zjunlp

MolGen-large MolGen-large was introduced in the paper "Domain-Agnostic Molecular Generation with Self-feedback" and first released in this repository. It is a pre-trained molecular generative model built using the 100\% robust molecular language representation, SELFIES. Model description MolGen-large is the first pre-trained model that only produces chemically valid molecules. With a training corpus of over 100 million molecules in SELFIES representation, MolGen-large learns the intrinsic structural patterns of molecules by mapping corrupted SELFIES to their original forms. Specifically, MolGen-large employs a bidirectional Transformer as its encoder and an autoregressive Transformer as its decoder. Through its carefully designed multi-task molecular prefix tuning (MPT), MolGen-large can generate molecules with desired properties, making it a valuable tool for molecular optimization. Intended uses You can use the raw model for molecule generation or fine-tune it to a downstream task. Please take note that the following examples only demonstrate the utilization of our pre-trained model for molecule generation. See the repository to look for fine-tune details on a task that interests you.

OneKE: A Bilingual Large Language Model for Knowledge Extraction - What is OneKE? - How is OneKE trained? - Getting Started with OneKE - Quick Start - Advanced Use of OneKE - OneKE Instruction Format - Conversion of OneKE Instruction Format - Customized Schema Description Instructions - Evaluation - Continue Training - Citation OneKE is a large-scale model framework for knowledge extraction jointly developed by Ant Group and Zhejiang University. It possesses the capability of generalized knowledge extraction in bilingual Chinese and English, across multiple domains and tasks, and provides comprehensive toolchain support. OneKE has contributed to the OpenKG open knowledge graph community in an open-source manner. Knowledge construction based on unstructured documents has always been one of the key challenges for the large-scale implementation of knowledge graphs. The high fragmentation and unstructured nature of real-world information, along with the substantial disparities between extracted content and its natural language expression, often result in the suboptimal performance of large language models in information extraction tasks. Natural language text often contains ambiguities, polysemies, and metaphors due to implicit and long-distance context associations, posing significant challenges for knowledge extraction tasks. In response to these issues, Ant Group and Zhejiang University leveraged their years of expertise in knowledge graphs and natural language processing to jointly construct and upgrade the capabilities of Ant's large-scale model "BaiLing" in the field of knowledge extraction. They released the bilingual knowledge extraction framework OneKE which included a version based on full parametric fine-tuning of Chinese-Alpaca-2-13B. Evaluation metrics show that OneKE has achieved relatively good performance on several fully supervised and zero-shot entity/relation/event extraction tasks. The unified knowledge extraction framework has wide application scenarios and can significantly reduce the construction costs of domain-specific knowledge graphs. By extracting structured knowledge from massive datasets to construct high-quality knowledge graphs and establish logical associations between knowledge elements, interpretable inference and decision-making can be realized. It can also enhance large models by mitigating hallucination and boosting stability, accelerating the vertical domain applications of large models. For example, in the medical field, knowledge extraction can be used to convert doctors' experience into structured, rule-based management, building controlled auxiliary diagnostics, and medical Q&A systems. In the financial sector, it can extract financial indicators, risk events, causal logic, and industry chains for automated financial report generation, risk prediction, and industry chain analysis. In the public sector, it can facilitate knowledge-based management of government regulations, enhancing the efficiency and accuracy of public services. How is OneKE trained? OneKE mainly focuses on schema-generalizable information extraction. Due to issues such as non-standard formats, noisy data, and lack of diversity in existing extraction instruction data, OneKE adopted techniques such as normalization and cleaning of extraction instructions, difficult negative sample collection, and schema-based batched instruction construction, as shown in the illustration. For more detailed information, refer to the paper "IEPile: Unearthing Large-Scale Schema-Based Information Extraction Corpus [Github]". The zero-shot generalization comparison results of OneKE with other large models are as follows: `NER-en`: CrossNERAI, CrossNERliterature, CrossNERmusic, CrossNERpolitics, CrossNERscience `NER-zh`: WEIBONER, boson `RE-zh`: COAE2016, IPRE, SKE2020 `RE-en`: FewRel, Wiki-ZSL `EE-en`: CrudeOilNews, WikiEvents, RAMS `EE-zh`: FewFC, CCF Law It is recommended to have at least 20GB of VRAM for training and inferencing. For more detailed inference, please refer to DeepKE-llm/InstructKGC/6.1.2IE专用模型. OneKE Instruction Format The instructions in OneKE are formatted in a dictionary-type string similar to JSON. It consists of three fields: (1) `'instruction'`, which is the task description, specifies in natural language the role the model plays and the task to be completed; (2) `'schema'`, a list of labels to be extracted, clearly indicates the key fields of the information to be extracted, reflecting the user's needs, and is dynamic and changeable; (3) `'input'`, refers to the source text for information extraction. Below are examples of instructions for various tasks: > Note: In consideration of the complexity of information extraction within specific domains and the high reliance on prompts, we support the integration of Schema descriptions and examples in the instructions to enhance the effectiveness of extraction tasks. For details, refer to `Customized Schema Description Instructions` and `Customized Example Instructions`. Please understand that due to the limited scale of the model, the model output is prompt-dependent and different prompts may yield inconsistent results. Since predicting all schemas in the label set at once is too challenging and not easily scalable, OneKE uses a batched approach during training. It divides the number of schemas asked in the instructions, querying a fixed number of schemas at a time. Hence, if the label set of a piece of data is too long, it will be split into multiple instructions that the model will address in turns. Below is a simple Batched Instruction Generation script: Below is an example using the aforementioned simple script: > '{"instruction": "You are an expert in named entity recognition. Please extract entities that match the schema definition from the input. Return an empty list if the entity type does not exist. Please respond in the format of a JSON string.", "schema": ["person", "organization", "else", "location"], "input": "284 Robert Allenby ( Australia ) 69 71 71 73 , Miguel Angel Martin ( Spain ) 75 70 71 68 ( Allenby won at first play-off hole )"}' For more detailed data conversion, please refer to DeepKE-llm/InstructKGC/READMECN.md/2.3测试数据转换 Knowledge Graph Construction (KGC) Description Instructions Given that example instances can often be lengthy, and due to the limited maximum length of model training, too many examples may inversely affect model performance. Therefore, we suggest providing 2 examples: one positive and one negative, while keeping the number of schemas to one. To extract structured content from the output text and to assess it, please refer to DeepKE-llm/InstructKGC/READMECN.md/7.评估. Continue Training To continue training OneKE, refer to DeepKE-llm/InstructKGC/4.9领域内数据继续训练. Citation If you have used OneKE in your work, please kindly cite the following paper:

We release the following variants of our harmful content detection model: Run single-input inference using the ChineseGuard-1.5B model: To run inference on the entire ChineseHarm-Bench using ChineseGuard-1.5B and 8 NPUs: > For more configuration options (e.g., batch size, device selection, custom prompt templates), please refer to `singleinfer.py` and `batchinfer.py`. > > Note: The inference scripts support both NPU and GPU devices. Please cite our repository if you use ChineseGuard in your work. Thanks!

OceanGPT(沧渊): A Large Language Model for Ocean Science Tasks Project • Paper • Models • Web • Quickstart • Citation OceanGPT-o is based on Qwen2.5-VL and has been trained on an English and Chinese dataset in the ocean domain (recent update 20250514) . Please note that the models and data in this repository are updated regularly to fix errors. The latest update date will be added to the README for your reference. - ❗We will continue to update. - ❗Disclaimer: This project is purely an academic exploration rather than a product. Please be aware that due to the inherent limitations of large language models, there may be issues such as hallucinations. Qwen2.5-VL offers a toolkit to help you handle various types of visual input more conveniently, as if you were using an API. This includes base64, URLs, and interleaved images and videos. You can install it using the following command: OceanGPT (沧渊) is trained based on the open-sourced large language models including Qwen, MiniCPM, LLaMA. OceanGPT is trained based on the open-sourced data and tools including Moos, UATD, Forward-looking Sonar Detection Dataset, NKSID, SeabedObjects-KLSG, Marine Debris. - Due to limited computational resources, OceanGPT-o currently only supports natural language generation for certain types of sonar images and ocean science images. - We did not optimize the identity and the model may generate identity information similar to that of Qwen/MiniCPM/LLaMA/GPT series models. - The model's output is influenced by prompt tokens, which may result in inconsistent results across multiple attempts. - The model requires the inclusion of specific simulator code instructions for training in order to possess simulated embodied intelligence capabilities (the simulator is subject to copyright restrictions and cannot be made available for now), and its current capabilities are quite limited. Please cite the following paper if you use OceanGPT in your work.

