--- license: apache-2.0 language: - en tags: - reasoning - instruction-tuning - chatml - math - coding - general - orbit pretty_name: Orbit-200K size_categories: - 100K

> **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: ```text <|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 ```python from datasets import load_dataset dataset = load_dataset( "Siddh07ETH/Orbit-200K", split="train" ) ``` Example: ```python 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: ```bibtex @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.