LLM inference in C/C++

Replace OpenAI GPT with another LLM in your app by changing a single line of code. Xinference gives you the freedom to use any LLM you need. With Xinference, you're empowered to run inference with any open-source language models, speech recognition models, and multimodal models, whether in the cloud, on-premises, or even on your laptop.

Run GGUF models easily with a KoboldAI UI. One File. Zero Install.

[Unmaintained, see README] An ecosystem of Rust libraries for working with large language models

Stable Diffusion and Flux in pure C/C++

llama and other large language models on iOS and MacOS offline using GGML library.

INT4/INT5/INT8 and FP16 inference on CPU for RWKV language model

Calculate token/s & GPU memory requirement for any LLM. Supports llama.cpp/ggml/bnb/QLoRA quantization

Go manage your Ollama models

Suno AI's Bark model in C/C++ for fast text-to-speech generation

Self-evaluating interview for AI coders

Run inference on MPT-30B using CPU

Port of MiniGPT4 in C++ (4bit, 5bit, 6bit, 8bit, 16bit CPU inference with GGML)

CLIP inference in plain C/C++ with no extra dependencies

Large Language Models for All, 🦙 Cult and More, Stay in touch !

Inference Vision Transformer (ViT) in plain C/C++ with ggml

Running any GGUF SLMs/LLMs locally, on-device in Android

This custom_node for ComfyUI adds one-click "Virtual VRAM" for any GGUF UNet and CLIP loader, managing the offload of layers to DRAM or VRAM to maximize the latent space of your card. Also includes nodes for directly loading entire components (UNet, CLIP, VAE) onto the device you choose. Includes 16 examples covering common use cases.

llama.cpp (GGUF LLMs) and llava.cpp (GGUF VLMs) for ROS 2

Chat with your data privately using MPT-30b