research tools

Firecrawl MCP Server is a plug-in that integrates powerful web crawling functions and supports a variety of LLM clients such as Cursor and Claude. It can efficiently crawl, search and extract web content, and provides functions such as automatic retry and traffic limitation, making it suitable for developers and researchers. The product is highly flexible and scalable and can be used for batch crawling and in-depth research.

DeepSeek-Prover-V2-671B is an advanced artificial intelligence model designed to provide powerful inference capabilities. It is based on the latest technology and suitable for a variety of application scenarios. This model is open source and aims to promote the democratization and popularization of artificial intelligence technology, lower technical barriers, and enable more developers and researchers to use AI technology to innovate. By using this model, users can improve their work efficiency and promote the progress of various projects.

Atom of Thoughts (AoT) is a new reasoning framework that transforms the reasoning process into a Markov process by representing solutions as combinations of atomic problems. This framework significantly improves the performance of large language models on inference tasks through the decomposition and contraction mechanism, while reducing the waste of computing resources. AoT can not only be used as an independent inference method, but also as a plug-in for existing test-time extension methods, flexibly combining the advantages of different methods. The framework is open source and implemented in Python, making it suitable for researchers and developers to conduct experiments and applications in the fields of natural language processing and large language models.

ViDoRAG is a new multi-modal retrieval-enhanced generation framework developed by Alibaba's natural language processing team, specifically designed for complex reasoning tasks in processing visually rich documents. This framework significantly improves the robustness and accuracy of the generative model through dynamic iterative inference agents and a Gaussian Mixture Model (GMM)-driven multi-modal retrieval strategy. The main advantages of ViDoRAG include efficient processing of visual and textual information, support for multi-hop reasoning, and high scalability. The framework is suitable for scenarios where information needs to be retrieved and generated from large-scale documents, such as intelligent question answering, document analysis and content creation. Its open source nature and flexible modular design make it an important tool for researchers and developers in the field of multimodal generation.

Level-Navi Agent is an open source general network search agent framework that can decompose complex problems and gradually search information on the Internet until it answers user questions. It provides a benchmark for evaluating the performance of models on search tasks by providing the Web24 data set, covering five major fields: finance, games, sports, movies, and events. This framework supports zero-shot and few-shot learning, providing an important reference for the application of large language models in the field of Chinese web search agents.

M2RAG is a benchmark code library for retrieval augmentation generation in multimodal contexts. It answers questions by retrieving documents across multiple modalities and evaluates the ability of multimodal large language models (MLLMs) in leveraging multimodal contextual knowledge. The model was evaluated on tasks such as image description, multimodal question answering, fact verification, and image rearrangement, aiming to improve the effectiveness of the model in multimodal context learning. M2RAG provides researchers with a standardized testing platform that helps advance the development of multimodal language models.

olmOCR is an open source toolkit developed by the Allen Institute for Artificial Intelligence (AI2), designed to linearize PDF documents for use in the training of large language models (LLM). This toolkit solves the problem that traditional PDF documents have complex structures and are difficult to directly use for model training by converting PDF documents into a format suitable for LLM processing. It supports a variety of functions, including natural text parsing, multi-version comparison, language filtering, and SEO spam removal. The main advantage of olmOCR is that it can efficiently process a large number of PDF documents and improve the accuracy and efficiency of text parsing through optimized prompt strategies and model fine-tuning. This toolkit is intended for researchers and developers who need to process large amounts of PDF data, especially in the fields of natural language processing and machine learning.

MLGym is an open source framework and benchmark developed by Meta's GenAI team and UCSB NLP team for training and evaluating AI research agents. It promotes the development of reinforcement learning algorithms by providing diverse AI research tasks and helping researchers train and evaluate models in real-world research scenarios. The framework supports a variety of tasks, including computer vision, natural language processing and reinforcement learning, and aims to provide a standardized testing platform for AI research.

