Search your computer by what's in your files: photos, PDFs, documents, recordings, videos and code. On-device on Mac, Windows and Linux, built on Google DeepMind's EmbeddingGemma 2.
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The official Ollama Docker image ollama/ollama is available on Docker Hub.
Local UI to run and train LLMs and diffusion models. 8, DeepSeek-V4, MiniMax-H3, Gemma 4, FLUX and more.
#702 · +52 · 1d ·GitHub
🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.
#1155 · +28 · 1d ·GitHub
🐧 PenguinHarness: Local-first multi-agent app development platform - create, fine-tune, deploy AI apps automatically!
《开源大模型食用指南》针对中国宝宝量身打造的基于Linux环境快速微调（全参数/Lora）、部署国内外开源大模型（LLM）/多模态大模型（MLLM）教程
Run MoE models beyond VRAM on AMD, NVIDIA & Intel GPUs. Vulkan-powered, portable Windows app with automatic setup. | 让游戏显卡跑起超显存大模型，支持 AMD、NVIDIA、Intel，免安装、自动配置。只需13MB，即下即用！
DFlash is a lightweight block diffusion model designed for speculative decoding. It enables efficient and high-quality parallel drafting.
What it is, and what it isn't. These are very small models. They make extremely fast, well-calibrated judgement calls between options, and they slot easily into your local code.
This is wire-compatible, but not mathematically equivalent to OpenJev's logit read. The probabilities are generated/self-reported by the model rather than read directly from its logits.
#6250 · +1 · 1d ·GitHub
3 GB model into memory. 35 GB core and FP16 KV cache in memory, then streams only the experts needed for each token from SSD. This is what lets the model run on Macs with 8 GB of RAM.
An educational project made to understand how LLM inference works. The full inference path is implemented in one file without external libraries.
#7101 · +0 · 1d ·GitHub
GUI for ChatGPT API and many LLMs. Supports agents, file-based QA, GPT finetuning and query with web search. All with a neat UI.
Personal AI Notebooks. Organize files & webpages and generate notes from them. Open source, local & open data, open model choice (incl.
PyTorch implementations of modern open-source LLM architectures (Llama, Qwen, DeepSeek, Gemma, GPT-OSS, Kimi, and more) — written from scratch for readability and learning, based on Sebastian Raschka's LLM Architecture Gallery.
Given a context and a list of pre-written options, the model prefills the context once, expands that KV cache across the option batch, and scores every option in a single padded forward pass. No decoding.