Tools tagged with "Hugging Face integration"

LiteRT is Google's open-source framework for developers building AI into apps that run on users' own devices. It succeeds TensorFlow Lite and covers model conversion, optimization and local inference. It's licensed under Apache 2.0.

HQQ is a Python library that compresses language and vision models without needing a calibration dataset. It's for developers preparing models to run on their own hardware or servers, particularly when GPU memory limits the model they can use. The library is open source under Apache 2.0.

Nanotron is a Python library for researchers and developers who want to pretrain language models on their own datasets and GPU infrastructure. Built on PyTorch, it supports NVIDIA CUDA GPUs and training across multiple servers with Slurm. It's open source under Apache 2.0.

OpenCompass is an open-source LLM evaluation platform for researchers, model developers and teams comparing models for their applications. Its Python framework evaluates local models and cloud APIs within the same experiment, so teams can compare candidates on shared benchmarks. It uses the Apache 2.0 license.

Model2Vec turns sentence transformers into small static embedding models that run locally on CPU. It's for developers who need text embeddings for retrieval, code search or classification without the size and inference cost of the original transformer. The Python package is open source under the MIT license.

EchoMimic turns a portrait image and an audio recording into an animated talking-head video on your own hardware. Its distinguishing feature is editable facial landmark control: you can drive animation with audio, landmarks, or audio combined with selected landmarks. It's intended for academic research and suits people comparing methods for speech animation and facial motion control.

GPTQModel is a Python toolkit for developers compressing LLMs and running them on their own hardware or servers. It brings model calibration, compression, quality checks and inference into one API, so teams can compare quantization methods without adopting a separate tool for each one.

SenseVoice is a local speech recognition model that adds language, emotion and sound-event tags to transcriptions. It's for developers building voice applications or analyzing recordings on their own hardware, particularly those working with Mandarin and Cantonese. The project is open source under the MIT license.

Hugging Face Datasets is an open source Python library for preparing data for AI training and evaluation on your own machine. It's for developers and researchers working with local files or datasets from the Hugging Face Hub. The library runs locally; downloading, streaming or sharing data through the Hub uses Hugging Face's hosted service.

OpenLLM is a self-hosted LLM server for developers who want to connect their applications to models running on their own hardware or servers. Its OpenAI-compatible API works with clients built for that interface, including the OpenAI Python client and LlamaIndex. The project is open source under the Apache License 2.0.

Optimum is a collection of Python packages for developers who want to train or run Hugging Face models more efficiently on specific hardware. It extends Transformers, Diffusers, TIMM and Sentence Transformers, with integrations for local machines, mobile and edge devices, and cloud accelerators. It's open source under Apache 2.0.

RVC WebUI is a local AI voice conversion tool for people who want to train a custom voice, change the voice in a recording, or use a live voice changer. It runs on Windows and Linux, including Ubuntu servers, with a browser interface for training and conversion and a separate interface for live use. It's free and open source under the MIT license.

AllTalk TTS generates speech on your own computer. The project recommends v2 for most users; the saved documentation below describes v1, built on Coqui TTS and XTTSv2 models. It's for people adding voices to AI conversations or producing spoken audio from longer texts. It runs as a standalone application or alongside Text-generation-webui, with support for Windows, Linux and macOS.

Ray Serve is a self-hosted Python library for developers building inference APIs that combine models with application logic. It runs on a laptop, on-premise servers, Kubernetes, or cloud infrastructure you choose. It's open source under Apache 2.0.

Wyoming Piper connects Piper's local text-to-speech engine to Home Assistant and other clients that use the Wyoming protocol. It's for people building a voice assistant on their own hardware who need speech generation as a self-hosted service. The project is open source under the MIT license.

NeMo Curator is an open source Python toolkit for ML engineers and data teams preparing AI training datasets on their own hardware. It handles text, images, video and audio, with reusable pipelines that can run on a laptop or scale across a multi-node Ray cluster. NVIDIA uses it to prepare data for Nemotron models.

Home-LLM connects Home Assistant to language models running on your own hardware, so you can control smart devices through voice or chat. It's for Home Assistant users who want natural language control without relying on a cloud service or subscription. The project pairs a custom integration with small models trained specifically for smart home commands.

PowerInfer is a local LLM inference engine for developers and researchers who want to run large models on a PC with a consumer GPU. It splits work between the CPU and GPU to reduce GPU memory demands and data transfers. The code is open source under the MIT license.