PIKE-RAG is a domain knowledge and reasoning enhanced generative model developed by Microsoft, designed to enhance the capabilities of large language models (LLM) through knowledge extraction, storage and reasoning logic. Through multi-module design, this model can handle complex multi-hop question and answer tasks, and significantly improves the accuracy of question and answer in fields such as industrial manufacturing, mining, and pharmaceuticals. The main advantages of PIKE-RAG include efficient knowledge extraction capabilities, powerful multi-source information integration capabilities, and multi-step reasoning capabilities, making it perform well in scenarios that require deep domain knowledge and complex logical reasoning.

SWE-Lancer is a benchmark launched by OpenAI to evaluate the performance of cutting-edge language models on real-world free software engineering tasks. The benchmark covers a variety of independent engineering tasks ranging from a $50 bug fix to a $32,000 feature implementation, as well as management tasks such as the selection of a model between technical implementation options. By mapping performance to monetary value through models, SWE-Lancer provides a new perspective on the economic impact of AI model development and advances related research.

Goedel-Prover is an open source large-scale language model focused on automated theorem proving. It significantly improves the efficiency of automated proof of mathematical problems by translating natural language mathematical problems into formal languages ​​(such as Lean 4) and generating formal proofs. The model achieved a success rate of 57.6% on the miniF2F benchmark, surpassing other open source models. Its main advantages include high performance, open source scalability, and deep understanding of mathematical problems. Goedel-Prover aims to promote the development of automated theorem proving technology and provide powerful tool support for mathematical research and education.

OpenThinker-32B is an open source reasoning model developed by the Open Thoughts team. It achieves powerful inference capabilities by scaling data scale, validating inference paths, and scaling model sizes. The model outperforms existing open data inference models on inference benchmarks in mathematics, code, and science. Its main advantages include open source data, high performance and scalability. The model is fine-tuned based on Qwen2.5-32B-Instruct and trained on large-scale data sets, aiming to provide researchers and developers with powerful inference tools.

RAG-FiT is a powerful tool designed to improve the capabilities of large language models (LLMs) through retrieval-augmented generation (RAG) technology. It helps models better utilize external information by creating specialized RAG augmented datasets. The library supports the entire process from data preparation to model training, inference, and evaluation. Its main advantages include modular design, customizable workflows and support for multiple RAG configurations. RAG-FiT is based on an open source license and is suitable for researchers and developers for rapid prototyping and experimentation.

Open-source DeepResearch is an open source project that aims to reproduce functions similar to OpenAI Deep Research through open source frameworks and tools. The project is based on the Hugging Face platform and utilizes open source large language models (LLM) and agent frameworks to achieve complex multi-step reasoning and information retrieval through code agents and tool calls. Its main advantages are that it is open source, highly customizable, and able to leverage the power of the community for continuous improvement. The goal of the project is to enable everyone to run intelligent agents like DeepResearch locally, using their favorite models, fully localized and customizable.

node-DeepResearch is a deep research model based on Jina AI technology that focuses on finding answers to questions through continuous search and reading of web pages. It leverages the LLM capabilities provided by Gemini and the web search capabilities of Jina Reader to handle complex query tasks and generate answers through multi-step reasoning and information integration. The main advantage of this model lies in its powerful information retrieval capabilities and reasoning capabilities, and its ability to handle complex problems that require multi-step solutions. It is suitable for scenarios that require in-depth research and information mining, such as academic research, market analysis, etc. The model is currently open source, and users can obtain the code through GitHub and deploy it themselves.

OpenDeepResearcher is an AI-based research tool that, by combining services such as SERPAPI, Jina, and OpenRouter, can automatically conduct multiple rounds of iterative searches based on query topics entered by users until sufficient information is collected and a final report is generated. The core advantage of this tool lies in its efficient asynchronous processing capabilities, deduplication function and powerful LLM decision support, which can significantly improve research efficiency. It is mainly aimed at scientific researchers, students and professionals in related fields who need to conduct large-scale literature searches and information sorting, helping them quickly obtain high-quality research materials. The tool is currently available as open source and users can deploy and use it as needed.

