AI Native Data App Development framework with AWEL(Agentic Workflow Expression Language) and Agents

Run any open-source LLMs, such as Llama 2, Mistral, as OpenAI compatible API endpoint in the cloud.

Chinese-Vicuna: A Chinese Instruction-following LLaMA-based Model —— 一个中文低资源的llama+lora方案，结构参考alpaca

InternGPT (iGPT) is an open source demo platform where you can easily showcase your AI models. Now it supports DragGAN, ChatGPT, ImageBind, multimodal chat like GPT-4, SAM, interactive image editing, etc. Try it at igpt.opengvlab.com (支持DragGAN、ChatGPT、ImageBind、SAM的在线Demo系统)

A colab gradio web UI for running Large Language Models

ChatGPT爆火，开启了通往AGI的关键一步，本项目旨在汇总那些ChatGPT的开源平替们，包括文本大模型、多模态大模型等，为大家提供一些便利

llama.go is like llama.cpp in pure Golang!

Safe RLHF: Constrained Value Alignment via Safe Reinforcement Learning from Human Feedback

"Video-ChatGPT" is a video conversation model capable of generating meaningful conversation about videos. It combines the capabilities of LLMs with a pretrained visual encoder adapted for spatiotemporal video representation. We also introduce a rigorous 'Quantitative Evaluation Benchmarking' for video-based conversational models.

[NeurIPS 2023] LLM-Pruner: On the Structural Pruning of Large Language Models. Support LLaMA, Llama-2, BLOOM, Vicuna, Baichuan, etc.

🗣️ Chat with LLM like Vicuna totally in your browser with WebGPU, safely, privately, and with no server. Powered by web llm.

Large Language Models for All, 🦙 Cult and More, Stay in touch !

UI for 🦙model . Run AI assistant locally ✨

🦖 X—LLM: Cutting Edge & Easy LLM Finetuning

AgentLLM is a PoC for browser-native autonomous agents

Open source, local, and self-hosted highly optimized language inference server supporting ASR/STT, TTS, and LLM across WebRTC, REST, and WS

An opensource ChatBot built with ExpertPrompting which achieves 96% of ChatGPT's capability.

The "vicuna-installation-guide" provides step-by-step instructions for installing and configuring Vicuna 13 and 7B

Open-Source Implementation of WizardLM to turn documents into Q:A pairs for LLM fine-tuning

[ICLR'24] Mitigating Hallucination in Large Multi-Modal Models via Robust Instruction Tuning