From b35abc28616c4f816cca909e538c4279674b5382 Mon Sep 17 00:00:00 2001 From: ganzhiruyi Date: Wed, 21 Jun 2023 21:05:09 +0800 Subject: [PATCH] update readme and upload pics --- README.md | 69 +++++++++++++++++++------------- README_en.md | 82 ++++++++++++++++++++++++++------------ pics/all_models.png | Bin 0 -> 592977 bytes pics/start_opensource.png | Bin 0 -> 100256 bytes 4 files changed, 99 insertions(+), 52 deletions(-) create mode 100644 pics/all_models.png create mode 100644 pics/start_opensource.png diff --git a/README.md b/README.md index 72eb6bdf..2bb704d6 100644 --- a/README.md +++ b/README.md @@ -29,9 +29,14 @@ > [**UniEX**](https://arxiv.org/abs/2305.10306): 统一抽取任务的自然语言理解模型。(```ACL 2023```) +> [**Solving Math Word Problems via Cooperative Reasoning induced Language Models**](https://2023.aclweb.org/program/accepted_main_conference/): 使用语言模型的协同推理框架解决数学问题。(```ACL 2023```) + +> [**MVP-Tuning**](https://2023.aclweb.org/program/accepted_main_conference/): 基于多视角知识检索的参数高效常识问答系统。(```ACL 2023```) + # 封神榜大事件 +- [多模态Ziya上线!姜子牙通用模型垂直能力系列 Vol.1发布](https://mp.weixin.qq.com/s/-gv9tG5-Vqo2iN_ETO84KQ) 2023.06.05 - [IDEA研究院封神榜团队再次出击, 推出开源通用大模型系列“姜子牙”](https://mp.weixin.qq.com/s/IeXgq8blGoeVbpIlAUCAjA) 2023.05.17 - [首个中文Stable Diffusion模型开源,IDEA研究院封神榜团队开启中文AI艺术时代](https://mp.weixin.qq.com/s/WrzkiJOxqNcFpdU24BKbMA) 2022.11.2 - [打破不可能三角、比肩5400亿模型,IDEA封神榜团队仅2亿级模型达到零样本学习SOTA](https://mp.weixin.qq.com/s/m0_W31mP4xKKla8jIwUXkw) 2022.10.25 @@ -52,17 +57,12 @@ - [模型系列简介](#模型系列简介) - [Fengshenbang-LM](#fengshenbang-lm) - [封神榜模型](#封神榜模型) + - [姜子牙系列](#姜子牙系列) - [二郎神系列](#二郎神系列) - - [模型下载地址](#模型下载地址) - - [模型加载](#模型加载) - - [使用示例](#使用示例) - - [下游效果](#下游效果) + - [太乙系列](#太乙系列) - [封神框架](#封神框架) - [安装](#安装) - - [使用自己的环境安装](#使用自己的环境安装) - - [使用Docker](#使用docker) - [Pipelines](#pipelines) -- [封神榜单](#封神榜单) - [封神榜系列文章](#封神榜系列文章) - [引用](#引用) - [联系我们](#联系我们) @@ -72,10 +72,11 @@ |系列名称|需求|适用任务|参数规模|备注| |:---:|:---:|:---:|:---:|---| +|[姜子牙](https://huggingface.co/IDEA-CCNL/Ziya-LLaMA-13B-v1.1)|通用|通用大模型|>70亿参数|通用大模型“姜子牙”系列,具备翻译,编程,文本分类,信息抽取,摘要,文案生成,常识问答和数学计算等能力| +|[太乙](https://fengshenbang-doc.readthedocs.io/zh/latest/docs/%E5%A4%AA%E4%B9%99%E7%B3%BB%E5%88%97/index.html)|特定|多模态|8千万-10亿参数|应用于跨模态场景,包括文本图像生成,蛋白质结构预测, 语音-文本表示等| |[二郎神](https://fengshenbang-doc.readthedocs.io/zh/latest/docs/%E4%BA%8C%E9%83%8E%E7%A5%9E%E7%B3%BB%E5%88%97/index.html)|通用|语言理解|9千万-39亿参数|处理理解任务,拥有开源时最大的中文bert模型,2021登顶FewCLUE和ZeroCLUE| |[闻仲](https://fengshenbang-doc.readthedocs.io/zh/latest/docs/%E9%97%BB%E4%BB%B2%E7%B3%BB%E5%88%97/index.html)|通用|语言生成|1亿-35亿参数|专注于生成任务,提供了多个不同参数量的生成模型,例如GPT2等| |[燃灯](https://fengshenbang-doc.readthedocs.io/zh/latest/docs/%E7%87%83%E7%81%AF%E7%B3%BB%E5%88%97/index.html)|通用|语言转换|7千万-50亿参数|处理各种从源文本转换到目标文本类型的任务,例如机器翻译,文本摘要等| -|[太乙](https://fengshenbang-doc.readthedocs.io/zh/latest/docs/%E5%A4%AA%E4%B9%99%E7%B3%BB%E5%88%97/index.html)|特定|多模态|8千万-10亿参数|应用于跨模态场景,包括文本图像生成,蛋白质结构预测, 语音-文本表示等| |[余元](https://fengshenbang-doc.readthedocs.io/zh/latest/docs/%E4%BD%99%E5%85%83%E7%B3%BB%E5%88%97/index.html)|特定|领域|1亿-35亿参数|应用于领域,如医疗,金融,法律,编程等。