Showing 1836 open source projects for "query"

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  • 1
    CheeSQL is a MySQL Front-End with autocomplete features. The GUI is a web page (html+javascript) that requests, using ajax, query results, updates and insertions to a local java desktop application, that handles the DB communication.
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  • 2
    ListQuery is a simple tool to read SQL instructions and to list the results in text format (console). You can read an archive (file) with lots of commands (query), separate for ';', the program will list all results one by one.
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  • 3
    The aim of the SQP project is to create client and server stubs for the SQP(Simple Query Protocol) protocol. Compliance and regression testing utilities are also within scope. It is a spin-off from the JSDC(Javascript Data Components) project.
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  • 4
    Teardrop provides a way to query multiple search engines at the same time, and explore their results as a single source. It's available both in a command line and a graphical version. Search engines can be added at will.
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  • 5
    Monet Objects is an easy-to-use .NET object-relational mapping library. It allows .NET developers to create applications dealing with persistent objects (relying on a relational database as MySql) without writing a single SQL query.
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  • 6
    bge-small-en-v1.5

    bge-small-en-v1.5

    Compact English sentence embedding model for semantic search tasks

    ...Designed for dense retrieval, semantic search, and similarity tasks, it produces 384-dimensional embeddings that can be used to compare and rank sentences or passages. This version (v1.5) improves similarity distribution, enhancing performance without the need for special query instructions. The model is optimized for speed and efficiency, making it suitable for resource-constrained environments. It is compatible with popular libraries such as FlagEmbedding, Sentence-Transformers, and Hugging Face Transformers. The model achieves competitive results on the MTEB benchmark, especially in retrieval and classification tasks. ...
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  • 7
    Qwen2-7B-Instruct

    Qwen2-7B-Instruct

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

    Qwen2-7B-Instruct is a 7.62-billion-parameter instruction-tuned language model from the Qwen2 series developed by Alibaba's Qwen team. 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. ...
    Downloads: 0 This Week
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  • 8
    Llama-3.2-1B

    Llama-3.2-1B

    Llama 3.2–1B: Multilingual, instruction-tuned model for mobile AI

    ...Llama 3.2-1B outperforms other open models in several benchmarks relative to its size and offers quantized versions for efficiency. It uses a refined transformer architecture with Grouped-Query Attention (GQA) and supports long context windows of up to 128k tokens.
    Downloads: 0 This Week
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  • 9
    QueryLite - a query analyzer for, and in the spirit of the Sqlite database, written in Java.
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  • 10
    This is a new generation of SQL (Structured query language) Server which is targeted to serve on a Virtual Appliance. Hybrid SQL Server is targeted to run on VirtualBox from SunMicrosystems.
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  • 11
    DeepSeek-V3.1-Terminus

    DeepSeek-V3.1-Terminus

    685B model with improved agents and consistency

    ...It improves language consistency, reducing mixed Chinese-English outputs and eliminating abnormal characters, enhancing reliability in multilingual scenarios. The update also refines agentic capabilities, especially for the Code Agent and Search Agent, leading to better tool integration and query handling. Benchmarks show small but notable gains, such as raising MMLU-Pro from 84.8 to 85.0, GPQA-Diamond from 80.1 to 80.7, and SWE Verified from 66.0 to 68.4, along with significant improvements in agent benchmarks like BrowseComp (30.0 → 38.5) and Terminal-bench (31.3 → 36.7). The model structure remains the same as DeepSeek-V3, ensuring compatibility with existing deployment methods, with updated inference demos provided for community use.
    Downloads: 0 This Week
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