Model Codebases & Model Families

Compact open model family with practical code, deployment notes, and active edge/on-device focus.

Attention-free language model architecture with linear-time inference, training code, inference examples, and an active open-source ecosystem.

OpenAI open-weight model repository with inference examples, recipes, and deployment guidance.

State Space Model implementation with pretrained checkpoints, architecture code, and research tooling for efficient long-sequence modeling.

Large-scale language model training codebase from EleutherAI with distributed training support and historical open-model importance.

Open-source mixture-of-experts language model family optimized for long-horizon planning, agentic tasks, and coding. Apache 2.0 licensed.

Local runtime and model package for the Bonsai ternary reasoning model family, with GGUF and MLX support for on-device text, vision, and tool-calling workloads. Apache-2.0 licensed.

Open implementation of CLIP with training code, pretrained models, and zero-shot evaluation tooling.

Vision-based GUI parsing model and tooling for computer-use agents.

Compact vision-language model family with edge-focused deployment examples and strong OCR-oriented use cases.

NVIDIA multimodal model codebase with open checkpoints and reusable research materials for vision-language and video-language tasks.

Open platform of world models, tokenizers, and post-training tools designed for physical AI, robotics, and autonomous systems.

Canonical open speech-to-text model codebase with widespread ecosystem support and many downstream implementations.

Speech recognition toolkit with pretrained models, streaming support, diarization, VAD, and production-oriented examples.

Scalable framework and model codebase for speech, language, and multimodal AI with recipes and deployment guidance.

Complete speech toolkit with ASR, TTS, diarization, source separation, and VAD across embedded and edge environments via ONNX Runtime.

Open speech and sound generation family focused on expressive, long-form text-to-speech with streaming and multi-speaker support.

Open-sourced tokenizer-free multilingual speech synthesis model with high-quality TTS and style transfer workflows.

Open Frontier Voice AI toolkit spanning speech understanding, generation, and multilingual TTS workflows, with active research and deployment tooling.

PyTorch speech toolkit with recipes for ASR, TTS, speaker recognition, and speech enhancement.

Lightweight text-to-speech engine optimized for CPU inference with low latency and streaming support. MIT licensed.

C/C++ speech-to-text inference library running 16+ model families on the ggml runtime with GPU acceleration.

Open-source on-device voice AI toolkit for low-latency speech-to-text, intent recognition, and text-to-speech.