拥有目前最大的开源GPT2医疗模型| |-待定-|特定|探索|-未知-|我们希望与各技术公司和大学一起开发NLP相关的实验模型。目前已有:[周文王](https://fengshenbang-doc.readthedocs.io/zh/latest/docs/%E5%91%A8%E6%96%87%E7%8E%8B%E7%B3%BB%E5%88%97/index.html)| @@ -94,20 +95,14 @@ 这个世界需要一个答案。 -为了解决中文领域研究进展滞后和研究资源严重不足的问题,2021年11月22日,IDEA研究院创院理事长沈向洋在IDEA大会上正式宣布,开启 “封神榜”开源体系——一个以中文驱动的基础生态系统,其中包括了预训练大模型,特定任务的微调应用,基准和数据集等。我们的目标是构建一个全面的,标准化的,以用户为中心的生态系统。尽管这一目标可以通过多种方式去实现,但是我们经过对中文社区的重新审视与思考,提出了我们认为最为有效的方案: - -![avatar](pics/fengshenbang_process1.png) - -尽管这看起来可能有些复杂,但是只需三步,用户就可以根据我们的资源轻松构建所需的应用了。 -- 步骤1: 从我们的封神榜模型库中选择一个预训练好的中文NLP模型. -- 步骤2: 通过阅读我们的教程示例,使用封神框架调整模型。 -- 步骤3: 在我们的封神榜单或者自定义任务中评估模型在下游任务上的表现。 +为了解决中文领域研究进展滞后和研究资源严重不足的问题,2021年11月22日,IDEA研究院创院理事长沈向洋在IDEA大会上正式宣布,开启 “封神榜”开源体系——一个以中文驱动的基础生态系统,其中包括了预训练大模型,特定任务的微调应用,基准和数据集等。我们的目标是构建一个全面的,标准化的,以用户为中心的生态系统。 +![avatar](pics/start_opensource.png) # 封神榜模型 “封神榜模型”将全方面的开源一系列NLP相关的预训练大模型。NLP社区中有着广泛的研究任务,这些任务可以被分为两类:通用任务和特殊任务。前者包括了自然语言理解(NLU),自然语言生成(NLG)和自然语言转换(NLT)任务。后者涵盖了多模态,特定领域等任务。我们考虑了所有的这些任务,并且提供了在下游任务上微调好的相关模型,这使得计算资源有限的用户也可以轻松使用我们的基础模型。而且我们承诺,将对这些模型做持续的升级,不断融合最新的数据和最新的训练算法。通过IDEA研究院的努力,打造中文认知智能的通用基础设施,避免重复建设,为全社会节省算力。 -![avatar](pics/model_pic1.png) +![avatar](pics/all_models.png) 同时,“封神榜”也希望各个公司、高校、机构加入到这个开源计划中,一起共建大模型开源体系。未来,当我们需要一个新的预训练模型,都应该是首先从这些开源大模型中选取一个最接近的,做继续训练,然后再把新的模型开源回这个体系。这样,每个人用最少的算力,就能得到自己的模型,同时这个开源大模型体系也能越来越大。 @@ -115,6 +110,36 @@ 为了更好的体验,拥抱开源社区,封神榜的所有模型都转化并同步到了Huggingface社区,你可以通过几行代码就能轻松使用封神榜的所有模型,欢迎来[IDEA-CCNL的huggingface社区](https://huggingface.co/IDEA-CCNL)下载。 +## 姜子牙系列 + +通用大模型“姜子牙”系列,具备翻译,编程,文本分类,信息抽取,摘要,文案生成,常识问答和数学计算等能力。目前姜子牙通用大模型(v1/v1.1)已完成大规模预训练、多任务有监督微调和人类反馈学习三阶段的训练过程。姜子牙系列模型包含以下模型: +- [Ziya-LLaMA-13B-v1.1](https://huggingface.co/IDEA-CCNL/Ziya-LLaMA-13B-v1.1) +- [Ziya-LLaMA-13B-v1](https://huggingface.co/IDEA-CCNL/Ziya-LLaMA-13B-v1) +- [Ziya-LLaMA-7B-Reward](https://huggingface.co/IDEA-CCNL/Ziya-LLaMA-7B-Reward) +- [Ziya-LLaMA-13B-Pretrain-v1](https://huggingface.co/IDEA-CCNL/Ziya-LLaMA-13B-Pretrain-v1) +- [Ziya-BLIP2-14B-Visual-v1](https://huggingface.co/IDEA-CCNL/Ziya-BLIP2-14B-Visual-v1) + +### 模型使用 + +参考 [Ziya-LLaMA-13B-v1](https://huggingface.co/IDEA-CCNL/Ziya-LLaMA-13B-v1) + +### 线上体验 + +- [Huggingface Ziya Space](https://huggingface.co/spaces/IDEA-CCNL/Ziya-v1) +- [Huggingface Ziya-visual Space](https://huggingface.co/spaces/IDEA-CCNL/Ziya-BLIP2-14B-Visual-v1-Demo) +- [ModelScope Ziya Space](https://modelscope.cn/studios/Fengshenbang/Ziya_LLaMA_13B_v1_online/summary) + + +### 微调示例 + +参考 [ziya_finetune](https://github.com/IDEA-CCNL/Fengshenbang-LM/tree/main/fengshen/examples/ziya_llama) + +### 推理量化示例 + +参考 [ziya_inference](https://github.com/IDEA-CCNL/Fengshenbang-LM/tree/main/fengshen/examples/ziya_inference) + + + ## 二郎神系列 Encoder结构为主的双向语言模型,专注于解决各种自然语言理解任务。 @@ -263,16 +288,6 @@ fengshen-pipeline text_classification train --model='IDEA-CCNL/Erlangshen-Robert [三分钟上手封神](fengshen/README.md) -# 封神榜单 - -我们提出的生态系统中还包括了一个基准模块——“封神榜单“,它允许用户在我们的平台上进行公平的比较,并且可以让整个中文社区追踪NLP模型最新进展。 - -为了构建高质量和健壮的基准,我们需要考虑到如何测试模型的方方面面。因此,我们在构建封神榜单时确定了以下要求: -- **广泛认可**:虽然一些现有的数据集不是用中文设计的,但它们多年来在NLP领域中被广泛使用,例如[SuperGLUE](https://super.gluebenchmark.com/)。