Showing 2140 open source projects for "context"

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  • 1
    JSP tag libraries (taglibs) used to create and maintain application context. Used specifically to allow an application to manage its own properties and logs.
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  • 2
    VisLua is a full featured IDE for the Lua scripting language written in Lua. VisLua supports full context debugging, syntax highlighing, and many other advanced IDE features.
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  • 3
    When translating becomes a game ! Text to translate can be graphically selected. Several dictionnaries can be sorted according to the context. A large choice of matching strategies is available. The OCR engine is tunable.
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  • 4
    ...It runs on win 95,98,2000 and NT without changes. The current relese is geared for Novell networks in that it expects the novell client to be available and will return the user context when aske
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  • 5
    Compiles a file of reusable, context-overridable html/perl chunks into straight perl CGI. Project file can survive being edited in a wysiwyg html editor (eg Frontpage or Dreamweaver) for artistic polish. Tracks session data. MySQL integration optional.
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  • 6
    context_menu

    context_menu

    A Python library to create and deploy cross-platform native context

    A Python library to create and deploy cross-platform native context. context_menu was created as due to the lack of an intuitive and easy to use cross-platform context menu library. The library allows you to create your own context menu entries and control their behavior seamlessly in native Python code. It's fully documented and used by over 80,000 developers worldwide.
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  • 7
    Nemotron 3

    Nemotron 3

    Large language model developed and released by NVIDIA

    ...The base Nano architecture uses a hybrid Mamba-Transformer Mixture-of-Experts (MoE) design, allowing the model to activate only a small fraction of its 31.6 billion parameters per token, which improves speed and efficiency without sacrificing quality on complex queries. This configuration supports a massive context length of up to 1 million tokens, making it suitable for long-context reasoning, agentic tasks, extended dialogues, and applications like code generation or document summarization.
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  • 8
    Nemotron 3 Super

    Nemotron 3 Super

    Open language model developed by NVIDIA as part of Nemotron-3 family

    ...The model contains approximately 120 billion parameters, but employs a Mixture-of-Experts architecture that activates only a smaller subset of parameters during inference, improving computational efficiency while maintaining high capability. Its architecture combines Transformer attention layers with Mamba state-space components to balance long-context reasoning, memory efficiency, and high-quality language generation. The model is optimized for building AI agents that must perform complex tasks such as planning, tool usage, coding assistance, and multi-step reasoning.
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  • 9
    Kimi K3

    Kimi K3

    Powerful, native multimodal AI agentic model

    Kimi K3 is an open-weight, multim is an open-weight, multimodal agentic AI model from Moonshot AI designed for advanced coding, research, reasoning, and knowledge work. Builtodal agentic AI model from Moonshot AI designed for advanced coding, research, reasoning, and knowledge work. Built with 2.8 trillion total parameters and a sparse mixture-of-experts architecture, it activates 104 billion parameters per token to with 2.8 trillion total parameters and a sparse mixture-of-experts...
    Downloads: 1 This Week
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    Demo Series - Small Business Backup By Veeam

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  • 10
    Mistral Small 4

    Mistral Small 4

    Model that fuses instruct, reasoning and agentic skills

    The Mistral Small 4 collection is a set of open-weight large language models developed by Mistral AI that aim to unify multiple capabilities, including instruction following, reasoning, and coding, within a single efficient architecture. These models are part of the broader Mistral Small family, which is designed to deliver strong performance across a wide range of everyday AI tasks while maintaining relatively low latency and efficient deployment requirements. The collection reflects an...
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  • 11
    Nemotron 3 Nano

    Nemotron 3 Nano

    LL model providing reasoning and conversational capabilities

    ...This architecture allows the system to maintain strong reasoning capabilities while improving throughput and reducing the computational cost associated with large context processing. The model is designed as a general-purpose language system capable of handling tasks such as chat interaction, coding assistance, document analysis, and instruction following.
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  • 12
    The aim of the project is to create a device delivery context api for web servers. Many existing devices don't have a CC/PP or UAProf profile.The project aims to extend Sun's JSR 188 reference implementation by using WURFL device repository.
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  • 13
    Qwen3.6-35B-A3B

    Qwen3.6-35B-A3B

    Open multimodal model for coding, agents, and long-context tasks

    ...A notable addition is thinking preservation, which allows the model to retain reasoning context from earlier messages, improving iterative work and reducing redundant computation. Architecturally, it uses a Mixture-of-Experts design with 35B total parameters and 3B active, supports a native 262K-token context window, and can be extended to about 1M tokens with YaRN. It also performs strongly across coding, agent, vision, reasoning, and document-understanding benchmarks.
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  • 14
    Qwen3.6-27B

    Qwen3.6-27B

    Dense multimodal Qwen model for coding, agents, and long context

    ...It also introduces thinking preservation, allowing it to retain reasoning traces from earlier turns to improve consistency, reduce repeated computation, and support iterative agent workflows. Qwen3.6-27B natively supports a 262K-token context window and can be extended to about 1M tokens with YaRN for ultra-long tasks. It is compatible with Transformers, vLLM, SGLang, and KTransformers, supports tool calling through Qwen-Agent.
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  • 15
    Solar Open 2

    Solar Open 2

    Efficient 250B MoE model for agents, coding, and long-context work

    ...Its Hybrid-Attention Mixture-of-Experts architecture contains 250B total parameters while activating only 15B per token, combining three linear-attention layers with one softmax-attention layer for efficient inference. The model supports a native 1M-token context window and uses NoPE instead of rotary positional encoding, reducing long-context KV-cache requirements. Solar Open 2 includes 321 experts, with eight routed experts plus one shared expert activated per token. It was pretrained on roughly 12 trillion tokens and supports English, Korean, and Japanese. Agent capabilities include multi-step reasoning, tool calling, MCP tools, and end-to-end task execution. ...
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  • 16
    Qwen3.8-2.4T-A95B

