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Open models & inferencekeras-team

keras

A high-level deep learning library that runs on top of JAX, TensorFlow or PyTorch: write a model once and pick the backend that suits the job.

Who it is for

For people who want to build and train a model without low-level plumbing.

How to start

  1. Install: pip install keras --upgrade.
  2. Install one backend: tensorflow, jax or torch.
  3. Open the guides at keras.io and adapt an example to your task.

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

Stars over the last 30 days

+77Sep 6 — Oct 5
64,27764,354

Author's description

Deep Learning for humans

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.

182Kstars+2.6K in 30 dGo

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++

deepseek-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.

105Kstars+327 in 30 dPython

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