Search Results for "llama-cpp-static" - Page 2
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Showing 1586 open source projects for "llama-cpp-static"
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Chinese-LLaMA-Alpaca-3
Chinese Llama-3 LLMs) developed from Meta Llama 3
Chinese-LLaMA-Alpaca-3 is an open-source project that provides Mandarin-focused large language models based on Meta’s LLaMA-3 architecture, with both foundational and instruction-tuned variants to support high-quality Chinese natural language understanding and generation. It extends the original LLaMA models with expanded Chinese vocabularies and additional pretraining on Chinese corpora to improve semantic encoding and decoding specifically for Chinese text. ... -
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revive Static Code
6x faster, stricter, configurable, and extensible
...Revive provides a framework for the development of custom rules, and lets you define a strict preset for enhancing your development & code review processes. Fast & extensible static code analysis framework for Go. Allows us to enable or disable rules using a configuration file. Allows us to configure the linting rules with a TOML file. 2x faster running the same rules as golint. Provides functionality for disabling a specific rule or the entire linter for a file or a range of lines. golint allows this only for generated files. ... -
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Huatuo-Llama-Med-Chinese
Instruction-tuning LLM with Chinese Medical Knowledge
Huatuo-Llama-Med-Chinese is an open-source project that develops medical-domain large language models by instruction-tuning existing models using Chinese medical knowledge. The project builds specialized models by fine-tuning architectures such as LLaMA, Alpaca-Chinese, and Bloom with curated medical datasets. These datasets are constructed from medical knowledge graphs, academic literature, and question-answer pairs designed to teach models how to respond accurately to healthcare-related queries. ... -
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kotaemon
An open-source RAG-based tool for chatting with your documents
An open-source clean & customizable RAG UI for chatting with your documents. Built with both end users and developers in mind. This project serves as a functional RAG UI for both end users who want to do QA on their documents and developers who want to build their own RAG pipeline. -
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llama2.c
Inference Llama 2 in one file of pure C
llama2.c is a minimalist implementation of the Llama 2 language model architecture designed to run entirely in pure C. Created by Andrej Karpathy, this project offers an educational and lightweight framework for performing inference on small Llama 2 models without external dependencies. It provides a full training and inference pipeline: models can be trained in PyTorch and later executed using a concise 700-line C program (run.c). -
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restc-cpp C++ library
Modern C++ REST Client library
The magic that takes the pain out of accessing JSON API's from C++. It formulates a HTTP request to a REST API server. Then, it transforms the JSON formatted payload in the reply into a native C++ object (GET). It Serializes a native C++ object or a container of C++ objects into a JSON payload and sends it to the REST API server (POST, PUT). It formulates an HTTP request to the REST API without serializing any data in either direction (typically DELETE). It uploads a stream of data, like a... -
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Static Analysis Tools for PHP
Docker image that provides static analysis tools for PHP
Docker image providing static analysis tools for PHP. The list of available tools and the installer is actually managed in the jakzal/toolbox repository. Docker image with quality analysis tools for PHP. To run the selected tool inside the container, you'll need to mount the project directory on the container with -v "$(pwd):/project". Some tools like to write to the /tmp directory (like PHPStan, or Behat in some cases), therefore it's often useful to share it between docker runs, i.e. with -v "$(pwd)/tmp-phpqa:/tmp". ... -
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CodeLlama
Inference code for CodeLlama models
Code Llama is a family of Llama-based code models optimized for programming tasks such as code generation, completion, and repair, with variants specialized for base coding, Python, and instruction following. The repo documents the sizes and capabilities (e.g., 7B, 13B, 34B) and highlights features like infilling and large input context to support real IDE workflows. -
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Ollama
Run models like Kimi-K2.5, GLM-5, DeepSeek, gpt-oss, Gemma, Qwen etc.
