Search Results for "thinking"
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Showing 41 open source projects for "thinking"
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GLM-4.5
GLM-4.5: Open-source LLM for intelligent agents by Z.ai
...The flagship GLM-4.5 model has 355 billion total parameters with 32 billion active parameters, while the compact GLM-4.5-Air version offers 106 billion total parameters and 12 billion active parameters. Both models unify reasoning, coding, and intelligent agent capabilities, providing two modes: a thinking mode for complex reasoning and tool usage, and a non-thinking mode for immediate responses. They are released under the MIT license, allowing commercial use and secondary development. GLM-4.5 achieves strong performance on 12 industry-standard benchmarks, ranking 3rd overall, while GLM-4.5-Air balances competitive results with greater efficiency. ... -
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GLM-V
GLM-4.5V and GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning
...GLM-4.5V builds on the flagship GLM-4.5-Air foundation (106B parameters, 12B active), achieving state-of-the-art results on 42 benchmarks across image, video, document, GUI, and grounding tasks. It introduces hybrid training for broad-spectrum reasoning and a Thinking Mode switch to balance speed and depth of reasoning. GLM-4.1V-9B-Thinking incorporates reinforcement learning with curriculum sampling (RLCS) and Chain-of-Thought reasoning, outperforming models much larger in scale (e.g., Qwen-2.5-VL-72B) across many benchmarks. -
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GLM-4.7
Advanced language and coding AI model
...It delivers significant gains over GLM-4.6 in multilingual agentic coding, terminal-based workflows, and real-world developer benchmarks such as SWE-bench and Terminal Bench 2.0. The model introduces stronger “thinking before acting” behavior, improving stability and accuracy in complex agent frameworks like Claude Code, Cline, and Roo Code. GLM-4.7 also advances “vibe coding,” producing cleaner, more modern UIs, better-structured webpages, and visually improved slide layouts. Its tool-use capabilities are substantially enhanced, with notable improvements in browsing, search, and tool-integrated reasoning tasks. ... -
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gemini-web2api
Convert Google Gemini web into OpenAI-compatible API
...The project can run as a simple local server and uses a mostly single-file design with an optional dependency for streaming. It supports model aliases for Flash, Thinking, Pro-style routing, Auto, and Lite variants. The tool also includes optional API keys, function calling, SSE streaming, web search access, Docker deployment, and client examples for OpenAI SDK-style usage. It is useful for developers who want local experimentation with OpenAI-compatible tooling while routing requests through Gemini web behavior. -
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Hy-MT2 is a family of fast-thinking multilingual translation models built for complex real-world translation scenarios. It includes 1.8B, 7B, and 30B-A3B model sizes, giving users options for lightweight deployment, stronger general performance, or MoE-based capacity. The models support translation across 33 languages and are designed to follow detailed translation instructions in multiple languages.
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Qwen3
Qwen3 is the large language model series developed by Qwen team
Qwen3 is a cutting-edge large language model (LLM) series developed by the Qwen team at Alibaba Cloud. The latest updated version, Qwen3-235B-A22B-Instruct-2507, features significant improvements in instruction-following, reasoning, knowledge coverage, and long-context understanding up to 256K tokens. It delivers higher quality and more helpful text generation across multiple languages and domains, including mathematics, coding, science, and tool usage. Various quantized versions,... -
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IQuest-Coder-V1 Model Family
New family of code large language models (LLMs)
...They support native long contexts up to 128K tokens, enabling them to reason across large codebases and multi-file interactions without context fragmentation, and include “Thinking” variants optimized for complex reasoning and “Loop” variants with recurrent mechanisms to improve inference efficiency. IQuest-Coder-V1 delivers state-of-the-art performance on multiple coding benchmarks, demonstrating strong results in competitive programming, tool use, and agentic code generation. -
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VectorVein
No-code AI workflow
Use the power of AI to build your personal knowledge base + automated workflow. No programming, just dragging to create a strong workflow and automate all tasks. Vector vein is affected LangChain as well as langflow The uncode AI workflow software developed by the inspiration aims to combine the powerful capabilities of large language models and allow users to realize the intelligibility and automation of various daily workflows through simple drag. After the software is opened normally,... -
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Mini Agent
A minimal yet professional single agent demo project
