kothaset 1.1.0

High-quality dataset generation CLI for LLM training

KothaSet

KothaSet is a powerful CLI tool for generating high-quality datasets using Large Language Models (LLMs) as teacher models. Create diverse training data for fine-tuning smaller models.

Features

• Multi-Provider — OpenAI, and OpenAI-compatible APIs (DeepSeek, vLLM, Ollama)
• Flexible Schemas — Instruction (Alpaca), Chat (ShareGPT), Preference (DPO), Classification
• Streaming Output — Real-time generation with progress tracking
• Resumable — Atomic checkpointing, never lose progress
• Multiple Formats — JSONL, Native Parquet, HuggingFace datasets
• Reproducible — Required seed for deterministic LLM generation
• Diversity Control — Input files for sequential topic coverage
• Validation — Validate configs, schemas, datasets, and provider connectivity

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Installation

pip (Python)

pip install kothaset

npm (Node.js)

npm install -g kothaset

Homebrew (macOS/Linux)

brew install shantoislamdev/tap/kothaset

Binary Download

Download from GitHub Releases.

From Source

go install github.com/shantoislamdev/kothaset/cmd/kothaset@latest

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Quick Start

1. Initialize configuration:

   kothaset init
   

2. Set your API key:

   # Windows PowerShell
   $env:OPENAI_API_KEY = "sk-..."
   
   # Linux/macOS
   export OPENAI_API_KEY="sk-..."
   

3. Generate a dataset:

   kothaset generate -n 100 -s instruction --seed 42 -i topics.txt -o dataset.jsonl
   

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Configuration

KothaSet uses a two-file configuration system for better security and organization:

1. kothaset.yaml (Public)

Contains shared settings, context, and instructions. Safe to commit to git.

version: "1.0"
global:
  provider: openai
  schema: instruction
  model: gpt-5.2
  concurrency: 4
  output_dir: ./output

# Context: Background info or persona injected into every prompt
context: |
  Generate high-quality training data for an AI assistant.
  The data should be helpful, accurate, and well-formatted.

# Instructions: Specific rules and guidelines for generation
instructions:
  - Be creative and diverse in topics and approaches
  - Vary the style and complexity of responses
  - Use clear and concise language

2. .secrets.yaml (Private)

Contains sensitive provider credentials. Add this to your .gitignore!

providers:
  - name: openai
    type: openai
    api_key: env.OPENAI_API_KEY  # Reads from environment variable
    # api_key: sk-...            # Or hardcode key directly
    timeout: 1m
    rate_limit:
      requests_per_minute: 60

  # Custom endpoint example (DeepSeek, vLLM)
  - name: local
    type: openai
    base_url: http://localhost:8000/v1
    api_key: not-needed

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Usage

Selecting a Schema

Schema	Description	Use Case
instruction	Alpaca-style {instruction, input, output}	SFT
chat	ShareGPT multi-turn conversations	Chat fine-tuning
preference	{prompt, chosen, rejected} pairs	DPO/RLHF
classification	{text, label} pairs	Classifiers

# Instruction dataset
kothaset generate -n 1000 -s instruction --seed 42 -i topics.txt -o instructions.jsonl

# Chat conversations
kothaset generate -n 500 -s chat --seed 123 -i conversations.txt -o conversations.jsonl

# Preference pairs for DPO  
kothaset generate -n 500 -s preference --seed 456 -i pairs.txt -o dpo_data.jsonl

Output Formats

# JSONL (default)
kothaset generate -n 100 --seed 42 -i topics.txt -f jsonl -o dataset.jsonl

# Parquet
kothaset generate -n 100 --seed 42 -i topics.txt -f parquet -o dataset.parquet

# HuggingFace datasets format
kothaset generate -n 100 --seed 42 -i topics.txt -f hf -o ./my_dataset

Advanced Options

# Use custom provider
kothaset generate -n 100 --seed 42 -i topics.txt -p local -o dataset.jsonl

# Control diversity with input file
kothaset generate -n 1000 --seed 42 -i topics.txt -o diverse.jsonl

# Resume interrupted generation
kothaset generate --resume dataset.jsonl.checkpoint

# Dry run (validate config)
kothaset generate --dry-run -n 100 --seed 42 -i topics.txt

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Documentation

Getting Started

• Quick Start Guide
• Examples

Reference

• Configuration Reference
• Schema Guide
• Provider Setup
• CLI Reference

Help

• FAQ
• Troubleshooting

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Contributing

Contributions welcome! See CONTRIBUTING.md.

License

Apache 2.0 License. See LICENSE.