所以,我们将召集一些专业的中英文的语言专家,精心翻译并校对这些热门的数据集。 -- **面向未来**:一些NLP模型已经在多个基准测试中超越了人类的表现。我们认为更重要和紧迫的工作是构建一些具有挑战性的数据集,而不是将现有数据集拟合到100%的准确度。未来的基准需要考虑更广泛的道德, 技术和社会上的挑战。我们的数据集将会尽快发布,以更好地支持中文社区的进一步研究。 -- **合作共创**:基准需要反映真实世界的场景。我们希望能够与行业活跃的公司合作收集真实世界的数据并发布。 - -我们在未来会发布榜单管理系统,希望可以推动更多定制的排行榜系统的发展,敬请期待。 # 封神榜系列文章 diff --git a/README_en.md b/README_en.md index 7581980a..69c4fb34 100644 --- a/README_en.md +++ b/README_en.md @@ -31,8 +31,14 @@ > [**UniEX**](https://arxiv.org/abs/2305.10306): A Natural Language Understanding Model for Unified Extraction Tasks.(```ACL 2023```) -# Fengshenbang Big Event +> [**Solving Math Word Problems via Cooperative Reasoning induced Language Models**](https://2023.aclweb.org/program/accepted_main_conference/): Solving Math Word Problems via Cooperative Reasoning induced Language Models.(```ACL 2023```) + +> [**MVP-Tuning**](https://2023.aclweb.org/program/accepted_main_conference/): 基Multi-View Knowledge Retrieval with Prompt Tuning +for Commonsense Reasoning.(```ACL 2023```) + +# Fengshenbang Big Event +- [Fengshenbang team open-sources the "Ziya-Visual"](https://mp.weixin.qq.com/s/-gv9tG5-Vqo2iN_ETO84KQ) 2023.06.05 - [Fengshenbang team open-sources the general large-scale model series "Ziya"](https://mp.weixin.qq.com/s/IeXgq8blGoeVbpIlAUCAjA) 2023.05.17 - [First Chinese stable diffusion model is open-sourced,IDEA Fengshenbang team opens the era of Chinese AI art](https://mp.weixin.qq.com/s/WrzkiJOxqNcFpdU24BKbMA) 2022.11.2 - [Breaking the impossible triangle, comparable to 540B models, IDEA Fengshenbang team only has 200B models to achieve zero-shot SOTA](https://mp.weixin.qq.com/s/m0_W31mP4xKKla8jIwUXkw) 2022.10.25 @@ -46,6 +52,22 @@ - [IDEA Meeting Release|"Fengshenbang" Open Source Project](https://mp.weixin.qq.com/s/Ct06-vLEKoYMyJQPBV2n0w) 2021.11.25 - [IDEA Chinese pre-trained language model Erlangshen tops the FewCLUE benchmark](https://mp.weixin.qq.com/s/bA_9n_TlBE9P-UzCn7mKoA) 2021.11.11 +- [封神榜科技成果](#封神榜科技成果) +- [封神榜大事件](#封神榜大事件) +- [导航](#导航) +- [模型系列简介](#模型系列简介) +- [Fengshenbang-LM](#fengshenbang-lm) +- [封神榜模型](#封神榜模型) + - [姜子牙系列](#姜子牙系列) + - [二郎神系列](#二郎神系列) + - [太乙系列](#太乙系列) +- [封神框架](#封神框架) + - [安装](#安装) + - [Pipelines](#pipelines) +- [封神榜系列文章](#封神榜系列文章) +- [引用](#引用) +- [联系我们](#联系我们) +- [版权许可](#版权许可) # Navigation - [Fengshenbang Achievements](#fengshenbang-achievements) @@ -54,15 +76,11 @@ - [Model Infofrmation](#model-infofrmation) - [Fengshenbang-LM](#fengshenbang-lm) - [Fengshenbang Model](#fengshenbang-model) + - [Ziya](#Ziya) - [Erlangshen](#erlangshen) - - [Download the Models](#download-the-models) - - [Load the Models](#load-the-models) - - [Example of Usage](#example-of-usage) - - [Performance on Downstream Tasks](#performance-on-downstream-tasks) + - [Taiyi](#Taiyi) - [Fengshen Framework](#fengshen-framework) - [Installation](#installation) - - [Installing in an existing environment](#installing-in-an-existing-environment) - - [Using Docker](#using-docker) - [Pipelines](#pipelines) - [Fengshen Benchmark](#fengshen-benchmark) - [Fegnshenbang Series Articles](#fegnshenbang-series-articles) @@ -73,6 +91,7 @@ # Model Infofrmation |Series|Demand|Task|Parameter Scale|Extra| |:---:|:---:|:---:|:---:|---| +|[Ziya](https://huggingface.co/IDEA-CCNL/Ziya-LLaMA-13B-v1.1)|General|AGI|>7B|Ziya has the capabilities of translation, programming, text classification, information extraction, summarization, copy generation, common sense question and answer, and mathematical calculation.