ChatLaw is an open-source legal large language model tailored for Chinese legal scenarios. It aims to combine LLM and knowledge bases to provide solutions for legal scenarios. The models include ChatLaw-13B and ChatLaw-33B, trained on various legal texts to construct dialogue data. The project focuses on improving logical reasoning abilities and plans to train models with parameters exceeding 30B for better performance. The dataset consists of forum posts, news, legal texts, judicial interpretations, legal consultations, exam questions, and court judgments, cleaned and enhanced to create dialogue data. The tool is designed to assist in legal tasks requiring complex logical reasoning, with a focus on accuracy and reliability.

Only Train Once (OTO) is an automatic, architecture-agnostic DNN training and compression framework that allows users to train a general DNN from scratch or a pretrained checkpoint to achieve high performance and slimmer architecture simultaneously in a one-shot manner without fine-tuning. The framework includes features for automatic structured pruning and erasing operators, as well as hybrid structured sparse optimizers for efficient model compression. OTO provides tools for pruning zero-invariant group partitioning, constructing pruned models, and visualizing pruning and erasing dependency graphs. It supports the HESSO optimizer and offers a sanity check for compliance testing on various DNNs. The repository also includes publications, installation instructions, quick start guides, and a roadmap for future enhancements and collaborations.

Trafilatura is a Python package and command-line tool for gathering text on the Web and simplifying the process of turning raw HTML into structured, meaningful data. It includes components for web crawling, downloads, scraping, and extraction of main texts, metadata, and comments. The tool aims to focus on actual content, avoid noise, and make sense of data and metadata. It is robust, fast, and widely used by companies and institutions. Trafilatura outperforms other libraries in text extraction benchmarks and offers various features like support for sitemaps, parallel processing, configurable extraction of key elements, multiple output formats, and optional add-ons. The tool is actively maintained with regular updates and comprehensive documentation.

This repository is dedicated to the pruning of large language models (LLMs). It aims to serve as a comprehensive resource for researchers and practitioners interested in the efficient reduction of model size while maintaining or enhancing performance. The repository contains various papers, summaries, and links related to different pruning approaches for LLMs, along with author information and publication details. It covers a wide range of topics such as structured pruning, unstructured pruning, semi-structured pruning, and benchmarking methods. Researchers and practitioners can explore different pruning techniques, understand their implications, and access relevant resources for further study and implementation.

Fluid is an open source Kubernetes-native Distributed Dataset Orchestrator and Accelerator for data-intensive applications, such as big data and AI applications. It implements dataset abstraction, scalable cache runtime, automated data operations, elasticity and scheduling, and is runtime platform agnostic. Key concepts include Dataset and Runtime. Prerequisites include Kubernetes version > 1.16, Golang 1.18+, and Helm 3. The tool offers features like accelerating remote file accessing, machine learning, accelerating PVC, preloading dataset, and on-the-fly dataset cache scaling. Contributions are welcomed, and the project is under the Apache 2.0 license with a vendor-neutral approach.

AutoPatent is a multi-agent framework designed for automatic patent generation. It challenges large language models to generate full-length patents based on initial drafts. The framework leverages planner, writer, and examiner agents along with PGTree and RRAG to craft lengthy, intricate, and high-quality patent documents. It introduces a new metric, IRR (Inverse Repetition Rate), to measure sentence repetition within patents. The tool aims to streamline the patent generation process by automating the creation of detailed and specialized patent documents.

LLMsForTimeSeries is a repository that questions the usefulness of language models in time series forecasting. The work shows that simple baselines outperform most language model-based time series forecasting models. It includes ablation studies on LLM-based TSF methods and introduces the PAttn method, showcasing the performance of patching and attention structures in forecasting. The repository provides datasets, setup instructions, and scripts for running ablations on different datasets.

