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OVOS voice satellite for HiveMind — full on-device voice stack (microphone, VAD, wake word, STT, TTS)

A command-line interface wrapper for Faster Whisper

This repository is now archived. Further development has been moved to https://github.com/medkit-lib/medkit.

Keras(Tensorflow) implementations of Automatic Speech Recognition

Automatic video translator and dubber using Whisper, XTTS v2 for voice cloning, and Ollama for local LLM translation. Supports 100+ languages.

Speech-to-text for Linux that just works

whisper.cpp Windows binary w/ Vulkan GPU support

大模型推理框架加速，让 LLM 飞起来

Run the LLM Swarm Router on machines to distribute Local Ai to the Swarm - More Machines - MORE SPEED

Local-first desktop assistant for the whole job hunt — scrape listings, match with a local LLM, generate applications in your own voice, and track everything on a Kanban board.

Run and manage both vLLM and llama.cpp model servers from one terminal UI + CLI — shared Docker Compose profiles, live tok/s, and cross-backend port/GPU conflict checks. NVIDIA + Linux.

Open-source, self-hosted AI workspace for local and open-weight LLMs with vLLM, LiteLLM, autonomous agents, MCP tools, deep research, artifacts, and BYOK.

Pretrained VGG-16 network as feature extractor for Object Recognition (Python, Keras, Scikit-Learn)

[ACL 2023] The code for our ACL'23 paper Cold-Start Data Selection for Few-shot Language Model Fine-tuning: A Prompt-Based Uncertainty Propagation Approach

A distributed training framework for large language models powered by Lightning.

[AAAI'25] The implementation of paper "Federated Foundation Models on Heterogeneous Time Series" | The first work to explore time series foundation models on federated setting.

[ACL2023] Preserving Commonsense Knowledge from Pre-trained Language Models via Causal Inference

DistRL: An Asynchronous Distributed Reinforcement Learning Framework for On-Device Control Agents

MinimalGPT is a concise, adaptable, and streamlined code framework that encompasses the essential components necessary for the construction, training, inference, and fine-tuning of the GPT model. This framework is implemented exclusively using Keras and TensorFlow, ensuring compatibility and coherence within the broader deep learning ecosystem.

A structured knowledge base for large language model technologies, covering LLMs, Agents, RAG, model training, evaluation methodologies, and AI engineering practices.