| |[Erlangshen](https://fengshenbang-doc.readthedocs.io/zh/latest/docs/%E4%BA%8C%E9%83%8E%E7%A5%9E%E7%B3%BB%E5%88%97/index.html)|General|NLU|97M-3.9B|Erlangshen was designed to solve NLU tasks; The largest BERT when publicly released; SOTA on FewCLUE and ZeroCLUE in 2021.| |[Wenzhong](https://fengshenbang-doc.readthedocs.io/zh/latest/docs/%E9%97%BB%E4%BB%B2%E7%B3%BB%E5%88%97/index.html)|General|NLG|1B-3.5B|Wenzhong focuses on NLG tasks; Provides several generative models with different scales, such as GPT2, etc.| |[Randeng](https://fengshenbang-doc.readthedocs.io/zh/latest/docs/%E7%87%83%E7%81%AF%E7%B3%BB%E5%88%97/index.html)|General|NLT|770M-5B|Randeng handles natural language transformation (NLT) type tasks that convert from source text to target text, such as machine translation, text summarization, etc.| @@ -97,28 +116,48 @@ The Chinese language as the world's largest spoken language (native speakers), h And the world needs an answer for this. -On November 22nd, 2021, Harry Shum, the Founder and Chairman of the IDEA (International Digital Economy Academy) officially announces the launch of "Fengshenbang" open source project. —— a Chinese language driven foundation ecosystem, incorporates pre-trained models, task-specific fine-tune applications, benchmarks, and datasets. Our goal is to build a comprehensive, standardized and user-centered ecosystem. Although this can be instantiated in a variety of ways, we present the following design that we find to be particularly effective: - -![avatar](pics/fengshenbang_enprocess1.png) +On November 22nd, 2021, Harry Shum, the Founder and Chairman of the IDEA (International Digital Economy Academy) officially announces the launch of "Fengshenbang" open source project. —— a Chinese language driven foundation ecosystem, incorporates pre-trained models, task-specific fine-tune applications, benchmarks, and datasets. +![avatar](pics/start_opensource.png) -Although this seems complicated, with only 3 sequential steps, users can build their applications based on our resources. -- Step 1: Choosing a pre-trained Chinese NLP model from our open-source library of Fengshenbang Models. -- Step 2: Employing Fengshen Framework to adjust the model by exploring the our tutorial examples. -- Step 3: Evaluating on downstream tasks, such as Fengshenbang Benchmarks or custom tasks. # Fengshenbang Model "Fengshenbang Model" will open-source a series of NLP-related pre-trained models in all aspects. There are a wide range of research tasks in the NLP community, which can be divided into two categories: general demands and special demands. In general demands, there are common NLP tasks, which are classified