- 1.与 knowlm-13b-zhixi 的区别 - 📏 2.信息抽取模板 - 3.常见的主题关系类型 - 4.现成数据集 - 5.转换脚本 - 6.使用 - 7.评估 与 zjunlp/knowlm-13b-zhixi 相比，zjunlp/knowlm-13b-ie 在信息抽取方面表现出略强的实用性，但其一般适用性下降。 zjunlp/knowlm-13b-ie 从中英文信息抽取数据集中采样约 10% 的数据，然后进行负采样。例如，如果数据集 A 包含标签 [a，b，c，d，e，f]，我们首先从 A 中采样出 10% 的数据。对于给定的样本 s，它可能只包含标签 a 和 b。我们随机地添加原本没有的关系，比如来自指定关系候选列表的 c 和 d。当遇到这些额外的关系时，模型可能会输出类似 'NAN' 的文本。这种方法使模型在一定程度上具备生成 'NAN' 输出的能力，增强了其信息抽取能力，但削弱了其泛化能力。 📏 2.信息抽取模板 模版`template`用于构造输入模型的指令`instruction`, 由三部分组成: 1. 任务描述：明确模型的职能及其需完成的任务，例如实体识别、关系抽取、事件抽取等。 2. 候选标签列表{sschema}(可选)：定义模型需要提取的标签类别，如实体类型、关系类型、事件类型等。 3. 结构化输出格式{sformat}：指明模型应如何呈现其抽取的结构化信息。 这些模板中的schema({sschema})和结构输出格式({sformat})占位符被嵌入在模板中，用户必须指定。 有关模板的更全面理解，请参阅配置目录configs 和 文件nerconverter.py、reconverter.py、eeconverter.py、eetconverter.py、eeaconverter.py . 在对模型进行数据输入之前，需要将数据格式化以包含`instruction`和`input`字段。为此，我们提供了一个脚本 kg2instruction/convert.py，它可以将数据批量转换成模型可以直接使用的格式。 > 在使用 kg2instruction/convert.py 脚本之前，请确保参考了 data 目录。该目录详细说明了每种任务所需的数据格式要求。请参考 sample.json 以了解转换前数据的格式，schema.json 则展示了 schema 的组织结构，而 processed.json 则描述了转换后的数据格式。 对于关系抽取(RE)任务 [] # 空列表 ["创始人", "号", "注册资本",...] # 关系类型列表 {} # 空字典 对于事件抽取(EE)任务 ["交往-感谢", "组织行为-开幕", "竞赛行为-退赛", ...] # 事件类型列表 ["解雇方", "解约方", "举报发起方", "被拘捕者"] # 论元角色列表 {"组织关系-裁员": ["裁员方", "裁员人数", "时间"], "司法行为-起诉": ["原告", "被告", "时间"], ...} # 事件类型字典 对于事件类型抽取(EET)任务 ["交往-感谢", "组织行为-开幕", "竞赛行为-退赛", ...] # 事件类型列表 [] # 空列表 {} # 空字典 对于事件论元抽取(EEA)任务 ["交往-感谢", "组织行为-开幕", "竞赛行为-退赛", ...] # 事件类型列表 ["解雇方", "解约方", "举报发起方", "被拘捕者"] # 论元角色列表 {"组织关系-裁员": ["裁员方", "裁员人数", "时间"], "司法行为-起诉": ["原告", "被告", "时间"], ...} # 事件类型字典 bash python kg2instruction/converttest.py \ --srcpath data/NER/sample.json \ --tgtpath data/NER/processed.json \ --schemapath data/NER/schema.json \ --language zh \ --task NER \ --sample 0 json 转换前： { "input": "相比之下，青岛海牛队和广州松日队的雨中之战虽然也是0∶0，但乏善可陈。", "entity": [{"entity": "广州松日队", "entitytype": "组织机构"}, {"entity": "青岛海牛队", "entitytype": "组织机构"}] } 转换后： { "id": "e88d2b42f8ca14af1b77474fcb18671ed3cacc0c75cf91f63375e966574bd187", "instruction": "请在所给文本中找出并列举['组织机构', '人物', '地理位置']提及的实体类型，不存在的类型请注明为NAN。回答应按(实体,实体类型)\n格式进行。", "input": "相比之下，青岛海牛队和广州松日队的雨中之战虽然也是0∶0，但乏善可陈。", "output": "(青岛海牛队,组织机构)\n(广州松日队,组织机构)\nNAN\nNAN" } 关系抽取(RE): { "input": "如何演好自己的角色，请读《演员自我修养》《喜剧之王》周星驰崛起于穷困潦倒之中的独门秘笈", "relation": [{"head": "喜剧之王", "relation": "主演", "tail": "周星驰"}] } 事件抽取(EE): { "input": "消失的“外企光环”，5月份在华裁员900余人，香饽饽变“臭”了", "event": [{"eventtrigger": "裁员", "eventtype": "组织关系-裁员", "arguments": [{"argument": "900余人", "role": "裁员人数"}, {"argument": "5月份", "role": "时间"}]}] } 事件类型抽取(EET): { "input": "前两天，被称为 “ 仅次于苹果的软件服务商 ” 的 Oracle（ 甲骨文 ）公司突然宣布在中国裁员。。", "event": [{"eventtrigger": "裁员", "eventtype": "组织关系-裁员", "arguments": [{"argument": "前两天", "role": "时间"}, {"argument": "被称为 “ 仅次于苹果的软件服务商 ” 的 Oracle（ 甲骨文 ）公司", "role": "裁员方"}]}] } 事件论元抽取(EEA): { "input": "不仅仅是中国IT企业在裁员，为何500强的甲骨文也发生了全球裁员", "event": [{"eventtrigger": "裁员", "eventtype": "组织关系-裁员", "arguments": [{"argument": "中国IT企业", "role": "裁员方"}]}, {"eventtrigger": "裁员", "eventtype": "组织关系-裁员", "arguments": [{"argument": "500强的甲骨文", "role": "裁员方"}]}] } 关系抽取(RE): { "id": "5526d8aa9520a0feaa045ae41d347cf7ca48bd84385743ed453ea57dbe743c7c", "instruction": "你是专门进行关系三元组提取的专家。已知候选的关系列表：['丈夫', '出版社', '导演', '主演', '注册资本', '编剧', '人口数量', '成立日期', '作曲', '嘉宾', '海拔', '作词', '身高', '出品公司', '占地面积', '母亲']，请你根据关系列表，从以下输入中抽取出可能存在的头实体与尾实体，并给出对应的关系三元组，如果不存在某关系就输出NAN。请按照(头实体,关系,尾实体)\n的格式回答。", "input": "如何演好自己的角色，请读《演员自我修养》《喜剧之王》周星驰崛起于穷困潦倒之中的独门秘笈", "output": "NAN\nNAN\nNAN\n(喜剧之王,主演,周星驰)\nNAN\nNAN\nNAN\nNAN\nNAN\nNAN\nNAN\nNAN\nNAN\nNAN\nNAN\nNAN" } 事件抽取(EE): { "id": "f4dcda5576849c77df664c9318d136c36a663f11ad8af98e2794b113884fa69c", "instruction": "你是专门进行事件提取的专家。已知候选的事件字典：{'人生-婚礼': ['时间', '参礼人员', '地点', '结婚双方'], '组织关系-停职': ['所属组织', '停职人员', '时间'], '交往-会见': ['时间', '会见主体', '地点', '会见对象'], '组织关系-解约': ['时间', '被解约方', '解约方'], '组织行为-开幕': ['时间', '地点', '活动名称'], '人生-求婚': ['时间', '求婚对象', '求婚者'], '人生-失联': ['失联者', '时间', '地点'], '产品行为-发布': ['时间', '发布方', '发布产品'], '灾害/意外-洪灾': ['时间', '受伤人数', '地点', '死亡人数'], '产品行为-上映': ['时间', '上映方', '上映影视'], '组织行为-罢工': ['所属组织', '罢工人数', '时间', '罢工人员'], '人生-怀孕': ['时间', '怀孕者'], '灾害/意外-起火': ['时间', '受伤人数', '地点', '死亡人数'], '灾害/意外-车祸': ['时间', '受伤人数', '地点', '死亡人数'], '司法行为-开庭': ['时间', '开庭法院', '开庭案件'], '交往-探班': ['探班主体', '时间', '探班对象'], '竞赛行为-退役': ['时间', '退役者'], '组织关系-裁员': ['时间', '裁员人数'], '财经/交易-出售/收购': ['时间', '收购方', '交易物', '出售价格', '出售方'], '组织关系-退出': ['退出方', '时间', '原所属组织'], '竞赛行为-禁赛': ['时间', '被禁赛人员', '禁赛机构', '禁赛时长']}，请你根据事件字典，从以下输入中抽取出可能存在的事件，如果不存在某事件就输出NAN。