Showing 11 open source projects for "artificial intelligence projects in java"

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  • 1
    LLamaSharp

    LLamaSharp

    C#/.NET binding of llama.cpp, including LLaMa/GPT model inference

    The C#/.NET binding of llama.cpp. It provides APIs to infer the LLaMa Models and deploy it on the local environment. It works on both Windows, Linux and MAC without the requirement for compiling llama.cpp yourself. Its performance is close to llama.cpp. Furthermore, it provides integrations with other projects such as BotSharp to provide higher-level applications and UI.
    Downloads: 1 This Week
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  • 2
    OpenAI API client for Kotlin

    OpenAI API client for Kotlin

    OpenAI API client for Kotlin with multiplatform capabilities

    OpenAI API client for Kotlin with multiplatform and coroutines capabilities.
    Downloads: 0 This Week
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  • 3
    CodeGeeX

    CodeGeeX

    CodeGeeX: An Open Multilingual Code Generation Model (KDD 2023)

    CodeGeeX is a large-scale multilingual code generation model with 13 billion parameters, trained on 850B tokens across more than 20 programming languages. Developed with MindSpore and later made PyTorch-compatible, it is capable of multilingual code generation, cross-lingual code translation, code completion, summarization, and explanation. It has been benchmarked on HumanEval-X, a multilingual program synthesis benchmark introduced alongside the model, and achieves state-of-the-art...
    Downloads: 4 This Week
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  • 4
    Scikit-LLM

    Scikit-LLM

    Seamlessly integrate LLMs into scikit-learn

    Seamlessly integrate powerful language models like ChatGPT into sci-kit-learn for enhanced text analysis tasks. At the moment the majority of the Scikit-LLM estimators are only compatible with some of the OpenAI models. Hence, a user-provided OpenAI API key is required. Additionally, Scikit-LLM will ensure that the obtained response contains a valid label. If this is not the case, a label will be selected randomly (label probabilities are proportional to label occurrences in the training...
    Downloads: 0 This Week
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  • 5
    Prompt Engineering Techniques

    Prompt Engineering Techniques

    Collection of tutorials for Prompt Engineering techniques

    Prompt Engineering Techniques is a focused companion repository that teaches prompt engineering systematically, from fundamentals to advanced strategies. It contains around twenty-plus hands-on Jupyter notebooks, each dedicated to a specific technique such as basic prompt structures, prompt templates and variables, zero-shot prompting, few-shot prompting, chain-of-thought, self-consistency, constrained generation, role prompting, task decomposition, and more. The tutorials are designed to be...
    Downloads: 7 This Week
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  • 6
    Opik

    Opik

    Open-source end-to-end LLM Development Platform

    Confidently evaluate, test, and monitor LLM applications. Opik is an open-source platform for evaluating, testing, and monitoring LLM applications. Built by Comet. Record, sort, search, and understand each step your LLM app takes to generate a response. Manually annotate, view, and compare LLM responses in a user-friendly table. Log traces during development and in production. Run experiments with different prompts and evaluate against a test set. Choose and run pre-configured evaluation...
    Downloads: 2 This Week
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  • 7
    llmx.txt hub

    llmx.txt hub

    The largest directory for AI-ready documentation and tools

    llms-txt-hub serves as a central directory and knowledge base for the emerging llms.txt convention, a simple, text-based way for project owners to communicate preferences to AI tools. It catalogs implementations across projects and platforms, helping maintain a shared understanding of how LLM-powered services should interact with code and documentation. The repository aims to standardize patterns for allowlists, denylists, attribution, rate expectations, and contact information, mirroring...
    Downloads: 0 This Week
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  • 8
    LLM Course

    LLM Course

    Course to get into Large Language Models (LLMs)

    LLM Course is a hands-on, notebook-driven path for learning how large language models work in practice, from data curation to training, fine-tuning, evaluating, and deploying. It emphasizes reproducible experiments: each step is demonstrated with runnable code, clear dependencies, and references to commonly used open-source models and libraries. Learners get exposure to multiple adaptation strategies—LoRA/QLoRA, instruction fine-tuning, and alignment techniques—so they can choose approaches...
    Downloads: 1 This Week
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  • 9
    OSS-Fuzz Gen

    OSS-Fuzz Gen

    LLM powered fuzzing via OSS-Fuzz

    OSS-Fuzz-Gen is a companion project that helps automatically create or improve fuzz targets for open-source codebases, aiming to increase coverage in OSS-Fuzz with minimal maintainer effort. It analyses a library’s APIs, examples, and tests to propose harnesses that exercise parsers, decoders, or protocol handlers—precisely the code where fuzzing pays off. The system integrates with modern LLM-assisted workflows to draft harness code and then iterates based on build errors or low coverage...
    Downloads: 0 This Week
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  • 10
    Super Easy AI Installer Tool

    Super Easy AI Installer Tool

    Application that simplifies the installation of AI-related projects

    "Super Easy AI Installer Tool" is a user-friendly application that simplifies the installation process of AI-related repositories for users. The tool is designed to provide an easy-to-use solution for accessing and installing AI repositories with minimal technical hassle to none the tool will automatically handle the installation process, making it easier for users to access and use AI tools. "Super Easy AI Installer Tool" is currently in early development phase and may have a few bugs. But...
    Downloads: 1 This Week
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  • 11
    Qwen2.5-Coder

    Qwen2.5-Coder

    Qwen2.5-Coder is the code version of Qwen2.5, the large language model

    Qwen2.5-Coder, developed by QwenLM, is an advanced open-source code generation model designed for developers seeking powerful and diverse coding capabilities. It includes multiple model sizes—ranging from 0.5B to 32B parameters—providing solutions for a wide array of coding needs. The model supports over 92 programming languages and offers exceptional performance in generating code, debugging, and mathematical problem-solving. Qwen2.5-Coder, with its long context length of 128K tokens, is...
    Downloads: 3 This Week
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