Chat with AI large language models running natively in your browser. Enjoy private, server-free, seamless AI conversations.

智元 Fast API 是一站式API管理系统，将各类LLM API进行统一格式、统一规范、统一管理，使其在功能、性能和用户体验上达到极致。

智元 Fast API 是一站式API管理系统，将各类LLM API进行统一格式、统一规范、统一管理，使其在功能、性能和用户体验上达到极致。

智元 Fast API 是一站式API管理系统，将各类LLM API进行统一格式、统一规范、统一管理，使其在功能、性能和用户体验上达到极致。

文心一言/讯飞星火/通义千问/腾讯混元/DeepSeek/智谱AI Python API

ms-swift: Use PEFT or Full-parameter to finetune 250+ LLMs or 35+ MLLMs. (Qwen2, GLM4, Internlm2, Yi, Llama3, Llava, Deepseek, Baichuan2...)

An efficient, flexible and full-featured toolkit for fine-tuning LLM (InternLM2, Llama3, Phi3, Qwen, Mistral, ...)

Langchain-Chatchat（原Langchain-ChatGLM）基于 Langchain 与 ChatGLM 等语言模型的本地知识库问答 | Langchain-Chatchat (formerly langchain-ChatGLM), local knowledge based LLM (like ChatGLM) QA app with langchain

《开源大模型食用指南》基于Linux环境快速部署开源大模型，更适合中国宝宝的部署教程

ModelScope-Agent: An agent framework connecting models in ModelScope with the world

Replace OpenAI GPT with another LLM in your app by changing a single line of code. Xinference gives you the freedom to use any LLM you need. With Xinference, you're empowered to run inference with any open-source language models, speech recognition models, and multimodal models, whether in the cloud, on-premises, or even on your laptop.

Run generative AI models in sophgo BM1684X

dpo算法实现

Unify Efficient Fine-Tuning of 100+ LLMs

🐋MindChat（漫谈）——心理大模型：漫谈人生路, 笑对风霜途

Openai style api for open large language models, using LLMs just as chatgpt! Support for LLaMA, LLaMA-2, BLOOM, Falcon, Baichuan, Qwen, Xverse, SqlCoder, CodeLLaMA, ChatGLM, ChatGLM2, ChatGLM3 etc. 开源大模型的统一后端接口

Personal Project: MPP-Qwen14B(Multimodal Pipeline Parallel-Qwen14B). Don't let the poverty limit your imagination! Train your own 14B LLaVA-like MLLM on RTX3090/4090 24GB.

智元 Fast API 是一站式API管理系统，将各类LLM API进行统一格式、统一规范、统一管理，使其在功能、性能和用户体验上达到极致。

Open-source evaluation toolkit of large vision-language models (LVLMs), support GPT-4v, Gemini, QwenVLPlus, 50+ HF models, 20+ benchmarks

《Pytorch实用教程》（第二版）无论是零基础入门，还是CV、NLP、LLM项目应用，或是进阶工程化部署落地，在这里都有。相信在本书的帮助下，读者将能够轻松掌握 PyTorch 的使用，成为一名优秀的深度学习工程师。