h2oGPT is an Apache V2 open-source project that allows users to query and summarize documents or chat with local private GPT LLMs. It features a private offline database of any documents (PDFs, Excel, Word, Images, Video Frames, Youtube, Audio, Code, Text, MarkDown, etc.), a persistent database (Chroma, Weaviate, or in-memory FAISS) using accurate embeddings (instructor-large, all-MiniLM-L6-v2, etc.), and efficient use of context using instruct-tuned LLMs (no need for LangChain's few-shot approach). h2oGPT also offers parallel summarization and extraction, reaching an output of 80 tokens per second with the 13B LLaMa2 model, HYDE (Hypothetical Document Embeddings) for enhanced retrieval based upon LLM responses, a variety of models supported (LLaMa2, Mistral, Falcon, Vicuna, WizardLM. With AutoGPTQ, 4-bit/8-bit, LORA, etc.), GPU support from HF and LLaMa.cpp GGML models, and CPU support using HF, LLaMa.cpp, and GPT4ALL models. Additionally, h2oGPT provides Attention Sinks for arbitrarily long generation (LLaMa-2, Mistral, MPT, Pythia, Falcon, etc.), a UI or CLI with streaming of all models, the ability to upload and view documents through the UI (control multiple collaborative or personal collections), Vision Models LLaVa, Claude-3, Gemini-Pro-Vision, GPT-4-Vision, Image Generation Stable Diffusion (sdxl-turbo, sdxl) and PlaygroundAI (playv2), Voice STT using Whisper with streaming audio conversion, Voice TTS using MIT-Licensed Microsoft Speech T5 with multiple voices and Streaming audio conversion, Voice TTS using MPL2-Licensed TTS including Voice Cloning and Streaming audio conversion, AI Assistant Voice Control Mode for hands-free control of h2oGPT chat, Bake-off UI mode against many models at the same time, Easy Download of model artifacts and control over models like LLaMa.cpp through the UI, Authentication in the UI by user/password via Native or Google OAuth, State Preservation in the UI by user/password, Linux, Docker, macOS, and Windows support, Easy Windows Installer for Windows 10 64-bit (CPU/CUDA), Easy macOS Installer for macOS (CPU/M1/M2), Inference Servers support (oLLaMa, HF TGI server, vLLM, Gradio, ExLLaMa, Replicate, OpenAI, Azure OpenAI, Anthropic), OpenAI-compliant, Server Proxy API (h2oGPT acts as drop-in-replacement to OpenAI server), Python client API (to talk to Gradio server), JSON Mode with any model via code block extraction. Also supports MistralAI JSON mode, Claude-3 via function calling with strict Schema, OpenAI via JSON mode, and vLLM via guided_json with strict Schema, Web-Search integration with Chat and Document Q/A, Agents for Search, Document Q/A, Python Code, CSV frames (Experimental, best with OpenAI currently), Evaluate performance using reward models, and Quality maintained with over 1000 unit and integration tests taking over 4 GPU-hours.

This repository contains tutorials for using Large Language Models (LLMs) in the U.S. Department of Defense. The tutorials utilize open-source frameworks and LLMs, allowing users to run them in their own cloud environments. The repository is maintained by the Defense Digital Service and welcomes contributions from users.

veRL is a flexible and efficient reinforcement learning training framework designed for large language models (LLMs). It allows easy extension of diverse RL algorithms, seamless integration with existing LLM infrastructures, and flexible device mapping. The framework achieves state-of-the-art throughput and efficient actor model resharding with 3D-HybridEngine. It supports popular HuggingFace models and is suitable for users working with PyTorch FSDP, Megatron-LM, and vLLM backends.

YAO is an open-source application engine written in Golang, suitable for developing business systems, website/APP API, admin panel, and self-built low-code platforms. It adopts a flow-based programming model to implement functions by writing YAO DSL or using JavaScript. Yao allows developers to create web services by processes, creating a database model, writing API services, and describing dashboard interfaces just by JSON for web & hardware, and 10x productivity. It is based on the flow-based programming idea, developed in Go language, and supports multiple ways to expand the data stream processor. Yao has a built-in data management system, making it suitable for quickly making various management backgrounds, CRM, ERP, and other internal enterprise systems. It is highly versatile, efficient, and performs better than PHP, JAVA, and other languages.

OpenNARS-for-Applications is an implementation of a Non-Axiomatic Reasoning System, a general-purpose reasoner that adapts under the Assumption of Insufficient Knowledge and Resources. The system combines the logic and conceptual ideas of OpenNARS, event handling and procedure learning capabilities of ANSNA and 20NAR1, and the control model from ALANN. It is written in C, offers improved reasoning performance, and has been compared with Reinforcement Learning and means-end reasoning approaches. The system has been used in real-world applications such as assisting first responders, real-time traffic surveillance, and experiments with autonomous robots. It has been developed with a pragmatic mindset focusing on effective implementation of existing theory.

Do-Not-Answer is an open-source dataset curated to evaluate Large Language Models' safety mechanisms at a low cost. It consists of prompts to which responsible language models do not answer. The dataset includes human annotations and model-based evaluation using a fine-tuned BERT-like evaluator. The dataset covers 61 specific harms and collects 939 instructions across five risk areas and 12 harm types. Response assessment is done for six models, categorizing responses into harmfulness and action categories. Both human and automatic evaluations show the safety of models across different risk areas. The dataset also includes a Chinese version with 1,014 questions for evaluating Chinese LLMs' risk perception and sensitivity to specific words and phrases.

The 'nlp-llms-resources' repository is a comprehensive resource list for Natural Language Processing (NLP) and Large Language Models (LLMs). It covers a wide range of topics including traditional NLP datasets, data acquisition, libraries for NLP, neural networks, sentiment analysis, optical character recognition, information extraction, semantics, topic modeling, multilingual NLP, domain-specific LLMs, vector databases, ethics, costing, books, courses, surveys, aggregators, newsletters, papers, conferences, and societies. The repository provides valuable information and resources for individuals interested in NLP and LLMs.

