tulu-3-wildchat-reused-on-policy-70b

<img src="https://huggingface.co/datasets/allenai/blog-images/resolve/main/tulu-3/Tulu3-logo.png" alt="Tulu3 banner" width="400" style="margin-left:'auto' margin-right:'auto' display:'block'"/>

# Llama 3.1 Tulu 3 Wildchat reused (on-policy 70B)

*Note that this collection is licensed under ODC-BY-1.0 license; different licenses apply to subsets of the data. Some portions of the dataset are non-commercial. We present the mixture as a research artifact.*

This preference dataset is part of our Tulu 3 preference mixture:
it contains prompts from [WildChat](allenai/WildChat-1M) and it contains 17,207 generation pairs (some of which on-policy completions from https://huggingface.co/allenai/Llama-3.1-Tulu-3-70B) obtained using the following models:

- [Mistral 7B Instruct v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) (Apache 2.0)
- [Mistral Nemo Instruct 2407](https://huggingface.co/mistralai/Mistral-Nemo-Instruct-2407) (Apache 2.0)
- [Tulu 2 7B](https://huggingface.co/allenai/tulu-2-7b) (Ai2 ImpACT Low Risk License)
- [Tulu 2 13B](https://huggingface.co/allenai/tulu-2-13b) (Ai2 ImpACT Low Risk License)
- [Yi-34B-Chat](https://huggingface.co/01-ai/Yi-34B-Chat) (Apache 2.0)
- [Yi-6B-Chat](https://huggingface.co/01-ai/Yi-6B-Chat) (Apache 2.0)
- [MPT 30B Chat](https://huggingface.co/mosaicml/mpt-30b-chat) (CC-BY-SA-4.0)
- [MPT 7B 8k Chat](https://huggingface.co/mosaicml/mpt-7b-8k-chat) (CC-BY-SA-4.0)
- [Google Gemma 2 27B it](https://huggingface.co/google/gemma-2-27b-it) (Gemma is provided under and subject to the Gemma Terms of Use found at [ai.google.dev/gemma/terms](https://ai.google.dev/gemma/terms))
- [Google Gemma 2 9B it](https://huggingface.co/google/gemma-2-9b-it) (Gemma is provided under and subject to the Gemma Terms of Use found at [ai.google.dev/gemma/terms](https://ai.google.dev/gemma/terms))
- [InternLM2.5 20B](https://huggingface.co/internlm/internlm2_5-20b-chat) (InternLM weights are fully open for academic research and also allow free commercial usage. A commercial license can be obtained as instructed in the model card.)
- [InternLM2.5 7B](https://huggingface.co/internlm/internlm2_5-7b-chat) (InternLM weights are fully open for academic research and also allow free commercial usage. A commercial license can be obtained as instructed in the model card.)
- [InternLM2.5 1.8B](https://huggingface.co/internlm/internlm2_5-1_8b-chat) (InternLM weights are fully open for academic research and also allow free commercial usage. A commercial license can be obtained as instructed in the model card.)
- [Falcon 7B](https://huggingface.co/tiiuae/falcon-7b-instruct) (Apache 2.0)
- [Qwen2.5 72B Instruct](https://huggingface.co/Qwen/Qwen2.5-72B-Instruct) (Qwen is licensed under the Qwen LICENSE AGREEMENT, Copyright (c) Alibaba Cloud. All Rights Reserved.)
- [Qwen2.5 32B Instruct](https://huggingface.co/Qwen/Qwen2.5-32B-Instruct) (Apache 2.0)
- [Qwen2.5 14B Instruct](https://huggingface.co/Qwen/Qwen2.5-14B-Instruct) (Apache 2.0)
- [Qwen2.5 7B Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) (Apache 2.0)
- [Llama 3.1 8B Instruct ](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) (this dataset was partially "Built with Llama" and is thus subject to the Llama 3.1 License)
- [Llama 3.1 70B Instruct](https://huggingface.co/meta-llama/Llama-3.1-70B-Instruct) (this dataset was partially "Built with Llama" and is thus subject to the Llama 3.1 License)
- [Llama 3 8B Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B) (this dataset was partially "Built with Meta Llama 3" and is thus subject to the Llama 3 License)
- [GPT-4 Turbo](https://openai.com/index/new-models-and-developer-products-announced-at-devday/) and [GPT-4o](https://openai.com/index/hello-gpt-4o/) (Outputs produced by GPT-4 are subject to OpenAI's [terms of use](https://openai.com/policies/row-terms-of-use))
- [Claude 3.5 Sonnet](https://www.anthropic.com/news/claude-3-5-sonnet) (Outputs produced by Claude are subject to Anthropic [terms of service](https://www.anthropic.com/legal/commercial-terms) and [usage policy](https://www.anthropic.com/legal/aup))

