license: apache-2.0
language:
- en
tags:
- reasoning
- instruction-tuning
- chatml
- math
- coding
- general
- orbit
pretty_name: Orbit-200K
size_categories:
- 100K<n<1M
task_categories:
- text-generation
configs:
- config_name: default
data_files:
- split: train
path: data/train-00000-of-00001.parquet
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_examples: 200000
download_size: 168000000
dataset_size: 200000000
🪐 Orbit-200K
A high-signal instruction dataset designed for training universal language models with dense reasoning, coding, mathematics, and general intelligence—without conversational bloat.
Orbit-200K is a carefully curated dataset of 200,000 instruction-response pairs optimized for training modern language models ranging from 0.5B to 7B parameters.
Unlike many public instruction datasets, Orbit-200K removes unnecessary conversational filler and focuses on maximizing useful learning signal per token, making it especially suitable for efficient fine-tuning of compact models.
Overview
| Property | Value |
|---|---|
| Examples | 200,000 |
| Language | English |
| Format | ChatML |
| License | Apache-2.0 |
| File Format | Parquet |
| Column | text |
| Training Target | Instruction Fine-tuning |
| Recommended Models | Qwen, Llama 3, Gemma, Mistral, Phi |
Why Orbit-200K?
Most instruction datasets contain significant amounts of unnecessary dialogue:
- "Certainly!"
- "Let's break this down."
- "I'd be happy to help."
- "I hope this helps!"
While these phrases improve user experience, they contribute very little to model capability and consume valuable context length.
Orbit-200K removes this conversational overhead so the model learns to produce:
- direct answers
- concise reasoning
- efficient coding solutions
- dense mathematical explanations
This results in higher information density during training.
Key Features
🚀 Zero Conversational Bloat
Responses begin immediately with useful content rather than assistant preambles.
Instead of:
Certainly! Let's solve this step by step...
the model learns outputs like:
Compute the derivative using the product rule...
🧠 Balanced Instruction Mixture
Orbit-200K combines three complementary domains:
| Dataset Source | Percentage | Purpose |
|---|---|---|
| MetaMathQA | 37.5% | Mathematical reasoning and logical thinking |
| OSS-Instruct (Exec Verified) | 25% | Coding, algorithms, software engineering |
| OpenHermes-2.5 (Filtered) | 37.5% | General knowledge, instruction following, QA |
This mixture provides strong general-purpose instruction tuning while maintaining excellent reasoning capability.
💬 Native ChatML Formatting
Every sample is already formatted using ChatML.
Compatible with:
- Qwen
- Llama 3
- Gemma
- Mistral
- most ChatML-based instruction models
No additional preprocessing is required.
Dataset Structure
The dataset contains a single column.
| Column | Type | Description |
|---|---|---|
| text | string | Fully formatted ChatML conversation |
Example:
<|im_start|>system
You are a helpful, logical, and precise AI assistant.
<|im_end|>
<|im_start|>user
Write a Python function to check if a string is a palindrome.
<|im_end|>
<|im_start|>assistant
def is_palindrome(s):
return s == s[::-1]
<|im_end|>
Loading the Dataset
from datasets import load_dataset
dataset = load_dataset(
"Siddh07ETH/Orbit-200K",
split="train"
)
Example:
print(dataset[0]["text"])
Data Processing Pipeline
Orbit-200K underwent multiple quality-control stages.
1. Conversational De-bloating
Removed unnecessary assistant phrases such as:
- Certainly!
- Of course!
- I'd be happy to help!
- Let's solve this together.
- I hope this helps!
2. Length Filtering
Very short assistant responses were removed.
Minimum assistant response length:
50 characters
This encourages denser learning signals.
3. Exact Duplicate Removal
Duplicate instruction-response pairs were removed to maximize unique information.
4. Formatting
All samples were converted into standardized ChatML format.
Recommended Use Cases
Orbit-200K is suitable for:
- Instruction tuning
- Supervised Fine-Tuning (SFT)
- Small language models (0.5B–7B)
- Reasoning models
- Coding assistants
- Mathematical reasoning
- General-purpose chat models
Recommended Training Models
Orbit-200K works particularly well with:
- Qwen 2.5
- Qwen 3
- Llama 3
- Gemma 2
- Mistral
- Phi
Training Objective
The dataset is designed to improve:
- Instruction following
- Logical reasoning
- Mathematical problem solving
- Programming capability
- Response density
- Reduced verbosity
Statistics
| Metric | Value |
|---|---|
| Total Examples | 200,000 |
| Number of Columns | 1 |
| Primary Column | text |
| Format | ChatML |
| File Format | Parquet |
| License | Apache-2.0 |
Citation
If you use Orbit-200K in your work, please cite:
@dataset{orbit200k2026,
title={Orbit-200K: A High-Signal Instruction Dataset for Universal LLM Reasoning},
author={Siddharth},
year={2026},
publisher={Hugging Face}
}
License
This dataset is released under the Apache License 2.0.
See the LICENSE file for details.
Acknowledgements
Orbit-200K builds upon publicly available datasets, including:
- MetaMathQA
- OSS-Instruct (Exec Verified)
- OpenHermes-2.5
Credit belongs to the original dataset creators whose work made this curated mixture possible.
Author
Siddharth
Hugging Face: https://huggingface.co/Siddh07ETH