DeepSeek-R1-Distill-Qwen-7B is a reinforcement learning-optimized inference model based on distillation optimization of Qwen-7B. It performs well on math, coding, and reasoning tasks, producing high-quality reasoning chains and solutions. This model significantly improves reasoning capabilities and efficiency through large-scale reinforcement learning and data distillation technology, and is suitable for scenarios that require complex reasoning and logical analysis.

DeepSeek-R1-Zero is an inference model developed by the DeepSeek team, focusing on improving the model's inference capabilities through reinforcement learning. The model exhibits powerful reasoning behaviors such as self-verification, reflection, and generation of long-chain reasoning without the need for supervised fine-tuning. Its key benefits include efficient inference capabilities, availability without pre-training, and superior performance on math, coding, and reasoning tasks. The model is developed based on the DeepSeek-V3 architecture, supports large-scale inference tasks, and is suitable for research and commercial applications.

DeepSeek-R1 is the first-generation inference model launched by the DeepSeek team. It is trained through large-scale reinforcement learning and can demonstrate excellent inference capabilities without supervised fine-tuning. The model performs well on math, coding, and inference tasks and is comparable to the OpenAI-o1 model. DeepSeek-R1 also provides a variety of distillation models suitable for scenarios with different scales and performance requirements. Its open source nature provides powerful tools for the research community and supports commercial use and secondary development.

PatronusAI/Llama-3-Patronus-Lynx-70B-Instruct is a large language model based on the Llama-3 architecture, designed to detect hallucination problems in RAG settings. The model analyzes a given document, question, and answer and evaluates whether the answer is faithful to the document content. Its main advantages lie in its high-precision hallucination detection capabilities and strong language understanding capabilities. This model was developed by Patronus AI and is suitable for scenarios that require high-precision information verification, such as financial analysis, medical research, etc. The model is currently free to use, but specific commercial applications may require contacting the developer.

Imitate Before Detect is an innovative text detection technology designed to improve the detection of machine-revised text. The technology more accurately identifies machine-revised text by mimicking the style preferences of large language models (LLMs). Its core advantage lies in its ability to effectively distinguish the nuances of machine-generated and human writing, thus having important application value in the field of text detection. Background information on this technology shows that it can significantly improve detection accuracy, and the AUC value increases by 13% when processing open source LLM revision text, and increases by 5% and 19% respectively when detecting GPT-3.5 and GPT-4o revision text. It is positioned to provide researchers and developers with an efficient text detection tool.

Eurus-2-7B-SFT is a large language model fine-tuned based on the Qwen2.5-Math-7B model, focusing on improving mathematical reasoning and problem-solving capabilities. This model learns reasoning patterns through imitation learning (supervised fine-tuning), and can effectively solve complex mathematical problems and programming tasks. Its main advantage lies in its strong reasoning ability and accurate processing of mathematical problems, and is suitable for scenarios that require complex logical reasoning. This model was developed by the PRIME-RL team and aims to improve the model's reasoning capabilities through implicit rewards.

ScreenSpot-Pro is a benchmark test specifically designed to evaluate GUI positioning models in high-resolution professional computer usage environments. It covers 23 applications spread across 5 specialized areas and 3 operating systems, highlighting the challenges models face when interacting with complex software. The existing model has an accuracy of only 18.9%, which emphasizes the need for further research. This product is designed to promote the development of GUI positioning models and improve the usability and performance of professional applications.

FlexRAG is a flexible and high-performance framework for retrieval augmentation generation (RAG) tasks. It supports multi-modal data, seamless configuration management, and out-of-the-box performance for research and prototyping. Written in Python, the framework is lightweight and high-performance, significantly increasing the speed and reducing latency of RAG workflows. Its main advantages include support for multiple data types, unified configuration management, and easy integration and expansion.

Zasper is an integrated development environment (IDE) designed for data science. It is designed from the ground up to support large-scale concurrent processing, with a minimal memory footprint, excellent speed, and the ability to handle a large number of concurrent connections. It is ideal for running REPL-style data applications like Jupyter notebooks. The main advantages of Zasper are its efficient concurrent processing capabilities and lightweight resource usage, making it of great application value in the field of data science. Currently, Zasper provides an open source version suitable for use by data scientists and developers.

