Infinity-Instruct

Infinity Instruct Beijing Academy of Artificial Intelligence (BAAI) [Paper][Code][🤗] The quality and scale of instruction data are crucial for model performance. Recently, open-source models have increasingly relied on fine-tuning datasets comprising millions of instances, necessitating both high quality and large scale. However, the open-source community has long been constrained by the high costs associated with building such extensive and high-quality instruction fine-tuning datasets, which has limited related research and applications. To address this gap, we are introducing the Infinity Instruct project, aiming to develop a large-scale, high-quality instruction dataset. News 🔥🔥🔥[2025/12/04] We release the [paper] (accepted to AAAI 2026) for Infinity Instruct Subject. 🔥🔥🔥[2025/05/13] We release the core version of the 7M foundational dataset, 7M Core! It reaches 95.7% performance of full 7M instructions with only 1.4M instructions. 🔥🔥🔥[2025/01/06] We supplemented 7M and Gen's instruction labeling types and reward scores based on a self-constructed instruction labeling system and reward model Skywork/Skywork-Reward-Llama-3.1-8B-v0.2. You can build customized instruction datasets based on this information. 🔥🔥🔥[2024/08/29] We release the first version of the preference data built from Infinity-Instruct, Infinity-Preference. The SimPO version model, Gemma2-9B-IT-Simpo-Infinity-Preference finetuned on Infinity-Preference is also publicly accessible. 🔥🔥🔥[2024/08/02] We release the model weights of InfInstruct-Llama3.1-70B Gen, InfInstruct-Llama3.1-8B Gen, InfInstruct-Mistral-7B Gen. 🔥🔥🔥[2024/08/02] We release the 7M foundational dataset Infinity-Instruct-7M. 🔥🔥🔥[2024/07/09] We release the model weights of InfInstruct-Mistral-7B 0625, InfInstruct-Qwen2-7B 0625, InfInstruct-Llama3-8B 0625, InfInstruct-Llama3-70B 0625, and InfInstruct-Yi-1.5-9B 0625. 🔥🔥🔥[2024/07/09] We release the chat dataset Infinity-Instruct-0625, it is a upgraded version of the Infinity-Instruct-0613