liquidrandom 0.1.0

Pseudo-random seed data generation for ML/LLM training diversity

liquidrandom

Pseudo-random seed data for ML/LLM training diversity.

When using LLMs to generate training data, outputs tend to be repetitive and lack variety. liquidrandom solves this by providing a large pool of diverse, pre-generated seed data (personas, jobs, scenarios, etc.) that you can inject into your prompts to steer generation toward more varied outputs.

Installation

pip install liquidrandom
# or
uv add liquidrandom

Quick Start

import liquidrandom

# Get a random persona to inject into your LLM prompt
persona = liquidrandom.persona()
print(persona)
# Alice is a 30-year-old female from Canada. They work as an engineer. ...

# Get a random coding task
task = liquidrandom.coding_task()
print(task)
# [Python, medium] Implement a trie: Build a trie data structure ...

Available Categories

Function	Returns	Description
liquidrandom.persona()	Persona	Random personas with name, age, gender, occupation, nationality, personality traits, background
liquidrandom.job()	Job	Professions with title, industry, description, required skills, experience level
liquidrandom.coding_task()	CodingTask	Programming challenges with title, language, difficulty, description, constraints, expected behavior
liquidrandom.math_category()	MathCategory	Math categories with name, field, description, example problems
liquidrandom.writing_style()	WritingStyle	Writing styles with name, tone, characteristics, description
liquidrandom.scenario()	Scenario	Real-world scenarios with title, context, setting, stakes, description
liquidrandom.domain()	Domain	Knowledge domains with name, parent field, description, key concepts
liquidrandom.science_topic()	ScienceTopic	Scientific topics with name, field, subfield, description
liquidrandom.language()	Language	Languages/locales with name, region, register, script, cultural notes
liquidrandom.reasoning_pattern()	ReasoningPattern	Reasoning approaches with name, category, description, when to use
liquidrandom.emotional_state()	EmotionalState	Emotional states with name, intensity, valence, behavioral description
liquidrandom.instruction_complexity()	InstructionComplexity	Instruction complexity levels with level, ambiguity, description, example

Usage Example

Use liquidrandom to add diversity to your LLM data generation pipeline:

import liquidrandom
from openai import OpenAI

client = OpenAI(
    base_url="https://openrouter.ai/api/v1",
    api_key="<OPENROUTER_API_KEY>",
)

persona = liquidrandom.persona()
style = liquidrandom.writing_style()
topic = liquidrandom.science_topic()

prompt = f"""You are {persona}
Write in the following style: {style}
Explain the following topic: {topic}"""

response = client.chat.completions.create(
    model="liquid/lfm-2-24b-a2b",
    messages=[{"role": "user", "content": prompt}],
)

Each call to a liquidrandom function returns a typed dataclass. You can use them directly in f-strings (via __str__) or access their individual fields:

persona = liquidrandom.persona()
print(persona.name)               # "Alice"
print(persona.age)                 # 30
print(persona.personality_traits)  # ["curious", "patient"]

How It Works

The dataset contains 340,000+ samples across 12 categories, generated using hierarchical taxonomy trees with LLM-based quality validation and fuzzy deduplication.

Seed data is hosted on HuggingFace (mlech26l/liquidrandom-data) as zstd-compressed Parquet files. On first use, only the requested category file is downloaded and cached locally. Subsequent calls use the cached data.

License

MIT