tweaktune-plugins

tweaktune is a Rust-powered, Python-facing library for synthesizing datasets for training and fine-tuning AI models, especially Language Models.

Build powerful data pipelines to generate synthetic text, structured JSON, and function calling datasets using LLM APIs. Perfect for creating high-quality training data for model fine-tuning.

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Documentation

• Complete Documentation - Comprehensive guides and API reference
• Examples - Working code examples
• Getting Started Guide - Quick start tutorial

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Features

Flexible Data Sources

Load data from multiple sources:

• Files: Parquet, CSV, JSONL, JSON
• Databases: PostgreSQL, MySQL, SQLite (via ConnectorX)
• HuggingFace: Direct integration with datasets
• Arrow: PyArrow datasets and record batches
• Python: Dictionaries, functions, Pydantic models
• APIs: OpenAPI specifications for function calling
• SQL: Filter and transform with SQL queries

LLM Integration

Connect to any LLM provider:

• OpenAI: GPT-4, GPT-3.5, and compatible APIs
• Azure OpenAI: Enterprise deployments
• Local Models: Unsloth, MistralRS support
• Custom APIs: Any OpenAI-compatible endpoint

Powerful Pipeline Features

• Parallel Processing: Multi-worker execution for speed
• Dynamic Templates: Jinja2 templating with custom filters
• Data Validation: JSON schema, language detection, custom validators
• Deduplication: Exact hash, fuzzy simhash, semantic embeddings
• Quality Checks: Built-in and custom quality filters
• Conditional Logic: If-else branching in pipelines
• Custom Steps: Extend with Python classes
• Metadata Tracking: Track runs, items, and deduplication state

Dataset Generation

Create datasets for:

• Question-Answer pairs: Synthetic Q&A for training
• Function calling: Tool use and API interaction datasets
• Conversations: Multi-turn dialogue datasets
• Structured output: JSON conforming to schemas
• Chat formatting: Model-specific conversation formatting

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Quick Start

Installation

pip install tweaktune

Simple Example

Generate synthetic data in minutes:

from tweaktune import Pipeline
import os

# Create a Q&A dataset
(Pipeline()
    .with_workers(3)
    .with_llm_openai(
        name="gpt4",
        api_key=os.environ["OPENAI_API_KEY"],
        model="gpt-4o-mini"
    )
    .with_template("system", "You are an expert educator.")
    .with_template("question", "Generate a question about: {{topic}}")
    .with_template("answer", "Answer this question: {{question}}")
    .with_template("output", """{"topic": "{{topic}}", "question": "{{question}}", "answer": "{{answer}}"}""")
    .iter_range(100)
        .add_column("topic", lambda data: f"Topic {data['index']}")
        .generate_text(
            template="question",
            llm="gpt4",
            output="question",
            system_template="system"
        )
        .generate_text(
            template="answer",
            llm="gpt4",
            output="answer",
            system_template="system"
        )
        .write_jsonl(path="qa_dataset.jsonl", template="output")
    .run())

Function Calling Dataset

Create datasets for training models on tool use:

from tweaktune import Pipeline
from pydantic import Field

def search_products(
    query: str = Field(..., description="Search query"),
    category: str = Field(..., description="Product category")
):
    """Search for products in the catalog."""
    pass

(Pipeline()
    .with_workers(5)
    .with_llm_openai("gpt4", api_key, "gpt-4o-mini")
    .with_tools_dataset("tools", [search_products])
    .iter_range(50)
        .sample_tools("tools", 1, "tool")
        # Generate user question, tool call, and response
        # ... (see examples/08_function_calling.py for complete code)
        .render_conversation(
            conversation="@user:question|@assistant:tool_calls([call])|@tool:result|@assistant:answer",
            tools="tool",
            output="conversation"
        )
        .write_jsonl(path="function_calling.jsonl", value="conversation")
    .run())

More examples in the examples directory.

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Learn More

Documentation