Ollama is an open-source platform that enables developers to run large language models locally on their own machines. It simplifies working with modern AI models by providing a unified interface to download, manage, and interact with them. Users can run models like Llama, Gemma, Qwen, and others directly from the command line or through APIs. Ollama also integrates with popular developer tools and AI agents, allowing seamless workflows across coding environments and applications. It supports REST APIs, Python, and JavaScript SDKs, making it easy to build AI-powered features into software projects. ... -
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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. -
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LLM Foundry
LLM training code for MosaicML foundation models
Introducing MPT-7B, the first entry in our MosaicML Foundation Series. MPT-7B is a transformer trained from scratch on 1T tokens of text and code. It is open source, available for commercial use, and matches the quality of LLaMA-7B. MPT-7B was trained on the MosaicML platform in 9.5 days with zero human intervention at a cost of ~$200k. Large language models (LLMs) are changing the world, but for those outside well-resourced industry labs, it can be extremely difficult to train and deploy these models. This has led to a flurry of activity centered on open-source LLMs, such as the LLaMA series from Meta, the Pythia series from EleutherAI, the StableLM series from StabilityAI, and the OpenLLaMA model from Berkeley AI Research. -
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bert4torch
An elegent pytorch implement of transformers
An elegant PyTorch implement of transformers. -
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Skiplist-CPP
A tiny KV storage based on skiplist written in C++ language
Skiplist-CPP is a lightweight key-value storage engine implemented in C++ using a skip list as its core data structure. It showcases how a log-structured, ordered index can deliver fast inserts, lookups, and deletes while remaining simple to implement and reason about. The project supplies a compact codebase with a clear separation between the skip list implementation and the storage operations that use it. -
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TRIBE v2
A multimodal model for brain response prediction
...It is designed for in-silico neuroscience, enabling researchers to model how the brain responds to complex real-world inputs. The system integrates state-of-the-art encoders—including LLaMA for text, V-JEPA for video, and Wav2Vec-BERT for audio—into a unified Transformer architecture. This combined representation is mapped onto the cortical surface to predict fMRI responses across thousands of brain regions. TRIBE v2 allows researchers to simulate and analyze brain activity without requiring direct human experiments. ... -
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Jan.ai
Open source alternative to ChatGPT that runs 100% offline
...It allows you to download and run LLMs (local language models) offline while also offering optional integration with cloud-based model providers—giving you full control over your data and AI interactions. Download and run LLMs (Llama, Gemma, Qwen, GPT-oss etc.) from HuggingFace. Connect to GPT models via OpenAI, Claude models via Anthropic, Mistral, Groq, and others. Create specialized AI assistants for your tasks. MCP integration for agentic capabilities. -
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Next.js
The React Framework
Next.js is the React framework for lightweight apps, static websites, pre-rendered apps and more. It solves the most common problems associated with building a complete web application with React, such as those involving code bundling and transforming, production automizations, page rendering and having to write server-side code. Next.js offers a best in class “Developer Experience” through such capabilities as pre-rendering, single command static exporting, automatic code-splitting, hot code reloading and many other great features. ... -
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Modern C++ Programming
Modern C++ Programming Course
Modern-CPP-Programming is a teaching repository that introduces practical C++11/14/17 features through focused examples, exercises, and notes. It walks through core language topics like RAII, move semantics, templates and metaprogramming, lambdas, and smart pointers with an eye toward real-world patterns. Concurrency and performance enter the picture via threads, atomics, futures, and memory considerations, helping learners reason about correctness and speed. -
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Text Generation Web UI
Oobabooga - The definitive Web UI for local AI, with powerful features
...Very efficient text streaming. Parameter presets, 8-bit mode. Layers splitting across GPU(s), CPU, and disk. CPU mode, FlexGen, DeepSpeed ZeRO-3, API with streaming and without streaming. LLaMA model, including 4-bit GPTQ. RWKV model, LoRA (loading and training), Softprompts, and extensions. -
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llamafile
Distribute and run LLMs with a single file
...We're doing that by combining llama.cpp with Cosmopolitan Libc into one framework that collapses all the complexity of LLMs down to a single-file executable (called a "llamafile") that runs locally on most computers, with no installation. The easiest way to try it for yourself is to download our example llamafile for the LLaVA model (license: LLaMA 2, OpenAI). LLaVA is a new LLM that can do more than just chat; you can also upload images and ask it questions about them. With llamafile, this all happens locally; no data ever leaves your computer. -
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DeepSeek R1
Open-source, high-performance AI model with advanced reasoning
...This approach has resulted in performance comparable to leading models like OpenAI's o1, while maintaining cost-efficiency. To further support the research community, DeepSeek has released distilled versions of the model based on architectures such as LLaMA and Qwen. -
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fullmoon
Chat with private and local large language models
...Users can personalize the app by adjusting themes, fonts, and system prompts, and it integrates with Apple's Shortcuts for enhanced functionality. Fullmoon supports models like Llama-3.2-1B-Instruct-4bit and Llama-3.2-3B-Instruct-4bit, facilitating efficient on-device AI interactions without the need for an internet connection. -
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DocFX
Static site generator for .NET API documentation
DocFX can produce documentation from source code (including C#, F#, Visual Basic, REST, JavaScript, Java, Python and TypeScript) as well as raw Markdown files. DocFX can run on Linux, macOS, and Windows. The generated static website can be deployed to any host such as GitHub Pages or Azure Websites with no additional configuration. DocFX provides a flexible way to customize templates and themes. DocFX makes it extremely easy to generate your developer hub with a landing page, API reference, and conceptual documentation, from a variety of sources. DocFX builds a static HTML website from your source code and Markdown files, which can be easily hosted on any webserver (for example, github.io). ... -
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Orpheus TTS
Towards Human-Sounding Speech
Orpheus TTS is a state-of-the-art open-source text-to-speech system built on a Llama-3B backbone, treating speech synthesis as a large language model problem instead of a traditional TTS pipeline. It is designed to produce human-like speech with natural intonation, emotion, and rhythm, targeting quality comparable to or better than many closed-source systems. The project ships both pretrained and finetuned English models, as well as a family of multilingual models released as a research preview, and includes data-processing scripts so users can train or finetune their own variants. ... -
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SonarQube
Continuous inspection
SonarQube empowers all developers to write cleaner and safer code. Thousands of automated Static Code Analysis rules, protecting your app on multiple fronts, and guiding your team. Catch tricky bugs to prevent undefined behavior from impacting end-users. Fix vulnerabilities that compromise your app, and learn AppSec along the way with Security Hotspots. Make sure your codebase is clean and maintainable, to increase developer velocity!