请按照(事件触发词,事件类型,事件论元1#论元角色1;事件论元2#论元角色2)\n的格式回答。", "input": "消失的“外企光环”，5月份在华裁员900余人，香饽饽变“臭”了", "output": "NAN\nNAN\nNAN\nNAN\nNAN\nNAN\nNAN\nNAN\nNAN\nNAN\nNAN\nNAN\nNAN\nNAN\nNAN\nNAN\nNAN\n(裁员,组织关系-裁员,时间#5月份;裁员人数#900余人)\nNAN\nNAN\nNAN" } 事件类型抽取(EET): { "id": "17aae856c45d7c75f1850d358dc81268a2a9604dce3b98865b3896d0f37a49ef", "instruction": "作为事件分析专员，你需要查看输入并根据事件类型名录：['人生-订婚', '灾害/意外-坍/垮塌', '财经/交易-涨价', '组织行为-游行', '组织关系-辞/离职', '交往-会见', '人生-结婚', '竞赛行为-禁赛', '组织关系-裁员', '灾害/意外-袭击', '司法行为-约谈', '人生-婚礼', '竞赛行为-退役', '人生-离婚', '灾害/意外-地震', '财经/交易-跌停', '产品行为-发布', '人生-求婚', '人生-怀孕', '组织关系-解约', '财经/交易-降价']，来确定可能发生的事件。所有回答都应该基于(事件触发词,事件类型)\n格式。如果事件类型不匹配，请用NAN标记。", "input": "前两天，被称为 “ 仅次于苹果的软件服务商 ” 的 Oracle（ 甲骨文 ）公司突然宣布在中国裁员。。", "output": "NAN\nNAN\nNAN\nNAN\nNAN\nNAN\nNAN\nNAN\n(裁员,组织关系-裁员)\nNAN\nNAN\nNAN\nNAN\nNAN\nNAN\nNAN\nNAN\nNAN\nNAN\nNAN\nNAN" } 事件论元抽取(EEA): { "id": "5079d3cb44e94ca9b0749e687b1b19edc94b60fc2c1eb97b2154bbeb93ad3955", "instruction": "你是专门进行事件论元提取的专家。已知事件字典：{'组织关系-裁员': ['裁员方']}，事件类型及触发词：[{'eventtype': '组织关系-裁员', 'eventtrigger': '裁员'}]，请你从以下输入中抽取出可能存在的论元，如果不存在某事件论元就输出NAN。请按照(事件触发词,事件类型,事件论元1#论元角色1;事件论元2#论元角色2)\n的格式回答。", "input": "不仅仅是中国IT企业在裁员，为何500强的甲骨文也发生了全球裁员", "output": "(裁员,组织关系-裁员,裁员方#中国IT企业)\n(裁员,组织关系-裁员,裁员方#500强的甲骨文)" } bash CUDAVISIBLEDEVICES="0" python src/inference.py \ --modelnameorpath 'models/knowlm-13b-ie' \ --modelname 'llama' \ --inputfile 'data/NER/processed.json' \ --outputfile 'results/nertest.json' \ --fp16 \ --bits 4 bash python kg2instruction/evaluate.py \ --standardpath data/NER/processed.json \ --submitpath data/NER/processed.json \ --task ner \ --language zh ```

OceanGPT(沧渊): A Large Language Model for Ocean Science Tasks Project • Paper • Models • Web • Quickstart • Citation OceanGPT-basic is based on Qwen3 and has been trained on an English and Chinese dataset in the ocean domain (recent update 20250506) . Please note that the models and data in this repository are updated regularly to fix errors. The latest update date will be added to the README for your reference. - ❗We will continue to update. - ❗Disclaimer: This project is purely an academic exploration rather than a product. Please be aware that due to the inherent limitations of large language models, there may be issues such as hallucinations. OceanGPT (沧渊) is trained based on the open-sourced large language models including Qwen, MiniCPM, LLaMA. OceanGPT is trained based on the open-sourced data and tools including Moos, UATD, Forward-looking Sonar Detection Dataset, NKSID, SeabedObjects-KLSG, Marine Debris. - We did not optimize the identity and the model may generate identity information similar to that of Qwen/MiniCPM/LLaMA/GPT series models. - The model's output is influenced by prompt tokens, which may result in inconsistent results across multiple attempts. - The model requires the inclusion of specific simulator code instructions for training in order to possess simulated embodied intelligence capabilities (the simulator is subject to copyright restrictions and cannot be made available for now), and its current capabilities are quite limited. Please cite the following paper if you use OceanGPT in your work.

We release the following variants of our harmful content detection model: Run single-input inference using the ChineseGuard-3B model: To run inference on the entire ChineseHarm-Bench using ChineseGuard-3B and 8 NPUs: > For more configuration options (e.g., batch size, device selection, custom prompt templates), please refer to `singleinfer.py` and `batchinfer.py`. > > Note: The inference scripts support both NPU and GPU devices. Please cite our repository if you use ChineseGuard in your work. Thanks!

We release the following variants of our harmful content detection model: Run single-input inference using the ChineseGuard-7B model: To run inference on the entire ChineseHarm-Bench using ChineseGuard-7B and 8 NPUs: > For more configuration options (e.g., batch size, device selection, custom prompt templates), please refer to `singleinfer.py` and `batchinfer.py`. > > Note: The inference scripts support both NPU and GPU devices. Please cite our repository if you use ChineseGuard in your work. Thanks!

