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Translated🎬 卡卡字幕助手 (Kaka Subtitle Assistant) | VideoCaptioner - An intelligent LLM-based subtitle assistant - handles the full workflow of video subtitle generation, sentence segmentation, correction, and subtitle translation! - A powered tool for easy and efficient video subtitling.
Nano vLLM
Automated Penetration Testing Agentic Framework Powered by Large Language Models
Supercharge Your LLM Application Evaluations 🚀
Python SDK for Agent AI Observability, Monitoring and Evaluation Framework. Includes features like agent, llm and tools tracing, debugging multi-agentic system, self-hosted dashboard and advanced analytics with timeline and execution graph view
Android in docker solution with noVNC supported, video recording, mcp server and AI-agent
Use PEFT or Full-parameter to CPT/SFT/DPO/GRPO 600+ LLMs (Qwen3.8, DeepSeek-V4, GLM-5.1, InternLM3, Llama4, ...) and 300+ MLLMs (Qwen3-VL, Qwen3-Omni, InternVL3.5, Ovis2.5, GLM5.3, Gemma4, Llava, Phi4, ...) (AAAI 2025).
Structured Outputs
TranslatedChatGLM2-6B: An Open Bilingual Chat LLM
GUI for ChatGPT API and many LLMs. Supports agents, file-based QA, GPT finetuning and query with web search. All with a neat UI.
Research and development (R&D) is crucial for the enhancement of industrial productivity, especially in the AI era, where the core aspects of R&D are mainly focused on data and models. We are committed to automating these high-value generic R&D processes through R&D-Agent, which lets AI drive data-driven AI. 🔗https://aka.ms/RD-Agent-Tech-Report
TensorRT LLM provides users with an easy-to-use Python API to define Large Language Models (LLMs) and supports state-of-the-art optimizations to perform inference efficiently on NVIDIA GPUs. TensorRT LLM also contains components to create Python and C++ runtimes that orchestrate the inference execution in a performant way.
Unified framework for building enterprise RAG pipelines with small, specialized models
Fully autonomous & self-evolving research from idea to paper. Chat an Idea. Get a Paper. 🦞
RWKV (pronounced RwaKuv) is an RNN with great LLM performance, which can also be directly trained like a GPT transformer (parallelizable). We are at RWKV-7 "Goose". So it's combining the best of RNN and transformer - great performance, linear time, constant space (no kv-cache), fast training, infinite ctx_len, and free sentence embedding.
TranslatedLlama Chinese community: compiles the latest Llama learning materials in real time and builds the best open-source ecosystem of Chinese Llama large models, fully open source and commercially usable
Open-source, secure environment with real-world tools for enterprise-grade agents.
Talk to any LLM with hands-free voice interaction, voice interruption, and Live2D avatar running locally across platforms
Self-evolving agent: grows skill tree from 3.3K-line seed, achieving full system control with 6x less token consumption
structured outputs for llms
One portable memory layer for every AI agent: local-first, Markdown-native, user-owned, and self-evolving across apps, tools, and workflows.
[MLsys2026 Best Paper]: https://arxiv.org/abs/2506.08276. RAG on Everything with LEANN. Enjoy 97% storage savings while running a fast, accurate, and 100% private RAG application on your personal device.
20+ high-performance LLMs with recipes to pretrain, finetune and deploy at scale.
TranslatedChatGLM3 series: Open Bilingual Chat LLMs