Distilabel is a framework for synthetic data and AI feedback for engineers who need fast, reliable and scalable pipelines based on verified research papers.

This repository is the official implementation of TimeHC-RL (Distilabel (Data Generation) + TRL (SFT) + VeRL (GRPO)).

Synthetic Data Generation using LLM via Argilla, Distilabel, ChatGPT, etc.

Repository containing the SPIN experiments on the DIBT 10k ranked prompts

A working repository for experimental pipelines in distilabel

Synthetic Data Generation using LLM via Argilla, Distilabel, ChatGPT, etc.

Distilabel is a framework for synthetic data and AI feedback for engineers who need fast, reliable and scalable pipelines based on verified research papers.

Distilabel DeepSeek-R1 模型蒸馏实战

A repo that implements Stanford CRFM their HELM Instruct with adaptable evaluation criteria

A custom Step for LLM API cost calculation for the distilabel library.

A simple distilabel generation pipeline to create a dataset for inclusivity training for language models.

practice distilabel

No description available

No description available

No description available

No description available

A conda-smithy repository for distilabel.

No description available

No description available

No description available

No description available

A simplified Distilabel-inspired pipeline for synthetic dataset creation using pandas DataFrames and local Ollama models.

Generate synthetic datasets for instruction tuning and preference alignment using tools like `distilabel` for efficient and scalable data creation.

the repo is for the real time rag pipeline for the research papers , extract all the rag research papers from the arxiv and the semantic chunkin is done on it , then the embedding model finetuning is done to make cluster for finetuning embedding model the distilabel is used for generating synthetic data set