Can I use this for DPO or GRPO datasets?

Yes, the synthesizer includes specific interactive flows to configure pipelines for SFT, DPO, and GRPO dataset creation.

Does it support local LLMs like Ollama?

Yes, it can configure pipelines to use any OpenAI-compatible API, including local deployments like Ollama, vLLM, and LM Studio.

What input data sources are supported?

Pipelines can be configured to use local files (CSV, Parquet, JSONL), HuggingFace datasets, SQL databases via connectorx, or generated from scratch.

How does it handle data quality and deduplication?

The skill can generate pipeline steps for exact hash deduplication, fuzzy simhash deduplication, semantic embedding-based filtering, and JSON schema validation.

TweakTune is a Rust-powered, Python-facing library designed for building scalable pipelines to synthesize training data for Large Language Models.

TweakTune Data Synthesizer

Name: TweakTune Data Synthesizer
Author: qooba

byqooba

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Ciencia de Datos y ML

Designs and generates high-performance pipelines for synthesizing high-quality LLM training datasets, conversations, and structured data.

Acerca de

TweakTune Data Synthesizer is an interactive assistant that streamlines the creation of synthetic training data for large language models. By guiding users through a structured discovery process, it generates production-ready Python code for the TweakTune library to create datasets for Supervised Fine-Tuning (SFT), DPO, and GRPO. It automates complex tasks like multi-turn conversation building, function-calling simulation, and data validation, leveraging Rust-powered performance for efficient, scalable data synthesis.

Características Principales

Advanced deduplication via hash, simhash, and embeddings
2 GitHub stars
Multi-provider LLM support including OpenAI, Azure, and local APIs
Automated data validation for JSON schemas and conversation formats
Interactive Q&A for rapid pipeline prototyping
Multi-turn conversation and function-calling dataset generation

Casos de Uso

Generating synthetic datasets for LLM fine-tuning (SFT/DPO/GRPO)
Creating structured JSON datasets from unstructured text or seeds
Simulating complex tool-use and function-calling dialogues for agent training

Acerca de

Características Principales

Advanced deduplication via hash, simhash, and embeddings
2 GitHub stars
Multi-provider LLM support including OpenAI, Azure, and local APIs
Automated data validation for JSON schemas and conversation formats
Interactive Q&A for rapid pipeline prototyping
Multi-turn conversation and function-calling dataset generation

Casos de Uso

Generating synthetic datasets for LLM fine-tuning (SFT/DPO/GRPO)
Creating structured JSON datasets from unstructured text or seeds
Simulating complex tool-use and function-calling dialogues for agent training