |
|
|
| Both models are trained entirely from random initialization using FlatBuild without relying on pretrained language models. |
|
|
| --- |
|
|
| # License |
|
|
| Apache-2.0 |