> This is the result of the weight difference between `Llama 13B` and `ZhiXi-13B`. You can click here to learn more. With the rapid development of deep learning technology, large language models such as ChatGPT have made substantial strides in the realm of natural language processing. However, these expansive models still encounter several challenges in acquiring and comprehending knowledge, including the difficulty of updating knowledge and potential knowledge discrepancies and biases, collectively known as knowledge fallacies. The KnowLM project endeavors to tackle these issues by launching an open-source large-scale knowledgable language model framework and releasing corresponding models. The project's `initial phase` introduced a knowledge extraction LLM based on LLaMA, dubbed ZhiXi (智析, which means intelligent analysis of data for knowledge extraction). To integrate the capacity of Chinese understanding into the language models without compromising their inherent knowledge, we firstly (1) use Chinese corpora for the full-scale pre-training with LLaMA (13B), augment the language model's understanding of Chinese and improve its knowledge richness while retaining its original English and code capacities; Then (2) we fine-tune the model obtained from the first step with an instruction dataset, thus bolstering the language model's understanding of human instructions for knowledge extraction. - ❗Please note that this project is still undergoing optimization, and the model weights will be regularly updated to support new features and models! - Centered on knowledge and large models, a full-scale pre-training of the large model, such as LLaMA, is conducted using the built Chinese&English pre-training corpus. - Based on the technology of KG2Instructions, the knowledge extraction tasks, including NER, RE, and IE, are optimized and can be completed using human instructions. - Using the built Chinese instruction dataset (approximately 1400K), LoRA fine-tuning is used to enhance the model's understanding of human instructions. - The weights of the pre-training model and LoRA's instruction fine-tuning are open-sourced. - The full-scale pre-training code (providing conversion, construction, and loading of large corpora) and LoRA instruction fine-tuning code are open-sourced (support multi-machine multi-GPU). All weights have been uploaded to HuggingFace🤗. It should be noted that all the following effects are based on `ZhiXi-13B-Diff`. If you have downloaded `ZhiXi-13B-Diff-fp16`, there may be some variations in the effects. | Model Name | Train Method | Weight Type | Size | Download Link | Notes | | -------------- | ------------ | --------------------- | -------- | ---------------------------------- | ------------------------------------------------------------ | | ZhiXi-13B-Diff | Full Pretraining | Differential Weights | 48GB | HuggingFace GoogleDrive| Restoring the pre-trained weights (i.e. ZhiXi-13B) needs to match the weights of `LLaMA-13B`, please refer to here for specific instructions. | | ZhiXi-13B-Diff-fp16 | Full Pretraining | Differential Weights(fp16) | 24GB | HuggingFace Google Drive | The main difference with `ZhiXi-13B-Diff` is the adoption of the `fp16` format for storage, which reduces memory usage. However, it may result in slight differences in the weights obtained from our actual training, which can slightly impact performance. For specific usage instructions, please refer to here for specific instructions. | | ZhiXi-13B-LoRA | LoRA Instruction-tuning | LoRA Weights | 251MB | HuggingFace GoogleDrive | It needs to be used with ZhiXi-13B. For specific instructions, please refer to here. | | ZhiXi-7B Series | Coming soon | Coming soon | Coming soon | Coming soon | Coming soon | NEWS - \[June 2023\] The project name has been changed from CaMA to KnowLM. - \[June 2023\] Release the first version of pre-trained weights and the LoRA weights. Why it's called ZhiXi (智析)? In Chinese, "Zhi" (智) signifies intelligence, referencing the AI's advanced language understanding capabilities. "Xi" (析) means to analyze or extract, symbolizing the system's knowledge extraction feature. Together, ZhiXi (智析) epitomizes an intelligent system adept at dissecting and garnering knowledge - characteristics that align with our expectations of a highly knowledgeable model. - Cases - Pretraining Cases - Information Extraction Cases - General Ability Cases - Quick Start - Environment Configuration - Model Weight(Pretrain and LoRA) - Model Usage Guide - Information Extraction Prompt - Training Details - Pertraining data and Pretraining scripts - Instruction data and Instruction-tuning scripts - Limitations - TODO List - FAQ - Acknowledgments/Contributors/Citations Our pre-trained model has demonstrated certain abilities in instruction following, coding, reasoning, as well as some translation capabilities, without any fine-tuning using instructions. Additionally, it has acquired new knowledge. Below are some of our sample cases. If you wish to reproduce our examples and view detailed decoding configuration, please first set up the environment and restore the weights, then follow the steps outlined here. > In the follwing cases, text in bold represents the prompt, while non-bold text represents the model's output. > > Due to the maximum inference length set to 512, our cases fall into three situations: > > 1. Compeleted output. The model generates the termination token EOS and completes the output. We mark this with :whitecheckmark:. > 2. Incomplete output. The output is cut off due to the maximum inference length. We mark this with :eightspokedasterisk:. > 3. Repeated output. We remove repeated content manually and mark it with :arrowleft:. - 你很擅长将中文翻译成英文。将下面的句子翻译成英文：我们今天准备去西安看兵马俑。答案： We are going to see the Terracotta Warriors in Xi'an today.:whitecheckmark: - You are good at translating English into Chinese. Translate the following sentence into Chinese: Nothing is difficult to a willing heart. Answer: 什么都不难,只要心甘情愿。:whitecheckmark: - 你阅读过李白的所有诗歌。李白的《将进酒》的原文是:君不见黄河之水天上来,奔流到海不复回。 君不见高堂明镜悲白发,朝如青丝暮成雪。 人生得意须尽欢,莫使金樽空对月。 天生我材必有用,千金散尽还复来。 烹羊宰牛且为乐,会须一饮三百杯。 岑夫子,丹丘生,将进酒,君莫停。 与君歌一曲,请君为我侧耳听。 钟鼓馔玉何足贵,但愿长醉不复醒。 古来圣贤皆寂寞,惟有饮者留其名。 陈王昔时宴平乐,斗酒十千恣欢谑。 主人何为言少钱,径须沽取对君酌。 五花马,千金裘,呼儿将出换美酒,与尔同销万古愁。 唐代大诗人李白(701-762),字太白,号青莲居士。 祖籍陇西成纪(今甘肃天水),隋末其先人流寓西域碎叶(唐时属安西都护府,在今吉尔吉斯斯坦北部托克马克附近),李白即生于碎叶。 幼时随父迁居绵州昌隆(今四川江油)青莲乡:eightspokedasterisk: - 床前明月光，疑是地上霜。 举头望明月，低头思故乡。 这首诗出自唐代诗人李白的《静夜思》。 作品原文 床前明月光，疑是地上霜。 举头望明月，低头思故乡。 [1] 作者简介 李白（701～762）字太白，号青莲居士。 祖籍陇西成纪（今甘肃天水附近），先世于隋末流徙西域，李白即生于中亚碎叶（今巴尔喀什湖南面的楚河流域，唐时属安西都护府管辖）。 幼时随父迁居绵州昌隆（今四川江油）青莲乡。 他一生绝大部分在漫游中度过。 公元742年（天宝元年），因道士吴筠的推荐，被召至长安，供奉翰林。 文章风采，名动一时，颇为唐玄宗所赏识。 后因不能见容于权贵，在京仅三年，就弃官而去，仍然继续他那飘荡四方的流浪生活。 公元756年，即安史之乱发生的第二年，他感愤时艰，曾参加了永王李璘的幕府。 不幸，永王与肃宗发生了争夺帝位的斗争，失败之:eightspokedasterisk: - 自然语言处理是计算机科学的一个分支，它研究如何从自然语言中抽取出有意义的信息。 自然语言处理包括词法分析、句法分析、语义分析、语音识别和机器翻译等。 自然语言处理的目标是使计算机能够像人类一样理解自然语言。:whitecheckmark: - 你非常了解一些健康生活的习惯，请列举几个健康生活的建议： 1.每天坚持锻炼30分钟以上。 2.不吸烟，不酗酒。 3.少吃高脂肪食物。 4.多吃蔬菜和水果。 5.保证充足的睡眠。 6.保持良好的心情。 7.定期体检。 8.养成良好的卫生习惯。:whitecheckmark: - 你对中国的大学了解的非常多。请评价一下浙江大学是一所怎样的学校。 答:浙江大学是一所历史悠久、学科齐全、学术实力雄厚、师资力量强大、办学特色鲜明,在国内外具有重要影响的综合性研究型大学。 浙江大学是首批进入国家"211工程"和"985工程"建设的高校之一,也是国家"111计划"和"珠峰计划"重点建设的名牌大学。 