into Natural Language Understanding (NLU), Natural Language Generation (NLG), and Natural Language Transformation (NLT). Due to the fast development, NLP community brings special demands to the entire AI community, which are often assigned to MultiModal (MM), Domains and Exploration. We consider all of these tasks and provide models that are fine tuning for downstream tasks, making our base model easy to use for users with limited computing resources. We consider all of these demands and provide models that are fine-tuned for downstream tasks, making our base model easy to use for users with limited computing resources. Moreover, we guarantee that we will optimize the models continuously with new datasets and latest algorithms. We aim to build universal infrastructure for Chinese cognitive intelligence and prevent duplicative construction, and hence save computing resources for the community. -![avatar](pics/model_pic1.png) +![avatar](pics/all_models.png) We also call for businesses, universities and institutions to join us with the project and build the sytem of large-scale open-source models collaboratively. We envision that, in the near future, the first choice when in need of a new pretrained model should be selecting one in closest proximity to the desired scale,architecture and domain from the series, followed by further training. After obtaining a trained new model, we shall add it back to the series of open-source models for future usage. In this way we build the open-source system iteratively and collaboratively while individuals could get desired models using minimal computing resources. -![avatar](pics/model_pic2.png) - For better open source experience, all models of the Fengshenbang series are synchronized within the Huggingface community, and can be obtained for use within few lines of code. Welcome to download and use our models from our repo at [IDEA-CCNL at HuggingFace](https://huggingface.co/IDEA-CCNL). +## Ziya + +The general large-scale model "Ziya" series has the capabilities of translation, programming, text classification, information extraction, summarization, copy generation, common sense question and answer, and mathematical calculation. At present, Ziya's general-purpose large model (v1/v1.1) has completed a three-stage training process of large-scale pre-training, multi-task supervised fine-tuning, and human feedback learning. Ziya series models include the following models: +- [Ziya-LLaMA-13B-v1.1](https://huggingface.co/IDEA-CCNL/Ziya-LLaMA-13B-v1.1) +- [Ziya-LLaMA-13B-v1](https://huggingface.co/IDEA-CCNL/Ziya-LLaMA-13B-v1) +- [Ziya-LLaMA-7B-Reward](https://huggingface.co/IDEA-CCNL/Ziya-LLaMA-7B-Reward) +- [Ziya-LLaMA-13B-Pretrain-v1](https://huggingface.co/IDEA-CCNL/Ziya-LLaMA-13B-Pretrain-v1) +- [Ziya-BLIP2-14B-Visual-v1](https://huggingface.co/IDEA-CCNL/Ziya-BLIP2-14B-Visual-v1) + +### Example of Usage + +Refer to [Ziya-LLaMA-13B-v1](https://huggingface.co/IDEA-CCNL/Ziya-LLaMA-13B-v1) + +### Online Demo + +- [Huggingface Ziya Space](https://huggingface.co/spaces/IDEA-CCNL/Ziya-v1) +- [Huggingface