...It is designed both as a reference implementation and as a usable agent, demonstrating a full execution loop that includes planning, tool calls, and iterative refinement. The project exposes an Anthropic-compatible API interface and fully supports interleaved thinking, letting the agent alternate between reasoning steps and tool invocations during long, complex tasks. It includes a basic toolset for file-system operations and shell commands, plus integrations with MCP tools such as web search and knowledge graph access. Mini-Agent also comes with “Claude Skills”-style predefined skills for tasks like document processing, design work, and testing, packaged as reusable behaviors that can be invoked by the agent as needed. -
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Book5_Essentials-Probability-Statistics
The book 5 of statistics in simplicity
...The repository explains topics such as distributions, sampling, inference, and uncertainty using visual demonstrations and intuitive narratives. Its teaching philosophy prioritizes conceptual clarity over heavy formalism, making statistical thinking more approachable for beginners. The material connects probability theory directly to real analytical workflows, helping learners understand how statistics supports predictive modeling. Like the other books in the series, it blends mathematical explanation with Python-based experimentation. Overall, the project provides a practical statistical foundation for students advancing into AI and data science. -
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Think Python
Jupyter notebooks and other resources for Think Python
Think Python is the companion repository for the third edition of Think Python: How to Think Like a Computer Scientist. It introduces programming through Python while emphasizing problem solving, abstraction, debugging, and computational thinking. The material is organized into chapter-based Jupyter notebooks that combine explanations, examples, and executable code. Topics progress from expressions and functions to collections, recursion, classes, files, and larger program structures. Supporting resources include solution notebooks, turtle graphics utilities, diagrams, sample data, images, and downloadable notebook archives. ... -
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MiniMax-M1
Open-weight, large-scale hybrid-attention reasoning model
MiniMax-M1 is presented as the world’s first open-weight, large-scale hybrid-attention reasoning model, designed to push the frontier of long-context, tool-using, and deeply “thinking” language models. It is built on the MiniMax-Text-01 foundation and keeps the same massive parameter budget, but reworks the attention and training setup for better reasoning and test-time compute scaling. Architecturally, it combines Mixture-of-Experts layers with lightning attention, enabling the model to support a native context length of 1 million tokens while using far fewer FLOPs than comparable reasoning models for very long generations. ... -
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Book2_Beauty-of-Data-Visualization
Machine Learning, Criticism and Correction
...It includes practical examples that demonstrate how different chart types reveal patterns, trends, and distributions in real datasets. The material emphasizes visual storytelling and design thinking alongside coding implementation. By combining theory with hands-on plotting exercises, the book helps readers build both analytical and presentation skills. Overall, it is intended as a foundational guide for anyone seeking to produce professional-quality data visualizations. -
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AdminSet
AdminSet for DevOps
Adminset is developed based on the DevOps concept and takes the integration of all operation and maintenance scenarios as its own responsibility. Adminset is a fully automated operation and maintenance platform developed based on operation and maintenance thinking. All functions of the client need to configure the ssh password-free login from the server to the client. -
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Academic Research Skills for Claude Code
Academic Research Skills for Claude Code
Academic Research Skills is a structured learning repository aimed at improving users’ ability to conduct rigorous academic research, particularly in technical and scientific domains. It compiles methodologies, frameworks, and best practices for literature review, critical analysis, and research writing. The project is designed as a self-guided resource, helping learners understand how to evaluate sources, synthesize information, and develop strong arguments. It likely integrates examples,... -
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Khoj
An AI personal assistant for your digital brain
...Khoj is a desktop application to search and chat with your notes, documents, and images. It is an offline-first, open-source AI personal assistant that is accessible from Emacs, Obsidian or your Web browser. Khoj is a thinking tool that is transparent, fun, and easy to engage with. You can build faster and better by using Khoj to search and reason across all your data sources. Khoj learns from your notes and documents to function as an extension of your brain. So that you can stay focused on doing what matters. Khoj started with the founding principle that a personal assistant be understandable, accessible and hackable. ... -