## Completion Generation Approach:
Given a set of prompts, we generated the completions and preferences using a synthetic pipeline that combines both on-policy and off-policy data, and obtained the preference annotations on four different aspects using the Ultrafeedback template and an LLM judge. The code for the synthetic generation pipeline is found in the scripts/synth_pref directory of [open-instruct](https://github.com/allenai/open-instruct/)

## License
This dataset is licensed under ODC-BY. It is intended for research and educational use in accordance with Ai2's [Responsible Use Guidelines](https://allenai.org/responsible-use). This dataset includes output data generated from third party models that are subject to separate terms governing their use.

<img src="https://huggingface.co/datasets/allenai/blog-images/resolve/main/tulu-3/Tulu3-logo.png" alt="Tulu 3 横幅" width="400" style="margin-left:auto; margin-right:auto; display:block"/>
# Llama 3.1 Tulu 3 Wildchat 复用数据集（策略内（on-policy）70B 版本）

*请注意，本数据集合集采用 ODC-BY-1.0 许可证授权；数据集的不同子集可能适用不同的许可证。部分数据集内容为非商用性质。本混合数据集仅作为研究成果发布。

本偏好数据集属于 Tulu 3 偏好混合数据集的一部分：其提示词源自 [WildChat](allenai/WildChat-1M)，共包含17207组生成对（其中部分策略内（on-policy）生成结果来自 https://huggingface.co/allenai/Llama-3.1-Tulu-3-70B），生成所用模型如下：