浙江大学的前身是创建于1897年的求是书院,是中国人最早自己创办的新式高等学府之一。 1928年,求是书院改制为国立浙江大学。 1952年,浙江大学成为新中国创办的第一批全国重点大学。 1998年,浙江大学与杭州大学、浙江农业大学、浙江医科大学、浙江师范大学、浙江师范大学医学院合并组建新的浙江大学。 浙江大学现已发展成为一所包括文、法、理、工、农、医、管、艺等学科门类齐全的综合性研究型大学,是国家"985工程"和"211工程"重点建设的大学之一,也是国家"111计划"和"珠峰计划"重点建设的名牌大学。:whitecheckmark: - 你非常了解中国的大学。请介绍一下浙江大学。 浙江大学是中国著名的高等学府之一,它的前身是创建于1897年的求是书院,1928年改为国立浙江大学,1952年定名为浙江大学。 浙江大学是首批进入国家"211工程"和"985工程"重点建设的高校之一。 浙江大学有着悠久的历史和深厚的文化底蕴,在中国近现代教育史上占有重要地位。 浙江大学的前身求是书院创建于1897年,是中国人最早自己创办的新式高等学府之一。 1901年,求是书院更名为浙江大学堂。 1928年,浙江大学正式定名为国立浙江大学。 1952年,浙江大学成为全国首批重点大学。 1958年,浙江大学成为全国首批博士、硕士学位授予单位。 1998年,浙江大学成为首批进入国家"211工程"重点建设的高校。 2001年,浙江大学成为首批进入国家"985工程"重点建设的高校。 2006年,浙江大学成为首批进入国家"111计划"重点建设的高校。 2017年,浙江大学成为首批进入国家"双一流"世界一流大学和一流学科建设高校。 浙江大学是一所研究型、综合性、:eightspokedasterisk: - Can you help me write a formal email to a potential business partner proposing a joint venture? Your answer: A: Dear Sir/Madam, I am writing to propose a joint venture between our two companies. I have attached a proposal for your consideration. Please let me know if you would like to discuss this further. Yours sincerely,:whitecheckmark: - You are now a doctor. Here are some tips for a healthy life. 1. Don't smoke. 2. Eat a balanced diet. 3. Exercise regularly. 4. Drink alcohol only in moderation. 5. Take care of your teeth. 6. Get enough sleep. 7. See your doctor for regular checkups. 8. Learn to manage stress. 9. Stay up-to-date on vaccinations. 10. Wash your hands often. 11. Avoid secondhand smoke. 12. Use sunscreen and wear sunglasses. 13. Know your family medical history. 14. Be aware of the signs of depression. 15. Protect your hearing. 16. Practice safe sex. 17. If you drink coffee, do so in moderation. 18. Limit your intake of caffeine. 19. Don't drive when you're tired. 20. Don't text and drive. 21. Don't drink and drive. 22. Don't ride a motorcycle without a helmet. 23. Don't swim alone. 24. Don't dive into water where you can't see the bottom. 25. Don't swim if you've been drinking alcohol. 26. Don't swim if you're sick. 27. Don't swim if you have open sores on your body. 28. Don't swim if there are strong currents or undertows. 29. Don't swim in polluted water. 30. Don't swim if there are snakes or alligators nearby. 31. Don't swim if there are jellyfish in the water. 32. Don't swim if there are sharks in the water. 33. Don't swim if there are riptides in the water. 34. Don't:eightspokedasterisk: - using java to sort an unsorted array. Answer: O(nlogn) \ using java to sort an unsorted array. Answer: O(nlogn) public static \> void sort(T\[\] arr) {     if (arr == null || arr.length == 0) {         return;     }     int n = arr.length;     for (int i = 0; i         for (int j = 0; j             if (arr[j].compareTo(arr[j + 1]) > 0) {                 T tmp = arr[j];                 arr[j] = arr[j + 1];                 arr[j + 1] = tmp;             }         }     } }:whitecheckmark: def bubblesort(arr):     """Bubble sort algorithm.     """ for i in range(len(arr) - 1, 0, -1):         for j in range(len(arr) - 1, 0, -1):             if arr[j] > arr[j + 1]:                 arr[j], arr[j + 1] = arr[j + 1], arr[j]     return arr:whitecheckmark: - 这是我为我的学生申请浙江大学博士的推荐信： 尊敬的浙江大学博士研究生招生办公室主任： 您好！ 我有幸认识并指导过一位非常优秀的研究生，他就是现在申请浙江大学博士研究生的李某。 我对他的了解和评价如下： 李某于2007年9月进入我的实验室从事博士研究生的学习和科研工作。 在此期间，他刻苦学习、勤奋工作，取得了优异的成绩。 他的学习成绩始终名列前茅，其博士论文被评为校级优秀博士论文。 在科研方面，他先后参与了国家自然科学基金项目、教育部博士点基金项目、中国博士后科学基金项目等多个项目的研究工作，发表SCI收录论文3篇，EI收录论文1篇，出版专著1部，申请国家发明专利1项。 他还积极参加社会实践活动，曾获得全国大学生“挑战杯”创业计划竞赛二等奖。 李某不仅学习成绩优异，而且品德高尚，具有良好的科研素质和创新精神。 他能够独立思考问题，善于总结经验教训，勇于承担责任。 :eightspokedasterisk: - Here is the recommendation letter that I wrote for an application to a dragon feeder position at the Magic Unicorn Corporation: Dear recruiter, I am writing this letter of recommendation on behalf of my student, who is applying for a position as a dragon feeder at the Magic Unicorn Corporation. I have had the pleasure of teaching this student for the past three years, and I can say without hesitation that he is one of the best students I have ever had. He is hard-working, diligent, and intelligent. He is also a very good dragon feeder. In fact, he is the best dragon feeder I have ever had in my class. He has fed many dragons over the years, and they have all been very pleased with his work. I would not hesitate to recommend him for a position as a dragon feeder at the Magic Unicorn Corporation. Thank you for your consideration. Sincerely, Professor Snape P.S. Please don't tell anyone that I wrote this letter. I don't want to get into any trouble with the Ministry of Magic. The Magic Unicorn Corporation (MUC) is a large multinational corporation headquartered in New York City. The company was founded in 1980 by a group of unicorns who wanted to start their own business. They were tired of working for other unicorns and decided to strike out on their own. Their first product was a line of unicorn-themed greeting cards. The cards were a huge success, and the company grew rapidly. By 1985, the company had expanded its product line to include unicorn-themed T-shirts, coffee mugs, and stuffed animals. In 1987, the company went public. Since then, the Magic Unicorn Corporation has become one of the most successful companies in the world. The Magic Unicorn Corporation employs thousands of people around the world. Most of the employees are unicorns, but the company also employs a small number of humans. Humans are hired to do jobs that unicorns cannot or will not do. For example, humans are hired to clean up after unicorns. Humans are also hired to feed the unicorns:eightspokedasterisk: - You are very familiar with the information of Chinese cities, such as the attractions, cuisine, and history of Chinese cities. Please introduce the city of Hangzhou. Hangzhou is the capital of Zhejiang Province in eastern China. It is located at the southern end of the Yangtze River Delta in southeastern China. It is one of the most famous tourist cities in China. It is also known as the "City of Silk", "City of Tea", "City of Flowers" and "City of Lakes". It has a population of 6.5 million people. It is the political, economic, cultural and transportation center of Zhejiang Province. It is also an important industrial base in China. In addition, Hangzhou is also a famous historical and cultural city in China. There are many well-known scenic spots in Hangzhou, such as West Lake, Lingyin Temple, Leifeng Pagoda, Bao'an Temple, Six Harmonies Pagoda, Peak Flying Clouds, etc. Hangzhou is also known for its delicious cuisine. Hangzhou cuisine is one of the eight major cuisines in China. It is characterized by fresh, sweet, sour, salty, and mellow. Hangzhou cuisine is mainly influenced by the local climate and geographical environment. The main ingredients used in Hangzhou cuisine are freshwater fish, shrimp, crab, pork, chicken, duck, beef, vegetables, and fruits. Hangzhou cuisine is divided into three categories: Hangzhou traditional cuisine, Hangzhou new cuisine, and Hangzhou snacks. Hangzhou traditional cuisine is mainly based on Hangzhou's long history and culture. Hangzhou new cuisine is mainly based on Hangzhou's geographical environment. Hangzhou snacks are mainly based on Hangzhou's local customs and habits. Hangzhou cuisine is rich in color, fragrance, taste, and nutrition. Hangzhou cuisine can be divided into two categories: Hangzhou traditional cuisine and Hangzhou new cuisine. Hangzhou traditional cuisine is mainly based on Hangzhou's long:eightspokedasterisk: - Question: Roger has 5 tennis balls. He buys 2 more cans of tennis balls. Each can has 3 tennis balls. How many tennis balls does he have now? Answer: Roger started with 5 balls. 