Llama-3-Patronus-Lynx-8B-Instruct is a fine-tuned version based on the meta-llama/Meta-Llama-3-8B-Instruct model developed by Patronus AI, mainly used to detect hallucinations in RAG settings. The model is trained on multiple data sets including CovidQA, PubmedQA, DROP, RAGTruth, etc., including manual annotation and synthetic data. It evaluates whether a given document, question, and answer is faithful to the document content, does not provide new information outside the document, and does not contradict the document information.

Patronus-Lynx-8B-Instruct-v1.1 is a fine-tuned version based on the meta-llama/Meta-Llama-3.1-8B-Instruct model, mainly used to detect hallucinations in RAG settings. The model has been trained on multiple data sets such as CovidQA, PubmedQA, DROP, RAGTruth, etc., and contains manual annotation and synthetic data. It evaluates whether a given document, question, and answer is faithful to the document content, does not provide new information beyond the scope of the document, and does not contradict the document information.

SakanaAI/asal is a scientific research project that uses Foundation Models (FMs) to automatically search for Artificial Life (ALife). The project combines the latest artificial intelligence techniques, especially visual language-based models, to discover artificial life simulations capable of producing target phenomena, generating temporally open novelties, and illuminating an entire interesting and diverse simulation space. It can span a variety of ALife substrates, including Boids, Particle Life, Game of Life, Lenia, and Neural Cell Automata, demonstrating the potential of accelerating artificial life research through technical means.

GraphRAG Visualizer is a web-based tool designed to visualize and explore data produced by Microsoft's GraphRAG tool. GraphRAG is a technology developed by Microsoft for generating graph-structured data. GraphRAG Visualizer allows users to easily view and analyze data without additional software or scripts by letting users upload parquet files. Key benefits of the tool include graphical visualization, tabular presentation of data, search capabilities, and local processing of data ensuring data security and privacy.

Deepthought-8B is a small but powerful inference model built on LLaMA-3.1 8B and aims to make AI inference more transparent and controllable. Although the model is relatively small, it achieves complex inference capabilities comparable to larger models. Designed with its unique problem-solving approach, the model breaks down its thinking process into clear, unique, documented steps, and outputs the reasoning process in a structured JSON format, making it easy to understand and verify its decision-making process.

Skywork-o1-Open-PRM-Qwen-2.5-7B is a series of models developed by Kunlun Technology’s Skywork team that combine o1-style slow thinking and reasoning capabilities. This family of models not only exhibits innate thinking, planning, and reflective abilities in their output, but also shows significant improvements in reasoning skills on standard benchmark tests. It represents a strategic advance in AI capabilities, pushing an otherwise weak base model to the state of the art (SOTA) for inference tasks.

Skywork-o1-Open-PRM-Qwen-2.5-1.5B is a series of models developed by the Skywork team that combine o1-style slow thinking and reasoning capabilities. This model is specifically designed to enhance reasoning capabilities through incremental process rewards and is suitable for solving small-scale complex problems. Unlike simple OpenAI o1 model reproductions, the Skywork o1 Open series models not only demonstrate inherent thinking, planning and reflection capabilities in their output, but also significantly improve their reasoning skills in standard benchmark tests. This series represents a strategic advancement in AI capabilities, pushing originally weak basic models to the state of the art (SOTA) for inference tasks.

Skywork-o1-Open-Llama-3.1-8B is a series of models developed by Kunlun Technology’s Skywork team that combine o1-style slow thinking and reasoning capabilities. This family of models not only exhibits innate thinking, planning and reflection abilities in their output, but also significantly improves their reasoning skills on standard benchmark tests. This series represents a strategic advance in AI capabilities, pushing an otherwise weak base model to the state of the art (SOTA) for inference tasks.