- [Mistral 7B Instruct v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)（采用 Apache 2.0 许可证）
- [Mistral Nemo Instruct 2407](https://huggingface.co/mistralai/Mistral-Nemo-Instruct-2407)（采用 Apache 2.0 许可证）
- [Tulu 2 7B](https://huggingface.co/allenai/tulu-2-7b)（采用 Ai2 ImpACT 低风险许可证）
- [Tulu 2 13B](https://huggingface.co/allenai/tulu-2-13b)（采用 Ai2 ImpACT 低风险许可证）
- [Yi-34B-Chat](https://huggingface.co/01-ai/Yi-34B-Chat)（采用 Apache 2.0 许可证）
- [Yi-6B-Chat](https://huggingface.co/01-ai/Yi-6B-Chat)（采用 Apache 2.0 许可证）
- [MPT 30B Chat](https://huggingface.co/mosaicml/mpt-30b-chat)（采用 CC-BY-SA-4.0 许可证）
- [MPT 7B 8k Chat](https://huggingface.co/mosaicml/mpt-7b-8k-chat)（采用 CC-BY-SA-4.0 许可证）
- [Google Gemma 2 27B it](https://huggingface.co/google/gemma-2-27b-it)（Gemma 模型需遵循 [ai.google.dev/gemma/terms](https://ai.google.dev/gemma/terms) 中的 Gemma 使用条款）
- [Google Gemma 2 9B it](https://huggingface.co/google/gemma-2-9b-it)（Gemma 模型需遵循 [ai.google.dev/gemma/terms](https://ai.google.dev/gemma/terms) 中的 Gemma 使用条款）
- [InternLM2.5 20B](https://huggingface.co/internlm/internlm2_5-20b-chat)（InternLM 权重完全开放用于学术研究，同时支持免费商用；如需获取商业许可证，请参照模型卡中的说明进行操作）
- [InternLM2.5 7B](https://huggingface.co/internlm/internlm2_5-7b-chat)（InternLM 权重完全开放用于学术研究，同时支持免费商用；如需获取商业许可证，请参照模型卡中的说明进行操作）
- [InternLM2.5 1.8B](https://huggingface.co/internlm/internlm2_5-1_8b-chat)（InternLM 权重完全开放用于学术研究，同时支持免费商用；如需获取商业许可证，请参照模型卡中的说明进行操作）
- [Falcon 7B](https://huggingface.co/tiiuae/falcon-7b-instruct)（采用 Apache 2.0 许可证）
- [Qwen2.5 72B Instruct](https://huggingface.co/Qwen/Qwen2.5-72B-Instruct)（Qwen 采用 Qwen 许可协议授权，版权所有 © 阿里巴巴云计算有限公司，保留所有权利）
- [Qwen2.5 32B Instruct](https://huggingface.co/Qwen/Qwen2.5-32B-Instruct)（采用 Apache 2.0 许可证）
- [Qwen2.5 14B Instruct](https://huggingface.co/Qwen/Qwen2.5-14B-Instruct)（采用 Apache 2.0 许可证）
- [Qwen2.5 7B Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct)（采用 Apache 2.0 许可证）
- [Llama 3.1 8B Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct)（本数据集部分内容"基于Llama开发"，因此需遵循 Llama 3.1 许可证）
- [Llama 3.1 70B Instruct](https://huggingface.co/meta-llama/Llama-3.1-70B-Instruct)（本数据集部分内容"基于Llama开发"，因此需遵循 Llama 3.1 许可证）
- [Llama 3 8B Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B)（本数据集部分内容"基于Meta Llama 3开发"，因此需遵循 Llama 3 许可证）
- [GPT-4 Turbo](https://openai.com/index/new-models-and-developer-products-announced-at-devday/) 与 [GPT-4o](https://openai.com/index/hello-gpt-4o/)（GPT-4 生成的输出需遵循 OpenAI 的 [使用条款](https://openai.com/policies/row-terms-of-use)）
- [Claude 3.5 Sonnet](https://www.anthropic.com/news/claude-3-5-sonnet)（Claude 生成的输出需遵循 Anthropic 的 [服务条款](https://www.anthropic.com/legal/commercial-terms) 与 [使用政策](https://www.anthropic.com/legal/aup)）

## 生成结果生成方法：
针对给定的提示词集合，我们采用结合了策略内（on-policy）与策略外（off-policy）数据的合成流水线生成结果与偏好标签，并通过 Ultrafeedback 模板与大语言模型（LLM）评判器从四个不同维度获取偏好标注。合成生成流水线的代码可在 [open-instruct](https://github.com/allenai/open-instruct/) 的 scripts/synth_pref 目录中获取。

## 许可证：
本数据集采用 ODC-BY 许可证授权。根据艾伦人工智能研究所（Ai2）的 [负责任使用指南](https://allenai.org/responsible-use)，本数据集仅用于研究与教育用途。本数据集包含由第三方模型生成的输出数据，此类数据需遵循其各自的使用条款。

搜集汇总

背景与挑战

背景概述

该数据集是Tulu 3偏好混合的一部分，包含来自WildChat的提示和17,207个生成对，其中部分使用Llama-3.1-Tulu-3-70B模型生成。它通过多种模型（如Mistral、Yi、MPT等）生成完成和偏好，并采用合成管道和LLM法官进行注释，适用于研究和教育用途，许可证为ODC-BY。