Datasets:

flatseek
/

flatbot-mini-35M-dataset

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:

{
  "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.

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