QwQ-32B-Preview is an experimental research model developed by the Qwen team to improve the reasoning capabilities of artificial intelligence. The model demonstrates promising analytical capabilities but also has some important limitations. The model performed well in mathematics and programming, but there was room for improvement in common sense reasoning and nuanced language understanding. The model uses a transformers architecture with 32.5B parameters, 64 layers, and 40 attention heads (GQA). Product background information shows that QwQ-32B-Preview is a further development based on the Qwen2.5-32B model and has deeper language understanding and generation capabilities.

QwQ (Qwen with Questions) is an experimental research model developed by the Qwen team to improve the reasoning capabilities of artificial intelligence. It approaches every question with genuine curiosity and skepticism in a philosophical spirit, seeking deeper truth through self-questioning and reflection. QwQ excels in mathematics and programming, especially when dealing with complex problems. Although it is still learning and growing, it has already shown significant potential for deep reasoning in technology.

LLNL/LUAR is a Transformer-based model used to learn author representation and is mainly used for cross-domain transfer research for author verification. The model, introduced in an EMNLP 2021 paper, studies whether author representations learned in one domain can be transferred to another domain. Key advantages of the model include the ability to handle large-scale datasets and perform zero-shot transfer across multiple different domains such as Amazon reviews, fan fiction short stories, and Reddit comments. Product background information includes its innovative research in the field of cross-domain author verification and its application potential in the field of natural language processing. This product is open source, follows the Apache-2.0 license agreement, and can be used free of charge.

ZipPy is a fast research AI detection tool that uses compression ratio to indirectly measure text perplexity. ZipPy performs classification by comparing the similarity between the AI-generated corpus and the provided samples. The main advantages of this tool are that it is fast, scalable and can be embedded into other systems. Background information on ZipPy shows that it is intended to complement existing large language model detection systems, which often use large models to calculate the probability of each word, and ZipPy provides a faster approximation method.

Chonkie is a text chunking library designed for Retrieval Enhanced Generation (RAG) applications. It is lightweight, fast, and easy to use. The library provides multiple text chunking methods, supports multiple tokenizers, and is high-performance. The main advantages of Chonkie include rich functionality, ease of use, fast processing speed, extensive support and lightweight design. It is suitable for developers and researchers who need to efficiently process text data, especially in the fields of natural language processing and machine learning. Chonkie is open source, follows the MIT license, and can be used for free.

Fast GraphRAG is a streamlined and promptable framework designed for interpretable, high-precision, agent-driven retrieval workflows. It provides human-navigable views of knowledge by building graphs that support query, visualization, and updating. The framework is designed to run at scale without heavy resource or cost requirements, automatically generate and optimize graphs to fit specific domain and ontology needs, and support real-time updates. Fast GraphRAG leverages PageRank graph-based exploration with enhanced accuracy and reliability, and is fully asynchronous, providing full type support for robust and predictable workflows.

askrepo is a source code reading tool based on LLM (Large Language Model). It can read the content of text files managed by Git, send it to the Google Gemini API, and provide answers to questions based on specified prompts. This product represents the application of natural language processing and machine learning technology in the field of code analysis. Its main advantages include the ability to understand and interpret code, helping developers quickly understand new projects or complex code bases. Product background information shows that askrepo is suitable for scenarios that require an in-depth understanding of the code, especially during the code review and maintenance stages. The product is open source and free to use.

O1-Journey is a project initiated by the GAIR research group at Shanghai Jiao Tong University to replicate and reimagine the capabilities of OpenAI’s O1 model. This project proposes a new training paradigm of "journey learning" and builds the first model to successfully integrate search and learning in mathematical reasoning. This model becomes an effective way to handle complex reasoning tasks through processes of trial and error, correction, backtracking, and reflection.

paper-reviewer is an open source project designed to automatically generate comprehensive reviews from arXiv papers and convert them into blog posts. This project provides a set of tools to help users build their own paper review blog. It is implemented through two Python scripts collect.py and convert.py, which are used to collect and generate reviews and convert reviews into blog posts respectively. This tool is useful for researchers and academics as it saves them time reviewing papers and helps them share their research results more efficiently.

