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Open models & inferencedeepseek-ai

DeepSeek-V3

Repository of the large open DeepSeek-V3 mixture-of-experts model: description, benchmark results and run instructions. It showed an open model can stand next to closed ones.

Who it is for

For people with serious hardware, or anyone studying how a modern MoE model is built.

How to start

  1. Read the Model Summary and Evaluation Results sections.
  2. Download the weights from Model Downloads (published in FP8).
  3. Serve it with a recommended engine from How to Run Locally: SGLang, vLLM, LMDeploy or TensorRT-LLM.

Steps are taken from the README. Check the current version in the repository before running them.

Stars over the last 30 days

+327Sep 6 — Oct 6
104,186104,513

ollama

ollama

The easiest way to run an open model on your own machine: one command downloads and starts it, plus a local REST API and libraries for Python and JavaScript.

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huggingface

transformers

The core library for working with models: one codebase for text, vision, audio and multimodal tasks, for both inference and training. Most new open models ship through it.

167Kstars+2.4K in 30 dPython

open-webui

open-webui

A self-hosted ChatGPT-style interface that connects to Ollama and any OpenAI-compatible API. Installs with a single Docker command and runs on your own server.

154Kstars+3.3K in 30 dPython

ggml-org

llama.cpp

A C/C++ engine that runs language models on ordinary hardware: laptops, phones, GPU-less servers. Much of local AI, Ollama included, is built on it.

130Kstars+3.6K in 30 dC++

pytorch

pytorch

The foundation under most modern neural networks: GPU-accelerated tensors and automatic differentiation, wrapped in ordinary Python. Transformers, Llama and nearly every open model are written on it.

104Kstars+1.1K in 30 dPython

vllm-project

vllm

An engine for fast model serving on GPUs: frugal with memory and handles many requests at once. The default pick when a model has to run as a service.

93Kstars+2.4K in 30 dPython