🪐 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

---

⭐ If Orbit-200K helps your research or model training, consider giving the dataset a Like on Hugging Face.