SELA is an innovative system that enhances automated machine learning (AutoML) by combining Monte Carlo Tree Search (MCTS) with large language model (LLM) based agents. Traditional AutoML methods often produce low diversity and suboptimal code, limiting their effectiveness in model selection and integration. By representing pipeline configurations as trees, SELA enables agents to intelligently explore the solution space and iteratively improve their strategies based on experimental feedback.

LongRAG is a dual-view, robust retrieval-enhanced generation system paradigm based on large language models (LLM), aiming to enhance the understanding and retrieval capabilities of complex long text knowledge. This model is particularly suitable for long text question answering (LCQA) and is able to handle global information and factual details. Product background information shows that LongRAG improves the performance of long text question and answer tasks by combining retrieval and generation technologies, especially in scenarios that require multi-hop reasoning. The model is open source and free to use, mainly for researchers and developers.

ROCKET-1 is a visual-language model (VLMs) designed specifically for embodied decision-making in open-world environments. This model connects communication between VLMs and policy models through a visual-temporal context cueing protocol, leveraging object segmentation from past and current observations to guide policy-environment interactions. In this way, ROCKET-1 is able to unlock the visual-verbal reasoning capabilities of VLMs, enabling them to solve complex creative tasks, especially in spatial understanding. Experiments with ROCKET-1 in Minecraft show that this approach enables agents to accomplish previously unachievable tasks, highlighting the effectiveness of visual-temporal contextual cues in embodied decision-making.

KAG (Knowledge Augmented Generation) is a professional domain knowledge service framework that aims to bidirectionally enhance large language models and knowledge graphs through the advantages of knowledge graphs and vector retrieval, and solve the problems of RAG (Retrieval Augmentation Generation) technology such as the large gap between vector similarity and knowledge reasoning correlation, and its insensitivity to knowledge logic. KAG's performance on multi-hop question answering tasks is significantly better than methods such as NaiveRAG and HippoRAG. For example, the F1 score on hotpotQA increased by 19.6% and on 2wiki by 33.5%. KAG has been successfully used in two professional knowledge question and answer tasks of Ant Group, including government question and answer and health question and answer. Compared with the RAG method, the professionalism has been significantly improved.

Aya Expanse is an open-weighted research model with advanced multilingual capabilities. It focuses on combining high-performance pre-trained models with a year of research from Cohere For AI, including data arbitrage, multi-language preference training, security tuning and model merging. The model is a powerful multilingual large-scale language model serving 23 languages, including Arabic, Chinese (Simplified and Traditional), Czech, Dutch, English, French, German, Greek, Hebrew, Hindi, Indonesian, Italian, Japanese, Korean, Persian, Polish, Portuguese, Romanian, Russian, Spanish, Turkish, Ukrainian and Vietnamese.

Meta Lingua is a lightweight, efficient large language model (LLM) training and inference library designed for research. It uses easily modifiable PyTorch components, allowing researchers to experiment with new architectures, loss functions, and data sets. The library is designed to enable end-to-end training, inference, and evaluation, and provide tools to better understand model speed and stability. Although Meta Lingua is still under development, several sample applications are provided to demonstrate how to use this code base.

Langtrace is an open source observability tool for collecting and analyzing traces and metrics to help improve the performance of large language model (LLM) applications. It supports OpenTelemetry standard tracking and is self-hosted to avoid vendor lock-in. Langtrace provides end-to-end observability, helping users gain a comprehensive view of the entire machine learning process, including RAG or fine-tuning models. In addition, Langtrace also supports the establishment of feedback loops to create golden data sets through traced LLM interactions to continuously test and enhance AI applications.

Tost AI is a free, non-profit, open source service that provides inference services for the latest AI papers, using non-profit GPU clusters. Tost AI does not store any inference data and all data expires within 12 hours. Additionally, Tost AI provides the option to send data to a Discord channel. Each account is provided with 100 free wallet balances per day. If you want to get 1100 wallet balances per day, you can subscribe to GitHub Patron or Patreon. Tost AI sends all profits from the demo to the paper's first author, with a budget supported by corporate and individual sponsors.

