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LLM Kit

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Basic Information

LLM-Kit is a bundled WebUI integration package that brings together a full toolchain for working with language models. The project aims to let users run, customize and deploy LLMs and related applications without writing code. It consolidates model inference and training support, LLM API integrations, embedding pipelines, local knowledge bases, dataset preparation and application demos into a single distribution. The README emphasizes cross-platform use and provides deployment steps, dependency installation and example scripts to start a web demo. The repository targets developers, researchers and hobbyists who want an out-of-the-box environment for experimenting with mainstream open models, commercial LLM APIs, fine-tuning techniques and end-user demos such as roleplay, voice and avatar interaction.

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App URL
https://github.com/wpydcr/LLM-Kit
Github Repository
https://github.com/wpydcr/LLM-Kit/blob/main/README.md

App Details

Features
The repository organizes functionality into modular components including agent-related code, model and embedding support, application demos, UI code and utility scripts. It supports major LLM APIs and many open models for training and inference, quantization (4bit/8bit) and memory-saving techniques, LoRA and full-parameter fine-tuning, embedding model training and FAISS-based local retrieval. Tools include dataset creation and format conversion utilities, parallel and streaming LLM API calls, integration with MySQL and vector DBs, and demos for roleplay, memory, time-aware personas, image generation and voice (TTS, vits, svc) plus live2D support. The repo provides environment packaging, example scripts for Windows and Linux, pretrained model folders and demo data organization to accelerate experimentation.
Use Cases
LLM-Kit reduces setup friction by packaging environment, dependencies and example workflows so users can quickly run LLMs and related applications with a WebUI. It helps teams test different model backends, compare API and local model workflows, produce embeddings and build searchable knowledge bases, and perform fine-tuning with LoRA or full-parameter approaches. Built-in demos show integration patterns for roleplay, memory, voice synthesis, live2D avatars and database-backed knowledge retrieval, which are useful for prototyping conversational agents and multimodal experiences. Cross-platform testing notes and provided scripts streamline deployment on Windows and Linux. The AGPL-3.0 license and offered commercial licensing options clarify reuse and commercialization for organizations.

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