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AI Berkshire
AI-era Berkshire: a value investing research framework
...It uses parallel agent analysis, adversarial viewpoints, financial rigor checks, and repeatable report formats to reduce shallow or overly balanced conclusions. The framework covers company research, earnings review, industry screening, portfolio thinking, management analysis, and investment checklists. It is best understood as a research and decision-support system rather than a source of financial advice. -
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Data Science Interviews
Data science interview questions and answers
...Many of the questions cover fundamental machine learning topics such as linear models, decision trees, neural networks, and evaluation metrics. In addition to theoretical questions, the repository also includes practical interview topics related to coding challenges, SQL queries, and algorithmic thinking. -
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DoWhy
DoWhy is a Python library for causal inference
...DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks. Much like machine learning libraries have done for prediction, DoWhy is a Python library that aims to spark causal thinking and analysis. DoWhy provides a wide variety of algorithms for effect estimation, causal structure learning, diagnosis of causal structures, root cause analysis, interventions and counterfactuals. DoWhy builds on two of the most powerful frameworks for causal inference: graphical causal models and potential outcomes. For effect estimation, it uses graph-based criteria and do-calculus for modeling assumptions and identifying a non-parametric causal effect. ... -
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Yao Open Prompts
A Chinese AI prompt vocabulary covering work, learning, content, etc.
...The library includes prompts for meta-prompt generation, business productivity, learning methods, content operations, marketing, GEO strategy, web reverse engineering, product prototyping, and critical thinking. It is designed as a practical catalog that users can browse, copy, adapt, and test in their preferred AI model. The project also includes templates, references, maintenance checklists, scripts, and a complete catalog for easier navigation. -
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adversarial-spec
A Claude Code plugin that iteratively refines product specifications
adversarial-spec is a framework focused on designing and testing systems using adversarial thinking to uncover weaknesses and improve robustness. It encourages developers to define specifications that anticipate failure modes, edge cases, and malicious inputs before implementing solutions. The project emphasizes proactive design, ensuring that systems are built with resilience in mind from the beginning. It provides structured approaches for identifying vulnerabilities and stress-testing assumptions. ... -
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MiniCPM4.1
Achieving 3+ generation speedup on reasoning tasks
MiniCPM4.1 is an enhanced iteration of the MiniCPM4 architecture, introducing improvements in reasoning capabilities, inference speed, and hybrid operation modes that allow dynamic switching between deep reasoning and standard generation. It builds upon the same efficiency-focused philosophy but further optimizes decoding performance, achieving substantial speed gains in reasoning-intensive tasks while maintaining high-quality outputs. One of its key innovations is the hybrid reasoning mode,... -
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ReCall
Learning to Reason with Search for LLMs via Reinforcement Learning
ReCall is an open-source framework designed to train and evaluate language models that can reason through complex problems by interacting with external tools. The project builds on earlier work focused on teaching models how to search for information during reasoning tasks and extends that idea to a broader system where models can call a variety of external tools such as APIs, databases, or computation engines. Instead of relying purely on static knowledge stored inside the model, ReCall... -
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Ring
Ring is a reasoning MoE LLM provided and open-sourced by InclusionAI
Ring is a reasoning Mixture-of-Experts (MoE) large language model (LLM) developed by inclusionAI. It is built from or derived from Ling. Its design emphasizes reasoning, efficiency, and modular expert activation. In its “flash” variant (Ring-flash-2.0), it optimizes inference by activating only a subset of experts. It applies reinforcement learning/reasoning optimization techniques. Its architectures and training approaches are tuned to enable efficient and capable reasoning performance.... -
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wemake-python-styleguide
The strictest and most opinionated python linter ever!
...Fewer code revisions - strict linting ensures that you don't have to re-write the codebase again and again. Reduce code redundancy - Sometimes we write complex code as we are thinking in a certain way about a problem. The linter offers suggestions that can help simplify the code and eliminate redundant statements. The ultimate goal of this project is to make all people write exactly the same Python code.