MInference 1.0 is a sparse computing method designed to speed up the pre-population stage of long sequence processing. It implements a dynamic sparse attention approach to long-context large language models (LLMs) by identifying three unique patterns in the long-context attention matrix, accelerating the pre-population stage of 1M token cues while maintaining the capabilities of LLMs, especially retrieval capabilities.

FiddleCube is a product focused on the field of data science. It can quickly generate question and answer pairs from users' data and help users evaluate large language models (LLMs). It provides an accurate golden data set, supports multiple question types, and has the ability to evaluate data accuracy through metrics. In addition, FiddleCube also provides diagnostic tools to help users identify and improve poorly performing queries.

SuperCLUE is an online platform for evaluating and comparing the performance of large language models. It provides a variety of tasks and rankings, aiming to provide a standardized testing environment for AI researchers and developers. SuperCLUE supports various AI application scenarios, including mathematical reasoning, code generation, ultra-long text processing, etc., and can help users accurately evaluate the performance and capabilities of models on different tasks.

Berkeley Function-Calling Leaderboard is an online platform specifically designed to evaluate the ability of large language models (LLMs) to accurately call functions (or tools). The ranking is based on real-world data and updated regularly, providing a benchmark for measuring and comparing the performance of different models on specific programming tasks. It is a valuable resource for developers, researchers, and users interested in AI programming capabilities.

HyperCrawl is the first web crawler designed for LLM (Large Language Model) and RAG (Retrieval Augmented Generative Model) applications, aiming to develop powerful retrieval engines. By introducing a variety of advanced methods, it significantly reduces the crawling time of domain names and improves the efficiency of the retrieval process. HyperCrawl is part of HyperLLM and is dedicated to building the infrastructure for future LLM models that require fewer computing resources and outperform any existing models.

StarSearch is an online tool focused on git history and contributor analysis. It can help users quickly obtain information about contributor activities, identify key contributors, and find experts in specific fields based on work content. This tool is extremely important for open source project maintainers, developers, and team leaders because it can improve project management efficiency, optimize team collaboration, and promote communication and cooperation in the technical community. StarSearch is our AI-powered feature that provides deep insights into a contributor’s history and activity, bringing transparency and a new level of understanding to open source projects.

Imandra is an AI tool suite based on automated reasoning and formal verification technology. Whether you write critical code or need to understand the myriad of complex decisions a system may make, using Imandra ensures that the algorithms you create are safe, explainable, and fair.

OLMo is an open source language model and training framework released by AI2 Research Institute. It provides complete training data, code, model parameters, evaluation code and other resources, allowing researchers to train and experiment large-scale language models. The advantage of OLMo is that it is truly open. Users can access complete information from data to models, supplemented by rich documentation, which facilitates open research and collaboration among researchers. This framework lowers the threshold for language model research and enables more people to participate in promoting the advancement of language model technology.

NLTK is a leading Python platform for processing human language data. It provides an easy-to-use interface for accessing more than 50 corpora and lexical resources such as WordNet, and a set of text processing libraries for classification, tagging, parsing, and semantic reasoning. It also provides wrappers for industrial-grade NLP libraries and has an active discussion forum. NLTK is intended for linguists, engineers, students, educators, researchers, and industry users. NLTK is free to use and is an open source, community-driven project.

Cerelyze is a tool designed for engineers, researchers, and academics to turn technical research papers into usable code. It offers three main features: 1. Understand: Gain a deep understanding of research papers by having meaningful conversations with them. 2. Implementation: Automatically convert the methods discussed in the paper into Python code or step-by-step instructions, saving time and energy. 3. Run: Quickly understand the paper and view the output results by running the example case. Cerelyze also supports processing equations, tabular and graphical data, and can help engineers quickly prototype algorithms and accelerate innovation.

GitHub Data Explorer is an AI-based SQL generation tool that allows users to ask questions through natural language, generate corresponding SQL query statements, and display the results visually. It helps users quickly explore GitHub data without having SQL or drawing skills. The data source for GitHub Data Explorer comes from GH Archive, a non-profit project that records and archives GitHub event data. This tool is suitable for data analysis and research in the field of programming.