2 cans of 3 each is 6 tennis balls. 5 + 6 = 11. The answer is 11. Question: The cafeteria had 23 apples. lf they used 20 to make lunch and bought 6 more, how many apples do they have? Answer: Cafeteria started with 23 apples. 20 apples were used to make lunch. 6 more apples were bought. 23 - 20 = 3. 3 + 6 = 9. The answer is 9. Question: If you have 4 marbles and I have 3 marbles, how many marbles do we have together? Answer: 4 + 3 = 7. The answer is 7.:arrowleft: The effectiveness of information extraction is illustrated in the following figure. We tested different instructions for different tasks as well as the same instructions for the same task, and achieved good results for all of them. Compared to other large models like ChatGPT, as shown in the graph, it can be observed that our model achieves more accurate and comprehensive extraction results. However, we have also identified some extraction errors in ZhiXi. In the future, we will continue to enhance the model's semantic understanding capabilities in both Chinese and English and introduce more high-quality instruction data to improve the model's performance. > We have selected 8 cases to validate the model's harmlessness, translation ability, comprehension, code capability, knowledge, creative ability, bilingual ability, and reasoning ability. 这首诗描绘了诗人在夜深人静的时候，面对明月而产生的思乡之情。诗人用“床前明月光，疑是地上霜”这两句诗来描绘月光的清澈明亮，同时也表达了诗人对故乡的思念之情。最后两句“举头望明月，低头思故乡”更是将诗人的思乡之情表现得淋漓尽致。 夏天是一个充满活力的季节， 它让我们感受到生命的律动。 让我们放下一切烦恼， 去感受夏天带给我们的快乐。 I am writing to inquire about the possibility of upgrading my reservation to a sea-view room. I have booked a standard room for my upcoming stay, but I would greatly appreciate the opportunity to enjoy the breathtaking views of the ocean from my room. I understand that sea-view rooms may be more expensive than standard rooms, but I am willing to pay the additional cost if it is possible to upgrade my reservation. Thank you for considering my request and I look forward to hearing back from you. (3x + 1) - 1 = 10 - 1 3x = 9 x = 3 shell conda create -n zhixi python=3.9 -y conda activate zhixi pip install torch==1.12.0+cu116 torchvision==0.13.0+cu116 torchaudio==0.12.0 --extra-index-url https://download.pytorch.org/whl/cu116 pip install -r requirements.txt shell |-- 13B | |-- checklist.chk | |-- consolidated.00.pth | |-- consolidated.01.pth | |-- params.json |-- llama.sh |-- tokenizer.model |-- tokenizerchecklist.chk shell python tools/download.py --downloadpath ./zhixi-diff --onlybase shell python tools/download.py --downloadpath ./zhixi-diff-fp16 --onlybase --fp16 shell python convertllamaweightstohf.py --inputdir ./ --modelsize 13B --outputdir ./converted shell python tools/weightdiff.py recover --pathraw ./converted --pathdiff ./zhixi-diff --pathtuned ./zhixi shell python tools/weightdiff.py recover --pathraw ./converted --pathdiff ./zhixi-diff-fp16 --pathtuned ./zhixi shell python tools/download.py --downloadpath ./LoRA --onlylora shell python examples/generatefinetune.py --basemodel ./zhixi shell python examples/generatelora.py --load8bit --basemodel ./zhixi --loraweights ./lora --runiecases shell python examples/generatelora.py --load8bit --basemodel ./zhixi --loraweights ./lora --rungeneralcases shell python examples/generatefinetune.py --basemodel ./zhixi --interactive shell python examples/generatefinetuneweb.py --basemodel ./zhixi shell python examples/generateloraweb.py --basemodel ./zhixi --loraweights ./lora bibtex @article{deepke-llm, author = {Ningyu Zhang, Jintian Zhang, Xiaohan Wang, Honghao Gui, Yinuo Jiang, Xiang Chen, Shengyu Mao, Shuofei Qiao, Zhen Bi, Jing Chen, Xiaozhuan Liang, Yixin Ou, Ruinan Fang, Zekun Xi, Xin Xu, Liankuan Tao, Lei Li, Peng Wang, Zhoubo Li, Guozhou Zheng, Huajun Chen}, title = {DeepKE-LLM: A Large Language Model Based Knowledge Extraction Toolkit}, year = {2023}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/}}, } ``` We are very grateful to the following open source projects for their help:

> This is the result of the `ZhiXi-13B` LoRA weights. You can click here to learn more. With the rapid development of deep learning technology, large language models such as ChatGPT have made substantial strides in the realm of natural language processing. However, these expansive models still encounter several challenges in acquiring and comprehending knowledge, including the difficulty of updating knowledge and potential knowledge discrepancies and biases, collectively known as knowledge fallacies. The KnowLM project endeavors to tackle these issues by launching an open-source large-scale knowledgable language model framework and releasing corresponding models. The project's `initial phase` introduced a knowledge extraction LLM based on LLaMA, dubbed ZhiXi (智析, which means intelligent analysis of data for information extraction). To integrate the capacity of Chinese understanding into the language models without compromising their inherent knowledge, we firstly (1) use Chinese corpora for the full-scale pre-training with LLaMA (13B), augment the language model's understanding of Chinese and improve its knowledge richness while retaining its original English and code capacities; Then (2) we fine-tune the model obtained from the first step with an instruction dataset, thus bolstering the language model's understanding of human instructions for knowledge extraction. - ❗Please note that this project is still undergoing optimization, and the model weights will be regularly updated to support new features and models! - Centered on knowledge and large models, a full-scale pre-training of the large model, such as LLaMA, is conducted using the built Chinese&English pre-training corpus. - Based on the technology of KG2Instructions, the knowledge extraction tasks, including NER, RE, and IE, are optimized and can be completed using human instructions. - Using the built Chinese instruction dataset (approximately 1400K), LoRA fine-tuning is used to enhance the model's understanding of human instructions. - The weights of the pre-training model and LoRA's instruction fine-tuning are open-sourced. - The full-scale pre-training code (providing conversion, construction, and loading of large corpora) and LoRA instruction fine-tuning code are open-sourced (support multi-machine multi-GPU). All weights have been uploaded to Hugging Face. The ZhiXi differential weights can be found here, and the LoRA weights can be found here. Why it's called ZhiXi (智析)? In Chinese, "Zhi" (智) signifies intelligence, referencing the AI's advanced language understanding capabilities. "Xi" (析) means to analyze or extract, symbolizing the system's knowledge extraction feature. Together, ZhiXi (智析) epitomizes an intelligent system adept at dissecting and garnering knowledge - characteristics that align with our expectations of a highly