The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.
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Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow
- gbdt
- gbrt
- gbm
- distributed-systems
Distribute and run LLMs with a single file.
- cross-platform
- gguf
- llama-cpp
- local-ai
ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
The repository provides code for running inference with the Meta Segment Anything Model 2 (SAM 2), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.
Open-source State-of-the-art TTS model which runs on a CPU 😻
State-of-the-art Machine Learning for the web. Run 🤗 Transformers directly in your browser, with no need for a server!
- browser
- javascript
- transformers
- webml
Speech-to-text, text-to-speech, speaker diarization, speech enhancement, source separation, and VAD using next-gen Kaldi with onnxruntime without Internet connection. Support embedded systems, Android, iOS, HarmonyOS, Raspberry Pi, RISC-V, RK NPU, Axera NPU, Ascend NPU, x86_64 servers, websocket server/client, support 12 programming languages
Suite of tools for deploying and training deep learning models using the JVM. Highlights include model import for keras, tensorflow, and onnx/pytorch, a modular and tiny c++ library for running math code and a java based math library on top of the core c++ library. Also includes samediff: a pytorch/tensorflow like library for running deep learn...
- java
- gpu
- deeplearning
- neural-nets
The repository provides code for running inference and finetuning with the Meta Segment Anything Model 3 (SAM 3), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.
Visualize, query, and stream to train on multimodal robotics data.
Production ready toolkit to run AI locally
Deeplake is AI Data Runtime for Agents. It provides serverless postgres with a multimodal datalake, enabling scalable retrieval and training.
Run LLMs with MLX
- llms
- mlx
A studio for image and video generation — one prompt bar, each model’s own settings, and every finished run in one gallery.
Run Keras models in the browser, with GPU support using WebGL
Open-source library of optimized deep learning operations (matmul, convolution, attention) for CPUs (x64, AArch64, RISC-V) and Intel GPUs. Used by PyTorch, TensorFlow, OpenVINO, and ONNX Runtime.
- onednn
- oneapi
- performance
- cpp
Bringing stable diffusion models to web browsers. Everything runs inside the browser with no server support.
- webgpu
- deep-learning
- stable-diffusion
- web-assembly
Code for running RFdiffusion
Make videos on the computer you already own. FreeVideo runs MiniMax H3 in as little as 8 GB of VRAM and 16 GB of RAM, and adapts its acceleration path to your hardware.
Project Maya - run GLM-5.3-Flash (321B MoE), DeepSeek-V4.1-Flash-1017 (748B MoE) and MiniMax-M3.1-Flash (428B MoE) privately on your own GPU(s), from one card to 16, NVIDIA or AMD, Linux or Windows. Engine, server, dashboard, OpenAI- and Anthropic-compatible API, quant tools.
NobodyWho is an inference engine that lets you run LLMs locally and efficiently on any device.
1M param OCR model - runs on CPU, 15x faster than tesseract, close to 1B+ param models in accuracy
A uniform interface to run deep learning models from multiple frameworks