    Qwen3.8-2.4T-A95B

    Massive 2.4T MoE model for coding, agents, research, and reasoning

    ...The model emphasizes reliable autonomous execution, including stronger planning, environment feedback handling, and end-to-end completion of complex multi-step workflows. It natively supports a 262K-token context window that can be extended beyond one million tokens. Qwen3.8 also provides adjustable reasoning depth through low, medium, and xhigh reasoning-effort settings and preserves reasoning context across conversations. It is a text-only, thinking-first model and supports deployment through vLLM, SGLang, and TokenSpeed, with strong results across coding, tool use, research, and professional benchmarks.
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  • 17
    MiMo-V2.5-Pro

    MiMo-V2.5-Pro

    Flagship MoE model for long-context agents and complex coding

    ...It features approximately 1.02 trillion total parameters with 42B activated per inference, balancing extreme capability with efficient execution. The model supports a 1 million token context window, enabling it to maintain coherence across long workflows involving thousands of tool calls and multi-step reasoning chains. Architecturally, it uses a hybrid attention system combining Sliding Window Attention and Global Attention to significantly reduce memory usage while preserving long-context performance. It also integrates multi-token prediction modules that accelerate inference and improve reinforcement learning efficiency. ...
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  • 18
    MiMo-V2.5

    MiMo-V2.5

    Omnimodal AI model for agents, coding, and long-context tasks

    MiMo-V2.5 is a native omnimodal large language model developed by Xiaomi, designed for advanced agentic workflows, multimodal reasoning, and long-context processing. Built on a Mixture-of-Experts architecture with approximately 309B total parameters and around 15B activated per inference, it balances high capability with efficient execution. The model natively processes text, images, video, and audio within a unified system, enabling cross-modal understanding and complex task execution in a single pipeline. ...
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  • 19
    DeepSeek-V4-Flash

    DeepSeek-V4-Flash

    Efficient MoE model for million-token reasoning and coding

    DeepSeek-V4-Flash is a preview Mixture-of-Experts language model built for efficient million-token context intelligence. It has 284B total parameters with 13B activated and supports a 1M-token context window, making it suitable for long-document reasoning, complex coding, agentic workflows, and large-scale information processing. The model uses a hybrid attention architecture that combines Compressed Sparse Attention and Heavily Compressed Attention to improve long-context efficiency, while Manifold-Constrained Hyper-Connections strengthen signal stability across layers. ...
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  • 20
    Qwen3-Next

    Qwen3-Next

    Qwen3-Next: 80B instruct LLM with ultra-long context up to 1M tokens

    Qwen3-Next-80B-A3B-Instruct is the flagship release in the Qwen3-Next series, designed as a next-generation foundation model for ultra-long context and efficient reasoning. With 80B total parameters and 3B activated at a time, it leverages hybrid attention (Gated DeltaNet + Gated Attention) and a high-sparsity Mixture-of-Experts architecture to achieve exceptional efficiency. The model natively supports a context length of 262K tokens and can be extended up to 1 million tokens using RoPE scaling (YaRN), making it highly capable for processing large documents and extended conversations. ...
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  • 21
    Hunyuan-A13B-Instruct

    Hunyuan-A13B-Instruct

    Efficient 13B MoE language model with long context and reasoning modes

    ...Open-source under a custom license, it's ideal for researchers and developers seeking scalable, high-context AI capabilities with optimized inference.
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  • 22
    Postproxy-MCP

    Postproxy-MCP

    MCP (Model Context Protocol) server for integrating PostProxy API

    PostProxy MCP is a Model Context Protocol (MCP) server that integrates the PostProxy API directly into Claude Code, enabling AI-assisted publishing to social media platforms like Instagram, YouTube, TikTok, Facebook, LinkedIn, X/Twitter, and Threads.
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  • 23
    Hy3 preview

    Hy3 preview

    Efficient MoE model for reasoning, coding, and AI agent workflows

    Hy3 preview is Tencent Hunyuan’s latest open-weight Mixture-of-Experts language model, designed for advanced reasoning, coding, instruction following, and autonomous agent workflows. It is the first model built on Tencent’s rebuilt training infrastructure and introduces significant improvements in context learning, software engineering, and tool-based task execution. The model features 295B total parameters with only 21B activated during inference, plus a dedicated 3.8B Multi-Token Prediction (MTP) layer that accelerates generation through speculative decoding. Architecturally, it uses 192 routed experts with top-8 activation, a dense-MoE hybrid design, and a native 256K-token context window. ...
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  • 24
    Qwen2-7B-Instruct

    Qwen2-7B-Instruct

    Instruction-tuned 7B language model for chat and complex tasks

    ...Built on a transformer architecture with SwiGLU activation and group query attention, it is optimized for chat, reasoning, coding, multilingual tasks, and extended context understanding up to 131,072 tokens. The model was pretrained on a large-scale dataset and aligned via supervised fine-tuning and direct preference optimization. It shows strong performance across benchmarks such as MMLU, MT-Bench, GSM8K, and Humaneval, often surpassing similarly sized open-source models. Designed for conversational use, it integrates with Hugging Face Transformers and supports long-context applications via YARN and vLLM for efficient deployment.
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  • 25
    QwQ-32B

    QwQ-32B

    QwQ-32B is a reasoning-focused language model for complex tasks

    ...For long input handling, it supports YaRN (Yet another RoPE Namer) for context scaling.
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