GPT-SoVITS is a local text-to-speech and voice cloning tool. It can generate speech from a short reference recording or fine-tune a model for a custom voice. The source code uses the MIT license.

GOT-OCR2.0 is an OCR model for developers and researchers who want to extract text from images on their own hardware. It handles both plain text and formatted output through a single model, with recognition modes for selected regions and documents spanning multiple pages. The Python codebase builds on Vary.

CAMEL is an open-source Python framework for developers and researchers building systems where AI agents work together. Its focus is on agent roles, communication, and behavior across extended tasks, with applications in synthetic training data, task automation, and simulated societies. It uses the Apache 2.0 license.

Petals lets developers and researchers use large language models that won't fit on a single consumer GPU by sharing the work across a network of machines. It supports text generation and fine-tuning from a desktop computer or Google Colab. Each participant holds part of the model, while other computers handle the remaining parts.

Parakeet is NVIDIA's speech recognition model family. The linked parakeet-tdt-0.6b-v2 is its English speech-to-text model for developers and researchers building transcription services, subtitles or voice applications. It runs locally through NeMo on Linux, with NVIDIA GPUs recommended for inference. It's a model you can embed in an application, rather than a desktop transcription app.

DeepSeek-OCR is an open-source OCR model for developers building document processing tools and researchers studying how AI reads text through images. It runs on your own hardware with NVIDIA CUDA GPUs. Its distinctive focus is visual text compression: representing document images with compact sets of vision tokens for a language model to read.

rerankers is a Python library for developers building search and retrieval systems who want to compare reranking models without rewriting their integration each time. It takes a query and candidate documents, then ranks their relevance through a shared interface across local models and hosted services. It's open source under Apache 2.0.

Distil-Whisper is a family of local speech recognition models for developers building English transcription into their apps or services. It reduces Whisper's size and processing time while retaining much of its transcription accuracy. It supports English only.

PDF-Extract-Kit is a local AI model toolbox for developers and researchers building document processing applications. It extracts text, tables and mathematical formulas from PDFs, with separate models for identifying page elements and recognizing their contents. It's open source under AGPL-3.0, written in Python, and supports CPU or GPU execution on your own hardware.

Cactus is an on-device AI engine for developers building automation into mobile apps, wearables and embedded devices. Its Needle model handles tool calling locally, so a device can turn a request into an action without an internet connection. The focus is small devices, including smart home hardware, robots and microcontrollers.

MonkeyOCR is a local AI document parser for developers and researchers working with English and Chinese PDFs or images. It extracts text, formulas and tables while identifying page structure and relationships between blocks. That makes it useful for documents where plain text extraction loses reading order or separates content from its layout.

Moshi is a voice AI model and dialogue framework that can listen while it speaks. It processes speech directly, retaining information such as emotion and non-verbal cues that a text transcription can miss. It's aimed at researchers and developers building spoken AI applications, with local inference and self-hosted server options.

torchtitan is an open-source training platform for researchers and developers building generative AI models on their own GPU machines or server clusters. It uses PyTorch's distributed training tools and keeps the model code relatively simple when spreading work across GPUs. The Python codebase has extension points and replaceable components for experiments with model architectures and training infrastructure.

OpenSuperWhisper is a local speech-to-text app for people who want to dictate or transcribe recordings on an Apple Silicon Mac. It supports Whisper and Parakeet, with model downloads available inside the app. The project is open source under the MIT license.

TabbyAPI is a self-hosted LLM API server built around ExLlamaV3, for people who want local model inference behind an OpenAI-compatible API. It's the official server for that backend. The project targets personal use and small groups, and its maintainers explicitly advise against using it for production workloads.

Whisper WebUI turns audio into transcripts and subtitles through a browser interface that runs on your own machine or a self-hosted server. It's for people captioning videos, transcribing recordings or translating spoken content who want local speech processing. The project is open source under Apache 2.0 and supports Docker and Pinokio.

Distributed Llama runs a local LLM across several computers, sharing both the computation and the model's memory use. It's for people who want to use their own networked hardware for inference rather than keep the entire workload on one machine. The C++ project is open source under the MIT license.

localGPT is a self-hosted AI document chat app for people who want to question and summarise files on their own hardware. Its local Ollama setup keeps documents and conversations on your machine. Answers include source passages, so you can check what the model used.