BABILong is a generative benchmark designed to evaluate the performance of NLP models in processing long documents with distributed facts. It consists of 20 tasks that simulate interactions between characters and objects in various locations, requiring models to distinguish important information from irrelevant details. The tasks vary in complexity and reasoning aspects, with test samples potentially containing millions of tokens. The benchmark aims to challenge and assess the capabilities of Large Language Models (LLMs) in handling complex, long-context information.

ChatLaw is an open-source legal large language model tailored for Chinese legal scenarios. It aims to combine LLM and knowledge bases to provide solutions for legal scenarios. The models include ChatLaw-13B and ChatLaw-33B, trained on various legal texts to construct dialogue data. The project focuses on improving logical reasoning abilities and plans to train models with parameters exceeding 30B for better performance. The dataset consists of forum posts, news, legal texts, judicial interpretations, legal consultations, exam questions, and court judgments, cleaned and enhanced to create dialogue data. The tool is designed to assist in legal tasks requiring complex logical reasoning, with a focus on accuracy and reliability.

Lawyer LLaMA is a large language model that has been specifically trained on legal data, including Chinese laws, regulations, and case documents. It has been fine-tuned on a large dataset of legal questions and answers, enabling it to understand and respond to legal inquiries in a comprehensive and informative manner. Lawyer LLaMA is designed to assist legal professionals and individuals with a variety of law-related tasks, including: * **Legal research:** Quickly and efficiently search through vast amounts of legal information to find relevant laws, regulations, and case precedents. * **Legal analysis:** Analyze legal issues, identify potential legal risks, and provide insights on how to proceed. * **Document drafting:** Draft legal documents, such as contracts, pleadings, and legal opinions, with accuracy and precision. * **Legal advice:** Provide general legal advice and guidance on a wide range of legal matters, helping users understand their rights and options. Lawyer LLaMA is a powerful tool that can significantly enhance the efficiency and effectiveness of legal research, analysis, and decision-making. It is an invaluable resource for lawyers, paralegals, law students, and anyone else who needs to navigate the complexities of the legal system.

This repository is dedicated to summarizing papers related to large language models with the field of law. It includes applications of large language models in legal tasks, legal agents, legal problems of large language models, data resources for large language models in law, law LLMs, and evaluation of large language models in the legal domain.

ChatLaw is an open-source legal large language model tailored for Chinese legal scenarios. It aims to combine LLM and knowledge bases to provide solutions for legal scenarios. The models include ChatLaw-13B and ChatLaw-33B, trained on various legal texts to construct dialogue data. The project focuses on improving logical reasoning abilities and plans to train models with parameters exceeding 30B for better performance. The dataset consists of forum posts, news, legal texts, judicial interpretations, legal consultations, exam questions, and court judgments, cleaned and enhanced to create dialogue data. The tool is designed to assist in legal tasks requiring complex logical reasoning, with a focus on accuracy and reliability.

Lawyer LLaMA is a large language model that has been specifically trained on legal data, including Chinese laws, regulations, and case documents. It has been fine-tuned on a large dataset of legal questions and answers, enabling it to understand and respond to legal inquiries in a comprehensive and informative manner. Lawyer LLaMA is designed to assist legal professionals and individuals with a variety of law-related tasks, including: * **Legal research:** Quickly and efficiently search through vast amounts of legal information to find relevant laws, regulations, and case precedents. * **Legal analysis:** Analyze legal issues, identify potential legal risks, and provide insights on how to proceed. * **Document drafting:** Draft legal documents, such as contracts, pleadings, and legal opinions, with accuracy and precision. * **Legal advice:** Provide general legal advice and guidance on a wide range of legal matters, helping users understand their rights and options. Lawyer LLaMA is a powerful tool that can significantly enhance the efficiency and effectiveness of legal research, analysis, and decision-making. It is an invaluable resource for lawyers, paralegals, law students, and anyone else who needs to navigate the complexities of the legal system.

DISC-LawLLM is a legal domain large model that aims to provide professional, intelligent, and comprehensive **legal services** to users. It is developed and open-sourced by the Data Intelligence and Social Computing Lab (Fudan-DISC) at Fudan University.

Marly is a tool that allows users to search for and extract context-specific data from various types of documents such as PDFs, Word files, Powerpoints, and websites. It provides the ability to extract data in structured formats like JSON or Markdown, making it easy to integrate into workflows. Marly supports multi-schema and multi-document extraction, offers built-in caching for rapid repeat extractions, and ensures no vendor lock-in by allowing flexibility in choosing model providers.

This repository provides templates for software and digital work licenses that restrict usage in AI training datasets or AI technologies. It includes various license styles such as Apache, BSD, MIT, UPL, ISC, CC0, and MPL-2.0.

AutoPatent is a multi-agent framework designed for automatic patent generation. It challenges large language models to generate full-length patents based on initial drafts. The framework leverages planner, writer, and examiner agents along with PGTree and RRAG to craft lengthy, intricate, and high-quality patent documents. It introduces a new metric, IRR (Inverse Repetition Rate), to measure sentence repetition within patents. The tool aims to streamline the patent generation process by automating the creation of detailed and specialized patent documents.