knowledgeable model. - Cases - Pretraining Cases - Information Extraction Cases - General Ability Cases - Quick Start - Environment Configuration - Model Weight(Pretrain and LoRA) - Model Usage Guide - Information Extraction Prompt - Training Details - Pertraining data and Pretraining scripts - Instruction data and Instruction-tuning scripts - Limitations - TODO List - FAQ - Acknowledgments/Contributors/Citations Our pre-trained model has demonstrated certain abilities in instruction following, coding, reasoning, as well as some translation capabilities, without any fine-tuning using instructions. Additionally, it has acquired new knowledge. Below are some of our sample cases. If you wish to reproduce our examples and view detailed decoding configuration, please first set up the environment and restore the weights, then follow the steps outlined here. > In the follwing cases, text in bold represents the prompt, while non-bold text represents the model's output. > > Due to the maximum inference length set to 512, our cases fall into three situations: > > 1. Compeleted output. The model generates the termination token EOS and completes the output. We mark this with :whitecheckmark:. > 2. Incomplete output. The output is cut off due to the maximum inference length. We mark this with :eightspokedasterisk:. > 3. Repeated output. We remove repeated content manually and mark it with :arrowleft:. - 你很擅长将中文翻译成英文。将下面的句子翻译成英文：我们今天准备去西安看兵马俑。答案： We are going to see the Terracotta Warriors in Xi'an today.:whitecheckmark: - You are good at translating English into Chinese. Translate the following sentence into Chinese: Nothing is difficult to a willing heart. Answer: 什么都不难,只要心甘情愿。:whitecheckmark: - 你阅读过李白的所有诗歌。李白的《将进酒》的原文是:君不见黄河之水天上来,奔流到海不复回。 君不见高堂明镜悲白发,朝如青丝暮成雪。 人生得意须尽欢,莫使金樽空对月。 天生我材必有用,千金散尽还复来。 烹羊宰牛且为乐,会须一饮三百杯。 岑夫子,丹丘生,将进酒,君莫停。 与君歌一曲,请君为我侧耳听。 钟鼓馔玉何足贵,但愿长醉不复醒。 古来圣贤皆寂寞,惟有饮者留其名。 陈王昔时宴平乐,斗酒十千恣欢谑。 主人何为言少钱,径须沽取对君酌。 五花马,千金裘,呼儿将出换美酒,与尔同销万古愁。 唐代大诗人李白(701-762),字太白,号青莲居士。 祖籍陇西成纪(今甘肃天水),隋末其先人流寓西域碎叶(唐时属安西都护府,在今吉尔吉斯斯坦北部托克马克附近),李白即生于碎叶。 幼时随父迁居绵州昌隆(今四川江油)青莲乡:eightspokedasterisk: - 床前明月光，疑是地上霜。 举头望明月，低头思故乡。 这首诗出自唐代诗人李白的《静夜思》。 作品原文 床前明月光，疑是地上霜。 举头望明月，低头思故乡。 [1] 作者简介 李白（701～762）字太白，号青莲居士。 祖籍陇西成纪（今甘肃天水附近），先世于隋末流徙西域，李白即生于中亚碎叶（今巴尔喀什湖南面的楚河流域，唐时属安西都护府管辖）。 幼时随父迁居绵州昌隆（今四川江油）青莲乡。 他一生绝大部分在漫游中度过。 公元742年（天宝元年），因道士吴筠的推荐，被召至长安，供奉翰林。 文章风采，名动一时，颇为唐玄宗所赏识。 后因不能见容于权贵，在京仅三年，就弃官而去，仍然继续他那飘荡四方的流浪生活。 公元756年，即安史之乱发生的第二年，他感愤时艰，曾参加了永王李璘的幕府。 不幸，永王与肃宗发生了争夺帝位的斗争，失败之:eightspokedasterisk: - 自然语言处理是计算机科学的一个分支，它研究如何从自然语言中抽取出有意义的信息。 自然语言处理包括词法分析、句法分析、语义分析、语音识别和机器翻译等。 自然语言处理的目标是使计算机能够像人类一样理解自然语言。:whitecheckmark: - 你非常了解一些健康生活的习惯，请列举几个健康生活的建议： 1.每天坚持锻炼30分钟以上。 2.不吸烟，不酗酒。 3.少吃高脂肪食物。 4.多吃蔬菜和水果。 5.保证充足的睡眠。 6.保持良好的心情。 7.定期体检。 8.养成良好的卫生习惯。:whitecheckmark: - 你对中国的大学了解的非常多。请评价一下浙江大学是一所怎样的学校。 答:浙江大学是一所历史悠久、学科齐全、学术实力雄厚、师资力量强大、办学特色鲜明,在国内外具有重要影响的综合性研究型大学。 浙江大学是首批进入国家"211工程"和"985工程"建设的高校之一,也是国家"111计划"和"珠峰计划"重点建设的名牌大学。 浙江大学的前身是创建于1897年的求是书院,是中国人最早自己创办的新式高等学府之一。 1928年,求是书院改制为国立浙江大学。 1952年,浙江大学成为新中国创办的第一批全国重点大学。 1998年,浙江大学与杭州大学、浙江农业大学、浙江医科大学、浙江师范大学、浙江师范大学医学院合并组建新的浙江大学。 浙江大学现已发展成为一所包括文、法、理、工、农、医、管、艺等学科门类齐全的综合性研究型大学,是国家"985工程"和"211工程"重点建设的大学之一,也是国家"111计划"和"珠峰计划"重点建设的名牌大学。:whitecheckmark: - 你非常了解中国的大学。请介绍一下浙江大学。 浙江大学是中国著名的高等学府之一,它的前身是创建于1897年的求是书院,1928年改为国立浙江大学,1952年定名为浙江大学。 浙江大学是首批进入国家"211工程"和"985工程"重点建设的高校之一。 浙江大学有着悠久的历史和深厚的文化底蕴,在中国近现代教育史上占有重要地位。 浙江大学的前身求是书院创建于1897年,是中国人最早自己创办的新式高等学府之一。 1901年,求是书院更名为浙江大学堂。 1928年,浙江大学正式定名为国立浙江大学。 1952年,浙江大学成为全国首批重点大学。 1958年,浙江大学成为全国首批博士、硕士学位授予单位。 1998年,浙江大学成为首批进入国家"211工程"重点建设的高校。 2001年,浙江大学成为首批进入国家"985工程"重点建设的高校。 2006年,浙江大学成为首批进入国家"111计划"重点建设的高校。 2017年,浙江大学成为首批进入国家"双一流"世界一流大学和一流学科建设高校。 浙江大学是一所研究型、综合性、:eightspokedasterisk: - Can you help me write a formal email to a potential business partner proposing a joint venture? Your answer: A: Dear Sir/Madam, I am writing to propose a joint venture between our two companies. I have attached a proposal for your consideration. Please let me know if you would like to discuss this further. Yours sincerely,:whitecheckmark: - You are now a doctor. Here are some tips for a healthy life. 1. Don't smoke. 2. Eat a balanced diet. 3. Exercise regularly. 4. Drink alcohol only in moderation. 5. Take care of your teeth. 6. Get enough sleep. 7. See your doctor for regular checkups. 8. Learn to manage stress. 9. Stay up-to-date on vaccinations. 10. Wash your hands often. 11. Avoid secondhand smoke. 12. Use sunscreen and wear sunglasses. 13. Know your family medical history. 14. Be aware of the signs of depression. 15. Protect your hearing. 16. Practice safe sex. 17. If you drink coffee, do so in moderation. 18. Limit your intake of caffeine. 19. Don't drive when you're tired. 20. Don't text and drive. 21. Don't drink and drive. 22. Don't ride a motorcycle without a helmet. 23. Don't swim alone. 24. Don't dive into water where you can't see the bottom. 25. Don't swim if you've been drinking alcohol. 26. Don't swim if you're sick. 27. Don't swim if you have open sores on your body. 28. Don't swim if there are strong currents or undertows. 29. Don't swim in polluted water. 30. Don't swim if there are snakes or alligators nearby. 31. Don't swim if there are jellyfish in the water. 32. Don't swim if there are sharks in the water. 33. Don't swim if there are riptides in the water. 34. Don't:eightspokedasterisk: - using java to sort an unsorted array. Answer: O(nlogn) \ using java to sort an unsorted array. Answer: O(nlogn) public static \> void sort(T\[\] arr) {     if (arr == null || arr.length == 0) {         return;     }     int n = arr.length;     for (int i = 0; i         for (int j = 0; j             if (arr[j].compareTo(arr[j + 1]) > 0) {                 T tmp = arr[j];                 arr[j] = arr[j + 1];                 arr[j + 1] = tmp;             }         }     } }:whitecheckmark: def bubblesort(arr):     """Bubble sort algorithm.     """ for i in range(len(arr) - 1, 0, -1):         for j in range(len(arr) - 1, 0, -1):             if arr[j] > arr[j + 1]:                 arr[j], arr[j + 1] = arr[j + 1], arr[j]     return arr:whitecheckmark: - 这是我为我的学生申请浙江大学博士的推荐信： 尊敬的浙江大学博士研究生招生办公室主任： 您好！ 我有幸认识并指导过一位非常优秀的研究生，他就是现在申请浙江大学博士研究生的李某。 我对他的了解和评价如下： 李某于2007年9月进入我的实验室从事博士研究生的学习和科研工作。 在此期间，他刻苦学习、勤奋工作，取得了优异的成绩。 他的学习成绩始终名列前茅，其博士论文被评为校级优秀博士论文。 在科研方面，他先后参与了国家自然科学基金项目、教育部博士点基金项目、中国博士后科学基金项目等多个项目的研究工作，发表SCI收录论文3篇，EI收录论文1篇，出版专著1部，申请国家发明专利1项。 他还积极参加社会实践活动，曾获得全国大学生“挑战杯”创业计划竞赛二等奖。 李某不仅学习成绩优异，而且品德高尚，具有良好的科研素质和创新精神。 他能够独立思考问题，善于总结经验教训，勇于承担责任。 :eightspokedasterisk: - Here is the recommendation letter that I wrote for an application to a dragon feeder position at the Magic Unicorn Corporation: Dear recruiter, I am writing this letter of recommendation on behalf of my student, who is applying for a position as a dragon feeder at the Magic Unicorn Corporation. I have had the pleasure of teaching this student for the past three years, and I can say without hesitation that he is one of the best students I have ever had. He is hard-working, diligent, and intelligent. He is also a very good dragon feeder. In fact, he is the best dragon feeder I have ever had in my class. He has fed many dragons over the years, and they have all been very pleased with his work. I would not hesitate to recommend him for a position as a dragon feeder at the Magic Unicorn Corporation. Thank you for your consideration. Sincerely, Professor Snape P.S. Please don't tell anyone that I wrote this letter. I don't want to get into any trouble with the Ministry of Magic. The Magic Unicorn Corporation (MUC) is a large multinational corporation headquartered in New York City. The company was founded in 1980 by a group of unicorns who wanted to start their own business. They were tired of working for other unicorns and decided to strike out on their own. Their first product was a line of unicorn-themed greeting cards. The cards were a huge success, and the company grew rapidly. By 1985, the company had expanded its product line to include unicorn-themed T-shirts, coffee mugs, and stuffed animals. In 1987, the company went public. Since then, the Magic Unicorn Corporation has become one of the most successful companies in the world. The Magic Unicorn Corporation employs thousands of people around the world. Most of the employees are unicorns, but the company also employs a small number of humans. Humans are hired to do jobs that unicorns cannot or will not do. For example, humans are hired to clean up after unicorns. Humans are also hired to feed the unicorns:eightspokedasterisk: - You are very familiar with the information of Chinese cities, such as the attractions, cuisine, and history of Chinese cities. Please introduce the city of Hangzhou. Hangzhou is the capital of Zhejiang Province in eastern China. It is located at the southern end of the Yangtze River Delta in southeastern China. It is one of the most famous tourist cities in China. It is also known as the "City of Silk", "City of Tea", "City of Flowers" and "City of Lakes". It has a population of 6.5 million people. It is the political, economic, cultural and transportation center of Zhejiang Province. It is also an important industrial base in China. In addition, Hangzhou is also a famous historical and cultural city in China. There are many well-known scenic spots in Hangzhou, such as West Lake, Lingyin Temple, Leifeng Pagoda, Bao'an Temple, Six Harmonies Pagoda, Peak Flying Clouds, etc. Hangzhou is also known for its delicious cuisine. Hangzhou cuisine is one of the eight major cuisines in China. It is characterized by fresh, sweet, sour, salty, and mellow. Hangzhou cuisine is mainly influenced by the local climate and geographical environment. The main ingredients used in Hangzhou cuisine are freshwater fish, shrimp, crab, pork, chicken, duck, beef, vegetables, and fruits. Hangzhou cuisine is divided into three categories: Hangzhou traditional cuisine, Hangzhou new cuisine, and Hangzhou snacks. Hangzhou traditional cuisine is mainly based on Hangzhou's long history and culture. Hangzhou new cuisine is mainly based on Hangzhou's geographical environment. Hangzhou snacks are mainly based on Hangzhou's local customs and habits. Hangzhou cuisine is rich in color, fragrance, taste, and nutrition. Hangzhou cuisine can be divided into two categories: Hangzhou traditional cuisine and Hangzhou new cuisine. Hangzhou traditional cuisine is mainly based on Hangzhou's long:eightspokedasterisk: - Question: Roger has 5 tennis balls. He buys 2 more cans of tennis balls. Each can has 3 tennis balls. How many tennis balls does he have now? Answer: Roger started with 5 balls. 2 cans of 3 each is 6 tennis balls. 5 + 6 = 11. The answer is 11. Question: The cafeteria had 23 apples. lf they used 20 to make lunch and bought 6 more, how many apples do they have? Answer: Cafeteria started with 23 apples. 20 apples were used to make lunch. 6 more apples were bought. 23 - 20 = 3. 3 + 6 = 9. The answer is 9. Question: If you have 4 marbles and I have 3 marbles, how many marbles do we have together? Answer: 4 + 3 = 7. The answer is 7.:arrowleft: The effectiveness of information extraction is illustrated in the following figure. We tested different instructions for different tasks as well as the same instructions for the same task, and achieved good results for all of them. Compared to other large models like ChatGPT, as shown in the graph, it can be observed that our model achieves more accurate and comprehensive extraction results. However, we have also identified some extraction errors in ZhiXi. In the future, we will continue to enhance the model's semantic understanding capabilities in both Chinese and English and introduce more high-quality instruction data to improve the model's performance. > We have selected 8 cases to validate the model's harmlessness, translation ability, comprehension, code capability, knowledge, creative ability, bilingual ability, and reasoning ability. 这首诗描绘了诗人在夜深人静的时候，面对明月而产生的思乡之情。诗人用“床前明月光，疑是地上霜”这两句诗来描绘月光的清澈明亮，同时也表达了诗人对故乡的思念之情。最后两句“举头望明月，低头思故乡”更是将诗人的思乡之情表现得淋漓尽致。 夏天是一个充满活力的季节， 它让我们感受到生命的律动。 让我们放下一切烦恼， 去感受夏天带给我们的快乐。 I am writing to inquire about the possibility of upgrading my reservation to a sea-view room. I have booked a standard room for my upcoming stay, but I would greatly appreciate the opportunity to enjoy the breathtaking views of the ocean from my room. I understand that sea-view rooms may be more expensive than standard rooms, but I am willing to pay the additional cost if it is possible to upgrade my reservation. Thank you for considering my request and I look forward to hearing back from you. (3x + 1) - 1 = 10 - 1 3x = 9 x = 3 shell conda create -n zhixi python=3.9 -y conda activate zhixi pip install torch==1.12.0+cu116 torchvision==0.13.0+cu116 torchaudio==0.12.0 --extra-index-url https://download.pytorch.org/whl/cu116 pip install -r requirements.txt shell |-- 13B | |-- checklist.chk | |-- consolidated.00.pth | |-- consolidated.01.pth | |-- params.json |-- llama.sh |-- tokenizer.model |-- tokenizerchecklist.chk shell python tools/download.py --downloadpath ./zhixi-diff --onlybase shell python tools/download.py --downloadpath ./zhixi-diff-fp16 --onlybase --fp16 shell python convertllamaweightstohf.py --inputdir ./ --modelsize 13B --outputdir ./converted shell python tools/weightdiff.py recover --pathraw ./converted --pathdiff ./zhixi-diff --pathtuned ./zhixi shell python tools/weightdiff.py recover --pathraw ./converted --pathdiff ./zhixi-diff-fp16 --pathtuned ./zhixi shell python tools/download.py --downloadpath ./LoRA --onlylora shell python examples/generatefinetune.py --basemodel ./zhixi shell python examples/generatelora.py --load8bit --basemodel ./zhixi --loraweights ./lora --runiecases shell python examples/generatelora.py --load8bit --basemodel ./zhixi --loraweights ./lora --rungeneralcases shell python examples/generatefinetune.py --basemodel ./zhixi --interactive shell python examples/generatefinetuneweb.py --basemodel ./zhixi shell python examples/generateloraweb.py --basemodel ./zhixi --loraweights ./lora bibtex @article{cama, author = {Jintian Zhang, Xiaohan Wang, Honghao Gui, Xiang Chen, Yinuo Jiang, Zhen Bi, Jing Chen, Shengyu Mao, Shuofei Qiao, Xiaozhuan Liang, Yixin Ou, Ruinan Fang, Zekun Xi, Shumin Deng, Huajun Chen, Ningyu Zhang}, title = {DeepKE-LLM: A Large Language Model Based Knowledge Extraction Toolkit}, year = {2023}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/}}, } ``` We are very grateful to the following open source projects for their help: