FuseAI/FuseChat-Mixture-Qwen1.5-Chat-72B-Aligned-Representation

Dataset Card for FuseChat-Mixture

Dataset Description

FuseChat-Mixture is the training dataset used in 📑FuseChat: Knowledge Fusion of Chat Models

FuseChat-Mixture is a comprehensive training dataset covers different styles and capabilities, featuring both human-written and model-generated, and spanning general instruction-following and specific skills. These sources include:

• Orca-Best: We sampled 20,000 examples from Orca-Best, which is filtered from the original GPT-4 (1M) partition of Orca based on maximum length and embedding clustering of instructions.

• Capybara: We incorporated all the 16,000 examples of Capybara, which is a high-quality collection of multi-turn synthetic conversations.

• No-Robots: We included all the 9,500 examples of No-Robots, which is a dataset created by skilled human annotators for supervised fine-tuning.

• ShareGPT-GPT4: We utilized all 6,200 examples from ShareGPT-GPT4, which exclusively uses dialogues generated by GPT-4 in ShareGPT.

• Oasst-Top1: We selected 5,000 examples from Oasst-Top1, which is a refined version of Oasst1, a human-annotated assistant-style conversation dataset.

• MetaMathQA: We sampled 10,000 examples from MetaMathQA~\citep{yu2023metamath}, which is augmented from the GSM8K and MATH datasets for mathematics problem-solving.

• OSS-Instruct: We chose 10,000 examples from OSS-Instruct, which contains code instruction data synthesized from open-source code snippets.

• Evol-Alpaca: We sampled 10,000 examples from Evol-Alpaca, which is a code instruction dataset generated by GPT-4 with evol-instruct proposed by WizardCoder.

• Python-Code: We selected 10,000 examples from Python-Code, which comprises instructions and responses generated by GPT-3.5 and GPT-4 for python code generation.

We followed the data processing code in Vicuna to clean instances containing non-English or special characters. Then, we split long conversations into blocks with a maximum length of 2048 tokens, resulting in the final FuseChat Mixture with 95,000 examples.

Citation

If you find this work is relevant with your research or applications, please feel free to cite our work!

@article{wan2024fusechat,
  title={FuseChat: Knowledge Fusion of Chat Models},
  author={Fanqi Wan and Ziyi Yang and Longguang Zhong and Xiaojun Quan and Xinting Huang and Wei Bi},
  journal={arXiv preprint arXiv:2402.16107},
  year={2024}
}