Ziya-visual Space](https://huggingface.co/spaces/IDEA-CCNL/Ziya-BLIP2-14B-Visual-v1-Demo) +- [ModelScope Ziya Space](https://modelscope.cn/studios/Fengshenbang/Ziya_LLaMA_13B_v1_online/summary) + + +### Finetune Example + +Refer to [ziya_finetune](https://github.com/IDEA-CCNL/Fengshenbang-LM/tree/main/fengshen/examples/ziya_llama) + +### Inference & Quantization Example + +Refer to [ziya_inference](https://github.com/IDEA-CCNL/Fengshenbang-LM/tree/main/fengshen/examples/ziya_inference) ## Erlangshen @@ -264,15 +303,8 @@ fengshen-pipeline text_classification train --model='IDEA-CCNL/Erlangshen-Robert [Get Started with Fengshen in 3 Minutes](fengshen/README.md) -# Fengshen Benchmark -Our "Fengshenbang" ecosystem also includes a benchmark module - "Fengshen Benchmark", which allows users to make fair comparisons on our platform and allows the entire Chinese community to track the latest advances in NLP models. - -To collect high-quality and robust benchmarks, we consider different aspects of testing the models. As a result, we identify the following requirements while building the Fengshenbang benchmark: -- **Widely evaluated**: While some existing datasets are not designed in Chinese, they have been used extensively in NLP for years, e.g. SuperGLUE. We will gather some professional English and Chinese linguists to meticulously translate these popular datasets. -- **Future-oriented**: In fact, a few NLP models already surpass human performance on several benchmarks. This declares that AI has reached or even can surpass human cognitive intelligence. One reason we believe is their limited scope of evaluation. A more urgent and necessary work is to construct challenging datasets instead of fitting existing datasets to $100\%$ accuracy. Future benchmarks need to consider broader ethical, technical, and societal challenges. Our datasets will be published soon to better support the research community. -- **Applicable**: Benchmarks are required to represent real-world scenarios. This allows us to collaborate with industry-active companies to publish datasets and collect real-world data. -# Fegnshenbang Series Articles +# Fengshenbang Series Articles [Fengshen Series: Getting Started on Training Large Model with Data Parallelism](https://zhuanlan.zhihu.com/p/512194216) diff --git a/pics/all_models.png b/pics/all_models.png new file mode 100644 index 0000000000000000000000000000000000000000..c97354b67c13c8ff3021de69076aae6112ea39da GIT binary patch literal 592977 zcmZsD19YX$vS^HnZBK05wkEc1dtz&18xz~MZ95a~I6Kap|Gjh0yZ61j*Xq^v)voTZ zy1Kfmy1Vv{R8o*c_=fup1Ox;@T1rd>1Oz+`1OyBl2I?<{+43&xZw2b2A}In=GlPHj z_rcRlQ`%f!9)#vE3rqmIPbzrw+TaFxG5`ZbSbTFxbElArWGd1Z1gyJ$AB#g$~r$R&7_KNQEl?Pc$AT5Z0^j z76qQ13;{IW|E=+V`hp4kj!nVw?>_&7{^yMzE)c&AJvl9Y{&@dy_l4 z{iWQqj}tdImjqVfM9M;T;v@c&Bhe~-wKQ<4}q%){_R(LrN4t1M{%@XO7Zpc7C*dNc4sD{i1EPx-ldk-_{Qq$fkHJ z2rL=PSD}&BOm_Uen=%oBS7}{mG$+mQU(_f#LP%KDa~wm4VB>7Drk5jcMSs*VL_E>T z$ivokZJFj3uhMdBMZ^IW70Fnyq}&4huc)&TniKArDv_z0+&Va{V9Pd_*^pCkk{>h( 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